DSP Projects

PROJECT TOPICS-COMMUNCATION,IMAGE PROCESSING

2011-07-14T05:13:00.000-07:00

COMMUNICATIONS TOPICS

· Year 2007

An OFDM-CDMA scheme for High Data Rate UWB applications
Time-Domain Signal Detection Based on Second-Order Statistics for MIMO-OFDM Systems.

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· Year 2008

A Token-Based Scheduling Scheme for WLANs Supporting Voice/Data Traffic and its Performance Analysis
Adaptive Radio Resource Allocation for Downlink OFDMA/SDMA Systems with Multimedia Traffic
Coding Schemes Applied to Peak-to-Average Power Ratio (PAPR) Reduction in OFDM Systems
Cooperative Sensing for Primary Detection in Cognitive Radio
Design and Analysis of Bit Interleaved Coded Space-Time Modulation
Enhancing MB-OFDM Throughput with Dual Circular 32-QAM
Fast and Efficient QoS-Guaranteed Adaptive Transmission Algorithm in the Mobile WiMAX System
Filter Bank Spectrum Sensing for Cognitive Radios
Joint Optimum Linear Precoding and Power Control Strategies for Downlink MC-CDMA Systems
Efficient Spatial Covariance Estimation for Asynchronous Co-channel Interference Suppression in MIMO-OFDM Systems
Power Allocation for Two Different Traffics in Layered MIMO Systems
Performance Evaluation of the WiMedia PHY in WPAN Environments and Efficiency Improvement by Application of LDPC Codes

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· Year 2009

Combined Wavelet-Domain and Motion-Compensated Video Denoising Based on Video Codec Motion Estimation Methods
Ranging With Ultrawide Bandwidth Signals in Multipath Environments
Semisoft Handover Gain Analysis Over OFDM-Based Broadband Systems
Ultra-Wide-Band Propagation Channels
Pulsed-OFDM Modulation for Ultrawideband Communications
Grouping Technique for Cooperative Spectrum Sensing in Cognitive Radios
Implementation of the least squares channel estimation algorithm for MIMO- OFDM systems
Sequential Detection for Multiuser MIMO CDMA Systems with Single Spreading Code Per User
A Multicode Approach for High Data Rate UWB System Design
Replacement of Spectrum Sensing in Cognitive Radio
Variance-Reduced Partial Parallel Interference Cancellation for MC-CDMA Uplink Systems
A Closed-Form Blind CFO Estimator Based on Frequency Analysis for OFDM Systems
Eigenvalue based Spectrum Sensing Algorithms for Cognitive Radio
A New Parameter for UWB Indoor Channel Profile Identification
Cognitive Radio Sensing Architecture and A Sensor Selection Case Study
Cooperative Spectrum Sensing with Cluster-Based Architecture in Cognitive Radio Networks
Efficient Power Allocation for Coded OFDM Systems
Measurement Based Channel-Adaptive Video Streaming for Mobile Devices over Mobile WiMAX

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· Year 2010

Bandwidth Exchange: An Energy Conserving Incentive Mechanism for Cooperation
Blind Matched Filtering for Multiple Input Multiple Output Transceivers
Estimation of Cubic Nonlinear Bandpass Channels in Orthogonal Frequency-Division Multiplexing Systems
Error control coding in wireless sensor networks
Error-Resilient H.264/AVC Video Transmission Using Two-Way Decodable Variable Length Data Block
Simplified Multiaccess Interference Reduction for MC-CDMA With Carrier Frequency Offsets
Optical Communications Performance of Hybrid 34-Meter Microwave Antennas
Performance Analysis of Distributed Decision Fusion Using A Censoring Scheme in Wireless Sensor Networks

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Year 2011

Exploiting Sparse User Activity in Multi-user Detection

IMAGE PROCESSING

· Year 2007

Color Image Segmentation Based on Mean Shift and Normalized Cuts
Hierarchical contour matching for dental X-ray radiographs
An Improving Model Watermarking with Iris Biometric Code

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· Year 2008

Active Learning Methods for Interactive Image Retrieval
Detecting Dominant Motions in Dense Crowds
Personal Authentication Based on Iris Texture Analysis
Robust Image Segmentation Algorithm Using Fuzzy Clustering Based on Kernel-Induced Distance Measure
Wearable Monitoring of Seated Spinal Posture
Design of a Distributed Traffic Monitoring System and Algorithm based on Webcamera

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Year 2009

A Fast Image Compression Algorithm Based on SPIHT
Application of Support Vector Machine and Genetic Algorithm for Improved Blood Cell Recognition
Reconstruction Of Underwater Image By BISPECTRUM
Combined Wavelet-Domain and Motion-Compensated Video Denoising Based on Video Codec Motion Estimation Methods
Artificial Neural Network Based Automatic Face Parts Prediction System from Only Fingerprints
A Histogram Modification Framework and Its Application for Image Contrast Enhancement
New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss–Hermite Expansion
Fast Query Point Movement Techniques for Large CBIR Systems
Codebook Optimization in Vector Quantization using Genetic Algorithm

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Year 2010

Face Verification across Age Progression using Discriminative Methods
Frequency Compounding for Ultrasound Freehand Elastography
Satellite Image Resolution Enhancement Using Complex Wavelet Transform
Video sensor network for real-time traffic monitoring and surveillance
Error-Resilient H.264/AVC Video Transmission Using Two-Way Decodable Variable Length Data Block
Designing of High-Speed Image Cryptosystem Using VQ Generated Codebook and Index Table

Year 2011

A Point Feature-based Cylindrical Image Mosaic Method
Image Mosaics Algorithm Based on Feature-Block Matching

SIGNAL PROCESSING

Improving the intelligibility of dysarthric speech-2007
Content-BasedMusic Information Retrieval: Current Directions and Future Challenges-2008
Speech Enhancement Using Harmonic Emphasis and Adaptive Comb Filtering-2010

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NETWORKING

Adaptive Routing in Dynamic Ad Hoc Networks-2008
Cooperative MIMO-Beamforming For Multiuser Relay Networks-2008
A Medium Access Control Scheme for TDD-CDMA Cellular Networks with Two-Hop Relay Architecture-2009

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The above listed topics is just for reference. If you have any new Ideas/Papers send to us at info@verilogcourseteam.com or Call +91 9894220795.

VARIANCE-REDUCED PARTIAL PARALLEL INTERFERENCE CANCELLATION FOR MC-CDMA UPLINK SYSTEMS

2009-11-14T23:20:00.000-08:00

MULTICARRIER code division multiple access (MCCDMA) is one of the promising techniques for fourth generation wireless mobile communication systems. MCCDMA has several advantages, such as efficient spectrum utilization and immunity to multipath impairment. One major drawback associated with MC-CDMA is that multiuser detection is often necessary to overcome the multiple access interference (MAI) problem in uplink systems. Many multiuser detection approaches have been proposed in the literature .Among them, multistage parallel interference cancellation (PIC) has received great attention because of its low complexity and low processing latency, but it fails to guarantee performance improvement with more interference cancellation (IC) stages in moderate to high system load environments. To alleviate this problem, Divsalar et al. proposed the partial PIC (PPIC) detector, which first estimates MAI and then partially cancels it from the received signal on a stage-by-stage basis. Moshavi also indicated in that linearly combining soft tentative decisions of each IC stage can reduce the variance of the signal estimate, which will generate more reliable MAI estimates. As revealed in, a bit estimator that linearly combines the soft decisions from previous stages at a given stage for interference cancellation is inherently used by the PPIC approach. Therefore, the PPIC approach outperforms the PIC approach. Because the PPIC detector introduces weighting factors (WFs) to each IC stage, the performance depends highly on the choice of them. For good performance, two optimal WF (OWF) selection schemes based on analyzing the bit-error-rate (BER) were proposed for CDMA systems. However, the complexity of BER analysis increases greatly with the number of IC stages, and these approaches generally are not suitable for applications with more than two IC stages.

In this project, an improved version of PPIC, called variance-reduced PPIC (VRPPIC),for C-CDMA uplink systems. First,the main PPIC operations, where (the number of active users) multiplications of the original PPIC scheme are replaced with additions for each IC stage.Then we derive VRPPIC based on this simplified PPIC, where a new bit estimator is used for linearly combining soft tentative decisions from PPIC to reduce the conditional variance of the final signal estimate. In addition, an OWF selection algorithm for VRPPIC based on minimizing a conditional variance function of the final signal estimate. With the monotonically increasing property of the conditional variance function, it is shown that the OWFs for all IC stages are equal and can be approximately expressed as a linear function of the number of active users. Computer simulation results show that the proposed VRPPIC using the derived OWFs significantly improves the system performance.

VIDEO DEMO

SIMULATION OF HARDWARE BASED EDGE DETECTION

2009-11-13T15:40:00.000-08:00

INTRODUCTION:

Edge detection is a fundamental tool used in most image processing applications to obtain information from the frames before feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. Beyond that, Edge Detection refers to the process of identifying and locating sharp discontinuities in intensities in an image. The discontinuities are abrupt changes in pixels intensity which characterize boundaries of objects in a scene structure. This process significantly reduces the amount of date in the image, while preserving the most important structural feature of that image. Edge detection is considered to be the ideal algorithm for images that are corrupted with white noise. The Edge is characterized by its height, slope angle,and horizontal coordinate of the slope midpoint. An ideal Edge Detector should produce an edge indication localized to a single pixel located at the midpoint of the slope.There are many ways to perform Edge detection. However, the majority of different methods may be grouped into two categories, gradient and Laplacian. The basic Edge detection operator is a matrix area gradient operation that determines the level of variance between different pixels. The edge detection operator is calculated by forming a matrix centered on a pixel chosen as the centre of the matrix area. If the value of the matrix area is above a given threshold, then the middle pixel is classified as an edge. Examples of gradient based edge detectors are Roberts, Prewitt and Sobel operators. All the gradient –based algorithms have Kernel operators that calculate the strength of the slope in directions that are orthogonal to each other, generally horizontal and vertical.
The requirements that the algorithms must meet are:
a) Show the effectiveness and the noise resistance for remote sensing image.
b) Satisfying real time-constraints, and minimizing hardware resources in order to meet embedding requirements.
c) Significantly reducing the amount of date and filters out useless information.

Classically, Edge detection algorithms are implemented on software. With advances in the VLSI technology hardware implementation has become an attractive alternative. Assigning complex computation tasks to hardware and exploiting the parallelism and pipelining in algorithm yield significant speedup in running times. Implementation image processing on reconfigurable hardware minimizes the time-to-market cost, enables rapid prototyping of complex algorithm and simplifies debugging and verification.

VIDEO DEMO

AN IMPROVING MODEL WATERMARKING WITH IRIS BIOMETRIC CODE

2009-11-13T15:20:00.000-08:00

INTRODUCTION

We using one algorithm of the discrette wavelet transformation DWT, which sends itself in wise Haar for performing in water sign. The discrette wavelet transformation is relatively new notion, which wants to change the purpose (Or Filler) Broad used discrette transformation of the cosine (DCT) that uses itself in Compression JPEG. The Purpose a method on this in use biometric discriminating line is provided digital scene. The Copyright Protection- digital scene offers the method with use one Bio measured feature,and as follows: iris-play-off owner, who identifies his(its) synonymy Embedding watermark.The Input signal elements are a transport image, which must be protected for this method, watermark has took into consideration include. She standing with compute the rubbish for iris-play-off of the code of the pattern human. The Algorithm,which is elected to be an execution, which lifted contribution uses the obvious wave transformation so it is named transformation Haar, what include the water volumes of the mark in seconds level wavelets(2- two levels DWT), and two levels HL2 and two justify LH2 region. Since the message defines on these level information in less degree on image because of this also change (include the noise) on image and they will less. Here, it is necessary to addiction as is mentionned also in picture-machine and quality on water mark. This dependency solves as information, e.g. volume on water mark, can change without visibility of the garbling the rebel against transport image.The Used watermark in offered models - also digital image, which is elected to be on to grey scale of the format BMP.

The Number water sign must be according to with size transport image, this can be gained program. In undefended event this not carry in checkmore lung completion doubtful displacement to be capable observer changes quality introductions to other frame-transport and other water sign.For embedding the watermark is using setup the generator of whatever worth's uses itself, as he is installed with respect to chosen by us switch e. g. this hash worth had calculated for the concrete iris-code. The used hash function is MD 5 (Algorithm 5 Message Digest), from which we receive 128-BIT code. Each Hash value will be different on the different pictures to irises - but the goal is alone worth for digests is being got here unique worth for Iris code.

VIDEO DEMO

RECONSTRUCTION OF UNDERWATER IMAGE BY BISPECTRUM

2009-11-13T03:00:00.000-08:00

Reconstruction of an underwater object from a sequence of images distorted by moving water waves is a challenging task. A new approach is presented in this project. We make use of the bispectrum technique to analyze the raw image sequences and recover the phase information of the true object. We test our approach on both simulated and real-world data, separately. Results show that the algorithm is very promising. Such technique has wide applications to areas such as ocean study and submarine observation.

VIDEO DEMO

DESIGN AND ANALYSIS OF BIT INTERLEAVED CODED SPACE-TIME MODULATION

2009-11-13T01:22:00.000-08:00

INTRODUCTION

IT was recognized first by Zehavi in [1] that a bit–interleaved coding scheme provides a diversity order for transmission over flat Rayleigh fading channels equal to the smallest number of distinct bits along any error event (rather than channel symbols). This scheme was later referred to as bit-interleaved coded modulation (BICM) in, where non–iterative decoding was considered and Gray labeling was thereby preferred. For frequency–selective fading channels, BICM schemes could also provide a high diversity order together with orthogonal frequency division multiplexing (OFDM) technique, where again Gray labeling is chosen for a non–iterative decoding strategy. However, Zehavi pointed out in his work that the non–iterative decoding method for BICM was not optimal, because the interdependence between labeling bits addressing one modulation symbol can not be exploited, since no a-priori information on the labeling bits is available. Therefore, for high order modulation with inevitable inter-dependence between the labeling bits, an optimal decoder exploiting this inter-dependence has a complicated metric.

On the other hand, a question naturally arises whether Gray labeling is still preferred if an enhanced decoding method is applied to take the inter-dependence between the labeling bits into account. By addressing the former problem of investigating an optimal decoder, a possible way is to consider the encoder, bit-interleaving and the mapper jointly with maximum likelihood (ML) decoding by means of a super-trellis diagram, but the trellis complexity may be extremely high in most cases with long interleaver. As an alternative, a suboptimal, iterative decoding method, which has been originally proposed for Turbo codes could be adapted in principle to BICM with necessary modifications to take advantage of the inter-dependence between coded bits by updating a-priori information on labeling bits. This has been originally introduced by Li and Ritcey in with a harddecision feedback method and the effects of different labeling strategies were discussed with the result that set partitioning (SP) rather than Gray labeling performs best. Furthermore, iterative decoding (ID) employing soft decision feedback, was applied to BICM (i.e., BICM-ID), too, yielding the result that when the inter–dependence introduced by high order modulation is somewhat exploited, a properly designed labeling rule different from Gray labeling will bring significant benefits.

Later on, this iterative decoding with bit interleaver was further adapted to multiple input and multiple output (MIMO) systems in several different approaches, and we preferred to denote this by bit interleaved coded space–time modulation with iterative decoding (BICSTM-ID) .since it closely resembles BICM in many aspects. As a natural consequence, designing a well suited labeling rule for space–time modulation (STM) is certainly essential as well. Therefore, one main aim of this paper is to analyze the performance of a bit-interleaved concatenated coding scheme over a MIMO channel with different labeling strategies, each of which is a component mapping composed of the space– time block code (STBC) and the labeling rule for a substitute constellation. For designing the labeling rules, some analytical methods for BICM-ID can serve as a good starting point such as the harmonic mean of the minimum squared distance d2 h in and modified one d2 h in by assuming ideal feedback information. The advantage of this modified parameter in is that an analytical bit error ratio (BER) upper bound for the nonlinear scheme can be provided.

However, in the low signal to noise ratio (SNR) region, this upper bound is not reliable and even lower than the simulation results due to the fact that in this region only part of ideal a-priori information may be actually fed back by iterative decoding. Moreover, it suffersfrom its inability to predict the cliff region.

VIDEO DEMO

Optimized Software Implementation of a Full-Rate IEEE 802.11a Compliant Digital Baseband Transmitter on a Digital Signal Processor

2009-11-13T00:35:00.000-08:00

INTRODUCTION

The explosive growth of 802.11-based wireless LANs has attracted interest in providing higher data rates and greater system capacities. Among the IEEE 802.11 standards, the 802.11a standard based on OFDM modulation scheme has been defined to address high-speed and large-system-capacity challenges. Hardware implementations are often used to meet the high-data rate requirements of 802.11a standard. Although software based solutions are more attractive due to the lower cost, shorter development time, and higher flexibility, it is still a challenge to meet the high-data-rate requirements of 802.11a by software. In this project, we simulate (Modelsim/Matlab) a software-based 802.11a digital baseband transmitter using Verilog HDL /Matlab. The transmitter can operate over all data rates defined in the 802.11a standard and are compatible with the high-rate portions of the 802.11g standard. Two major optimizations have been used in the software implementation to achieve the high-data-rate:

1) parallelizing the scrambler function and

2) concatenating the FEC encoder, puncturing, and inter leaver functions.

Digital signal processors (DSPs) are a special class of processor optimized for signal-processing applications in communication systems. Although DSPs have been used to implement the 802.11a standard, they can only support limited data rates due to the lack of global parallelism found at the application level. Hence, it is still a major challenge to develop a software implementation for the 802.11a standard on a DSP to meet the high-data-date requirements.

802.11A DIGITAL BASEBAND TRANSMITTER

The OFDM modulation scheme used in 802.11a distributes the data over 52 subcarriers on a 20MHz channel to mitigate the effects of multipath. Among the 52 subcarriers, 48 are for data and 4 are for pilot signals used for tracking. Each subcarrier is 312.5kHz wide, giving raw data rates from 125kbits/s to 1.125Mbits/s per subcarrier depending on the modulation type – binary phase shift keying (BPSK), quaternary PSK (QPSK), 16-quadrature amplitude modulation (QAM), or 64-QAM – and the error-correcting code rate (1/2, 2/3, or 3/4). The composite signal therefore has a data rate ranging from 6Mbits/s to 54Mbits/s in the 20MHz channel.

VIDEO DEMO

COMBINED QRD-M AND DFE DETECTION TECHNIQUE FOR SIMPLE AND EFFICIENT SIGNAL DETECTION IN MIMO-OFDM SYSTEMS

2009-11-13T00:17:00.000-08:00

INTRODUCTION

THE orthogonal frequency division multiplexing (OFDM) is commonly used for high data rate wireless communication due to its inherent error susceptibility in a multipath environment. OFDM-based transmission systems can be extended to a multiple input multiple output (MIMO) architecture using vertical Bell laboratories layered spacetime (V-BLAST) concept, which provides significant capacity gain in wireless channels. In the MIMO-OFDM system, a MIMO signal detection with a low complexity and high performance is a knotty subject. Therefore, a lot of signal detection algorithms have been proposed . Among the detection schemes, a QRD-M scheme achieves very high detection performance. However, since the QRDM is a tree search algorithm, the detection complexity is highly increased, as either the QAM level or the number of transmission antennas increases.

In this project, we propose a simple and efficient detection technique based on QRD-M for MIMO-OFDM systems. In the proposed detection technique, we adopt a new parameter T. If the value of T is high, the system has good detection performance and high complexity. However, if the low value of T is selected, the detection performance is decreased and the complexity is reduced. Therefore, by adjusting the value of the new parameter T , the performance and computational complexity of the system can be controlled. After T -th stage, the decision feedback equalization (DFE) detection based on QR-decomposition is used. The reason why the DFE detection based on QR-decomposition is used is that by using QR-decomposition, both QRD-M and DFE can use the QR-decomposition without additional operation to detect the symbol and among some representative detection schemes, the DFE detection based on QR-decomposition has good performance and low complexity.

PREDICTION-BASED REVERSIBLE DATA HIDING

2009-11-13T00:07:00.000-08:00

Introduction

In recent years, a special kind of digital watermark, called reversible (which is also called lossless, invertible, erasable, distortion-free, etc.) data hiding technique, has drawn lots of interest. It not only declares the ownership of the digital media by embedding the digital watermark into the cover image but also can completely restore the original image after extraction. This kind of data hiding is suitable for some specific applications where images are sensitive to further processing, such as medical image, satellite image, and artwork.

In 2001, Honsinger proposed the first lossless data hiding concept providing lossless authentication. However, image quality made by the scheme was unsatisfactory. It suffered from salt-and-pepper visual artifacts. Then Vleeschouwer used patchwork algorithm and circular histogram to solve the noise problem. Although this scheme could survive high quality JPEG compression, its embedding capacity was very limited. Three years later, Ni proposed a reversible data hiding method based on histogram modification. The secret data was embedded by shifting the histogram. It demonstrated that the lower bound of the PSNR of a watermarked image is 48.13 dB. Additionally, lossless compression is also used for reversible data hiding. Fridrich compressed the least significant bit plane of selected pixels using lossless compression, and then combined the compressed result with the watermark to produce the bitstream. Then the bitstream was embedded into the cover work by using LSB substitution. Their algorithm achieved reversibility, but its payload was very limited. In 2002, Fridrich proposed another cheme to improve the embedded payload and visual quality. Each disjointed group of the cover ork was categorized into one of three sets: regular (R), singular (S), and unusable (U). The group types R and S were used to embed 1 and 0, respectively. During the embedding procedure, a lipping function was used to flip the embedded group into the specific set. Finally, the sets R and S were losslessly compressed and embedded into the cover work. In 2005, Celik proposed an improved scheme by using generalized-LSB embedding. The scheme first converts binary to L-ary, then lossless compression (CALIC) is used to compress the quantization residues as side information. This scheme efficiently uses CALIC to obtain high embedding capacity.

For some applications such as satellite and medical images, reversible data hiding is the best solution to provide copyright protection or authentication. Being reversible, the decoder can extract the hidden data and recover the original image without distortion. In this project, a reversible data hiding scheme based on prediction error expansion is proposed. The predictive value is computed by using various predictors. The secret data is embedded in the cover image by exploiting the expansion of the difference between a pixel and its predictive value. Experimental results show that our method is capable of providing a great embedding capacity without making noticeable distortion. In addition, the proposed scheme is also applicable to various predictors.

MEASUREMENT BASED CHANNEL-ADAPTIVE VIDEO STREAMING FOR MOBILE DEVICES OVER MOBILE WIMAX

2009-11-12T23:57:00.000-08:00

INTRODUCTION

Due to the explosive growth of the wireless multimedia communication services, there are increasing demands on real-time video streaming over the wireless systems. Recent advances in high-speed networks have made it feasible to provide real-time video streaming. Among the advanced wireless standards, WiMAX is an emerging wireless communication system that provides high-data rate as well as long-range coverage. The higher quality and seamless streaming in video transmission over the wireless network require to cope with the problems such as channel bandwidth variation, handoff, transmission error. Among those problems, the channel bandwidth variation and the handoff due to movement of the subscriber station (SS) are the most critical problems. The channel bandwidth variation causes the network congestion when the video transmission rate exceeds the channel bandwidth. In case of the mobile WiMAX, the adaptive modulation and coding (AMC) scheme from half-rate QPSK to 5/6 64-QAM offers various data rates to the SS according to the distance between the base station (BS) and the SS. In addition, the sudden disconnection due to handoff between BSs or sectors leads to errors in several frames because the error occurred in one frame would be propagated to the subsequent frames due to the prediction of the inter mode, which degrades the video quality significantly.To address these problems, several methods for wireless video streaming have been proposed . The method proposed are adjusted the transmission rate to the varying throughput of wireless 3G network. However, it requires the bandwidth estimation of wireless network and needs to consider the channel bandwidth in the mobile WiMAX to adapt the video transmission rate dynamically. In the periodical random intra refresh and motion information-based conditional intra refresh methods were proposed to reduce the error propagation in error-prone channel. However, it is not an efficient way to reduce the error propagation caused by handoff latency in wireless network

In this project, we propose a channel-adaptive video streaming method over mobile WiMAX, as shown in Fig. 1, which does not only dynamically adjust the video transmission rate based on the channel bandwidth, but also minimize the error propagation during handoff. Firstly, the current channel bandwidth is estimated by using channel parameters including the CINR. The estimated channel bandwidth is then exploited to determine the next video transmission rate, thereby avoiding the network congestion. Secondly, an efficient intra refresh method is proposed that inserts I-frame right after handoff by using the HOM to reduce the error propagation effectively.

MODEL REFERENCE ADAPTIVE SYSTEM (MRAS) BASED SENSORLESS CONTROL OF INDUCTION MOTOR

2009-11-12T23:49:00.000-08:00

Introduction:

There are many senseless methods. Which, class to two main kinds, openloop and close-loop. Up to this moment, there are many open-loop sensorless schemes have been presented but all of them have many defected by motor parameters and feed-back variable like stator current and voltage . Which, give many defected to control scheme, therefore they also make defect to operation process of induction motor. This kind of defected belong scheme by scheme of open-loop method. Shortly, this method has many defected by motor parameter and feed-back signals, stator resistance is the most importance defecting parameter. Special in low speed performance of motor and if rotor flux has been estimated by stator flux, it also get the defected by stator resistance. Opposite, close-loop method gives a better solution. It gets very low defected by motor parameter and feed-back variable because calculate and estimate process always have feed-back to adjust transition error. This method manage to estimate rotor speed by two models so call reference model and adaptive model, which work parallel to estimate rotor flux and the error of 2 those signals have been adjusted to zero through PI control unit to get the actual speed of rotor.

The operation of speed controlled ac drives without mechanical speed or position sensors equires the estimation of internal state variables of the machine. The assessment is based exclusively on the stability and capability to compensate machine parameters during operation of motor. This project gives one of the best method to design the drive without speed sensor, MRAS, which estimates speed and compensate machine parameters. Depending on requisition or working condition of the drives, each machine parameter has been monitored and compensated case by case. Stator resistance, Rs, has been compensated in this paper. Beside that, MRAS helps sensorless system work well incase the feedback variables have transition error.

MATLAB PROJECT TOPICS

2009-11-12T15:20:00.000-08:00

MATLAB PROJECT TOPICS

Robust DWT-SVD Domain Image Watermarking: Embedding Data in All Frequencies
Textural Features for Image Classification
Implementation of a Wimax Simulator in Simulink
MACAW: A Media Access Protocol for Wireless LAN’s
Doubly Fed Induction Generator Using Back-To-Back PWM Converters and Its Application to Variable Speed Wind-Energy Generation
Image Segmentation by Data Driven Markov Chain Monte Carlo
Robust Image Watermarking Based On Multiband Wavelets and Empirical Mode Decomposition
Grid Optical Burst Switched Networks (GOBS)
A Lossless Data Compression and Decompression Algorithm and Its Hardware Architecture
Colorization by Example
A Comparative Study Between Wavelet And Contourlet Transform Features For Textural Image Classification.
Wireless LAN Medium Access
Control (MAC) and Physical Layer
(PHY) Specifications
Achieving MAC Layer Fairness in Wireless Packet Network
Colorization Using Optimization
Hierarchical Contour Matching For Dental X-Ray Radiographs
A System for Human Identification from X-Ray Dental Radiographs
A Review of Routing Protocols for Mobile Ad Hoc Networks
Automatic Generation Control of Interconnected Power System Using Ann Technique Based On Μ–Synthesis
Discrete-Time Linear Parameter Varying Control Of Doubly-Fed Induction Generators
Blocking Probabilities of Optical Burst Switching Networks Based On Reduced Load Fixed Point Approximations
Automatic Recognition of Exudative Maculopathy Using Fuzzy Cmeans Clustering and Neural Networks
Computer-Aided Shape Analysis and Classification of Weld Defects in Industrial Radiography Based Invariant Attributes and Neural Networks
Optimized Software Implementation of A Full-Rate IEEE 802.11a Compliant Digital Baseband Transmitter on A Digital Signal Processor
Performance of Optical Burst Switched Networks for Grid Applications
A Hybrid Time Divisioning Scheme for Power Allocation In DMT-Based DSL Systems
DUCHA: A New Dual-Channel MAC Protocol for Multihop Ad Hoc Networks
Active Noise Cancellation with a Fuzzy Adaptive Filtered-X Algorithm
Adaptive Routing In Dynamic Ad Hoc Networks
An FPGA-Based Architecture for Real Time Image Feature Extraction
Signal Adaptive Subband Decomposition for Adaptive Noise Cancellation
Implementation of IEEE802.1x in MATLAB
Profiling Delay and Throughput Characteristics of Interactive Multimedia Traffic over Wlans Using MATLAB
An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks
CIC Filter Introduction
Call Admission Control Optimization in Wimax Networks
Contention-Based Qos MAC Mechanisms for VBR Voip in IEEE 802.11e Wireless Lans
An Area-Efficient Universal Cryptography Processor for Smart Cards.
Design and Analysis of Bit Interleaved Coded
Space-Time Modulation
A Real-Time Adaptive Learning Method for Driver Eye Detection
Real-Time System for Monitoring Driver Vigilance
Erlang Reduced Load Model For Optical Burst Switched Grids
The Contourlet Transform for Image De-Noising Using Cycle Spinning
Wavelet-Based Contourlet Transform and Its Application to Image Coding
Adaptive Nonlinear Congestion Controller for a Differentiated-Services Framework
Fast Handoff Support in IEEE 802.11 Wireless Networks
On The Design of A Multi-Mode Receive
Digital-Front-End for Cellular Terminal Rfics
Optical Burst Switching for Consumer Grids
Fuzzy Based PID Controller Using MATLAB for Transportation Application
An Improving Model Watermarking With Iris Biometric Code
Multiscale Feature Analysis Using Directional Filter Bank
Image & Sound Compression Using
Wavelet Transform
Modeling Sigma-Delta Modulator Non-Idealities In Simulink
Image Segmentation Using Iterative Watersheding Plus Ridge Detection
Signal Processing-Oriented Topics (Spots) For MIMO Wireless Communications
Mobility Management Techniques for the Next Generation Wireless Networks
VLSI Architecture and Chip for Combined Invisible Robust and Fragile Watermarking
Non-Symmetric Decompanding for Improved Performance of Companded OFDM Systems
Implementation of IEEE 802.11 A WLAN Baseband Processor
Content Based Image Retrieval Using Contourlet Transform
VLSI Architecture and FPGA Prototyping of a Digital Camera for Image Security and Authentication
A Performance Study of Mobile Handoff Delay in IEEE 802.11-Based Wireless Mesh Networks
Backup Path Set Selection in Ad Hoc Wireless Network Using Link Expiration Time
Impact of Heavy Traffic Beyond Communication Range in Multi-Hops Ad-Hoc Networks
Performance Improvement in Wireless Networks Using Cross-Layer ARQ
Analysis of IEEE 802.11e for Delay Sensitive Traffic in Wireless Lans
Sphere Detection in MIMO Communication Systems with Imperfect Channel State Information
Novel Channel Interference Reduction in Optical Synchronous FSK-CDMA Network Using a Data-Free Reference
An Adaptive Coherent Receiver for MPSK/MDPSK over the Nonselective Rayleigh Fading Channel with Unknown Characteristics
Code Shift Keying Impulse Modulation for UWB Communications
Wireless Mesh Networks: A Survey
Performance of Coded Multi-Carrier DS-CDMA Systems in Multi-Path Fading Channels
A VLSI Architecture for Visible Watermarking in a Secure Still Digital Camera (S²DC) Design(Corrected)

CALL ADMISSION CONTROL OPTIMIZATION IN WIMAX NETWORKS

2009-11-12T00:29:00.000-08:00

INTRODUCTION

Worldwide interoperability for microwave access (WiMAX) is a promising technology for last-mile Internet access, particularly in the areas where wired infrastructures are not available. In a WiMAX network, call admission control (CAC) is deployed to effectively control different traffic loads and prevent the network from being overloaded. In this paper, we propose a framework of a 2-D CAC to accommodate various features of WiMAX networks. Specifically, we decompose the 2-D uplink and downlink WiMAX CAC problem into two independent 1-D CAC problems and formulate the 1-D CAC optimization, in which the demands of service providers and subscribers are jointly taken into account. To solve the optimization problem, we develop a utility- and fairness-constrained optimal revenue policy, as well as its corresponding approximation algorithm.

THERE exist many regions in the world where wired infrastructures (i.e., T1, DSL, cables, etc.) are difficult to deploy for geographical or economic reasons. To provide broadband wireless access to these regions, many researchers advocate worldwide interoperability for microwave access (WiMAX), which is an IEEE 802.16 standardized wireless technology based on an orthogonal frequency-division multiplexing (OFDM) physical-layer architecture. To support a variety of applications, IEEE 802.16 has defined four types of service:

1) unsolicited grant service (UGS);

2) real-time polling service (rtPS);

3) non-real-time polling service (nrtPS); and

4) best effort (BE) service.

In a WiMAX network with heterogeneous traffic loads, it is essential to find a call admission control (CAC) solution that can effectively allocate bandwidth resources to different applications. In this Project, a proposed WiMAX CAC framework, which effectively meets all operational requirements of WiMAX networks. In this CAC framework, we decompose the 2-D uplink (UL) and downlink (DL) WiMAX CAC problem into two independent 1-D CAC problems. We further formulate the 1-D CAC as an optimization problem under a certain objective function, which should be chosen to maximize either the revenue of service providers or the satisfaction of subscribers.

With respect to 1-D CAC optimization problems, most previous studies were focused only on two approaches:

1) the optimal revenue strategy (also known as the stochastic knapsack problem) and

2) the minimum weighted sum of blocking strategy .

In this project, we will show that these two strategies are, in fact, equivalent. Therefore, we can mainly concentrate on the investigation of the optimal revenue strategy and view the minimum weighted sum of blocking strategy as the basis for fast calculation algorithms. Clearly, the optimal revenue policy only considers the profit of service providers. As an effort to conduct a multi objective study, in this paper, we will also take into account the requirements from WiMAX subscribers and develop a policy with a satisfactory tradeoff between service providers and subscribers.

The Project includes the following:

1) The development of a framework of CAC for WiMAX networks;

2) The investigation on various CAC optimization strategies; and

3) The proposal of a series of constrained greedy revenue algorithms for fast calculation. Through detailed performance evaluation, the study carried out in this paper will show that the proposed CAC solution can meet the expectations of both service providers and subscribers.

Modules:

CAC model for WiMAX networks
Calculate the UL and DL capacity
1-D CAC optimization strategies and develop their corresponding approximation algorithms

The following parameters calculate using Greedy algorithm:

Utility Requirement
Fairness Requirement
Constrained Optimal Revenue Strategy

Simulation graphs:

Traffic arrival vs Revenue
Traffic arrival vs Utility
Blocking probability vs Traffic arrival

VIDEO DEMO

APPLICATION OF SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM FOR IMPROVED BLOOD CELL RECOGNITION

2009-10-12T23:42:00.000-07:00

THE RELATIVE counting and assessment of the blood cells in the bone marrow of patients are very informative in clinical practice. It is particularly important for patients suffering from leukemia in the observation of the development stage of the illness and the preparation of the treatment of patients. To achieve proper diagnosis of the disease, we have to recognize the cells at different stages of their development and calculate their relative quantity in the aspirated bone marrow. There are different cell lines in the bone marrow, the most important of which are the granulocytic and lymphocytic (white blood cells) and erythrocytic (red blood cells) series .

The blood cells in the human bone marrow are continuously developing, transforming themselves from one type to another within the same development line. In the development of the white blood cells, the specialists recognize the myeloblast, promyelocyte, myelocyte, metamyelocyte, band neutrophil, and segmented neutrophil. In the case of the erythrocytic line,three different stages are recognized:
1) basophilic erythroblast;
2) polychromatic erythroblast; and
3) pyknotic erythroblast.

In the lymphocyte line, we recognize the prolymphocyte and lymphocyte cells. The most difficult problem is the recognition between two neighboring cells in their development line since the cells are very similar and the border point between two neighbors is not well defined (even for specialists).

This project presents the application of a genetic algorithm (GA) and a support vector machine (SVM) to the recognition of blood cells based on the image of the bone marrow aspirate. The main task of the GA is the selection of the features used by the SVM in the final recognition and classification of cells. The automatic recognition system has been developed, and the results of its numerical verification are presented and discussed. They show that the application of the GA is a powerful tool for the selection of the diagnostic features, leading to a significant improvement of the accuracy of the whole system.

MATLAB PROJECTS

2009-08-11T03:12:00.000-07:00

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TECHNICAL INFORMATION - ACADEMIC PROJECT

2009-08-05T03:51:00.000-07:00

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A NEW PARAMETER FOR UWB INDOOR CHANNEL PROFILE IDENTIFICATION

2009-04-15T02:35:00.000-07:00

INTRODUCTION

KNOWING the channel profile is very useful in order to maximize performance or minimize interferences in a wireless communication environment. There are several parameters which have to be properly set in the receiver depending on the link condition (LOS, Quasi-LOS, NLOS,extreme NLOS): for example the time delays and amplitudes of the channel multipath estimation algorithm, the number of fingers of the rake receiver, etc. The channel parameters (delay and amplitude of the receiving paths) estimation algorithm is particularly sensitive to the link condition. This means that if a simpler algorithm is selected under LOS conditions, the algorithm under NLOS conditions must be more complex in order to guarantee the performance. Similarly, the number of fingers of the rake receiver has to be selected depending on the link condition in order to achieve quality with lowest complexity.

Unfortunately it is not easy to identify the channel. In the literature there are few papers proposing parameters that allow to distinguish only between the LOS and the NLOS condition. Therefore, the present paper has a twofold aim: on the one hand, it intends to search for a new index that allows to distinguish more precisely between LOS and NLOS in every environment, and, furthermore, between different LOS or NLOS conditions, creating a sub-set of LOS links and NLOS links ordered with an increasing quality; on the other hand it aims at defining a low complex parameter that allows the identification of the quality of the received signal in an UWB indoor environment.

Algorithms for NLOS identification are proposed. The Time-Of-Arrival (TOA) of the signal is studied in order to identify if a mobile station is experiencing LOS or NLOS condition. In particular, the TOA measured probability distribution function has been considered. In, a joint power envelope and TOA measured algorithms are proposed to distinguish between LOS and NLOS. All the algorithms mentioned above have quite high complexity and it is necessary to estimate the channel parameters in order to apply the algorithm. In particular, multipath time delays are needed in , whereas in multipath amplitudes are needed. Moreover, both those algorithms can distinguish only LOS/NLOS condition

ROBUST DWT-SVD DOMAIN IMAGE WATERMARKING:

2009-03-18T23:06:00.000-07:00

INTRODUCTION

Watermarking (data hiding) is the process of embedding data into a multimedia element such as image, audio or video. This embedded data can later be extracted from, or detected in, the multimedia for security purposes. A watermarking algorithm consists of the watermark structure, an embedding algorithm, and an extraction, or a detection, algorithm. Watermarks can be embedded in the pixel domain or a transform domain. In multimedia applications, embedded watermarks should be invisible, robust, and have a high capacity. Invisibility refers to the degree of distortion introduced by the watermark and its affect on the viewers or listeners. Robustness is the resistance of an embedded watermark against intentional attacks, and normal A/V processes such as noise, filtering (blurring, sharpening, etc.), resampling, scaling, rotation, cropping, and lossy compression. Capacity is the amount of data that can be represented by an embedded watermark. The approaches used in watermarking still images include least-significant bit encoding, basic M-sequence, transform techniques, and image-adaptive techniques.An important criterion for classifying watermarking schemes isthe type of information needed by the detector:

• Non-blind schemes: Both the original image and the secret key(s) for watermark embedding.

• Semi-blind schemes: The secret key(s) and the watermarkbit sequence.

• Blind schemes: Only the secret key(s).

Typical uses of watermarks include copyright protection (identification of the origin of content, tracing illegally distributed copies) and disabling unauthorized access to content. Requirements and characteristics for the digital watermarks in these scenarios are different, in general. Identification of the origin of content requires the embedding of a single watermark into the content at the source of distribution. To trace illegal copies, a unique watermark is needed based on the location or identity of the recipient in the multimedia network. In both of these applications, non-blind schemes are appropriate as watermark extraction or detection needs to take place in a special laboratory environment only when there is a dispute regarding the ownership of content. For access control, the watermark should be checked in every authorized consumer device used to receive the content, thus requiring semi-blind or blind schemes. Note that the cost of a watermarking system will depend on the intended use, and may vary considerably. Two widely used image compression standards are JPEG and JPEG2000. The former is based on the Discrete Cosine Transform (DCT), and the latter the Discrete Wavelet Transform (DWT).

In recent years, many watermarking schemes have been developed using these popular transforms. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.In all frequency domain watermarking schemes, there is a conflict between robustness and transparency. If the watermark is embedded in perceptually most significant components, the scheme would be robust to attacks but the watermark may be difficult to hide. On the other hand, if the watermark is embedded in perceptually insignificant components, it would be easier to hide the watermark but the scheme may be least resistant to attacks. In image watermarking, two distinct approaches have been used to represent the watermark. In the first approach, the watermark is generally represented as a sequence of randomly generated real numbers having a normal distribution with zero mean and unity variance. This type of watermark allows the detector to statistically check the presence or absence of the embedded watermark. In the second approach, a picture representing a company logo or other copyright information is embedded in the cover image. The detector actually reconstructs the watermark, and computes its visual quality using an appropriate measure.

VIDEO DEMO

SIMULATION MODEL OF INVISIBLE ROBUST WATERMARKING USING VLSI/MATLAB

2009-03-18T21:02:00.000-07:00

INTRODUCTION

Owing to the usage of Internet, concerns about protecting and enforcing intellectual property (IP) rights of the digital content are mounting. Unauthorized replication and manipulation of digital content is relatively easy and can be achieved with inexpensive tools. Digital rights management (DRM) systems address issues related to ownership rights of digital content. Various aspects of content management – namely, content identification, storage,representation, and distribution – and IP rights management are highlighted in DRM.

Although unauthorized access of digital content is being prevented by implementing encryption technologies, these approaches do not prevent an authorized user from illegally replicating the decrypted content. igital watermarking is one of the key technologies that can be used in DRM systems for establishing ownership rights, tracking usage, ensuring authorized access,preventing illegal replication, and facilitating content authentication. Therefore, a two-layer protection mechanism utilizing both watermarking and encryption is needed to build effective DRM systems that can address IP rights and copyright issues .

In this project, the invisible watermarking aspect of DRM. Digital watermarking is the process of
embedding data, called a watermark, into a multimedia object such that the watermark can be detected whenever needed for DRM. The object may be an image, audio, video, text, or graphics. However, in this project“image” is the primary multimedia object, but similar work can be undertaken for other multimedia objects. In general, any watermarking algorithm consists of three parts: the watermark, the encoder (insertion algorithm), and the decoder and comparator (verification or extraction or detection algorithm). An entity, called the watermark key, which is unique and exhibits a one-to-one correspondence with every watermark, is also used during the process of embedding and detecting the watermark.

The key is private and known only to authorized parties,eliminating the possibility of illegal usage of digital content.Watermarks and watermarking techniques can be divided into different categories in various ways.Watermarks can be embedded in various domains, including the spatial and the frequency domains. The various transformations that have been used extensively as alternatives to the spatial domain are the discrete cosine transform (DCT), the Fourier transform (FT), and the wavelet transform (WT). Frequency-based methods have several advantages over spatial domain methods.For example, DCT domain techniques are more robust to attacks, and the perceptible quality of DCT domain watermarked images is better. On the other hand, spatial domain watermarking algorithms have less computational overhead than frequency domain algorithms. Spatial domain watermarking algorithms can also be faster in terms of computational time and hence are more suitable for real-time applications. Thus, we have focused on spatial domain watermarking because our ultimate goal is to develop VLSI architectures and chips such that real-time watermarking in the framework of electronic components would be possible.Digital watermarks can be divided into visible and invisible types, based on human perception.

A visible watermark is a secondary translucent image overlaid onto the primary image. An invisible watermark, on the other hand, is completely imperceptible. An invisible robust watermark is embedded in such a way that alterations made to the pixel value are not noticeable and can be recovered only with the appropriate decoding mechanism. An invisible fragile watermark is embedded in such a way that any manipulation or modification of the image would alter the watermark.

INVISIBLE WATERMARKING ALGORITHMS

Invisible robust image watermarking algorithm and an invisible fragile image watermarking algorithm whose VLSI architecture and chips are described in subsequent sections. The algorithms selected are simple and effective and, with modifications, can result in high-performance hardware that can perform watermarking in real time. We discuss the insertion and detection methods in brief, with the modifications necessary to facilitate hardware implementation.

VIDEO DEMO

SPHERE DETECTION IN MIMO COMMUNICATION SYSTEMS WITH IMPERFECT CHANNEL STATE INFORMATION

2009-03-18T11:51:00.000-07:00

Introduction

The deployment of multiple antennas in wireless communication systems is a promising method for the future high data rate wireless systems. It is shown that without increasing the total power consumption, this method bring the possibility to reach very high bandwidth efficiency compared to single antenna systems for a Rayleigh fading channel; where the transmission medium is enriched with scatterers .

In a MIMO system, multiple element antenna arrays are deployed at both the transmitter and the receiver. In such systems, the transmitting data stream is first demultiplexed into parallel data sub-streams and then transmitted by each antenna element. In these systems, there exist a separate path between each transmit-receive antenna pairs, where in a rich scattering environment, these channels can be considered statistically independent. To detect the transmitted signal at the receiver, the knowledge of these channels is necessary. Different estimation methods can be used to estimate the channel matrix at the receiver. These methods are mainly training based methods, superimposed raining methods, and the methods that estimate the channel matrix blindly.

However, the presence of noise and interference, outdated data (especially when the channel is changing very fast), and quantization error are sources of channel estimation inaccuracy. In the sense of symbol error rate (SER), the optimum detection method in MIMO system receivers is the maximum likelihood (ML) detector. ML performance can be obtained with lower computational complexity using the sphere detection (SD) algorithm almost in many cases of interest. Practically, the inevitable imperfect information about the channel increases the SER and corrupts the system performance. In this project,a briefly study the effect of channel estimation error on the performance of sphere detection method, where we analytically propose two modified and robust methods to cope with the destructive effect of channel errors on the performance of sphere detector.

MULTIVARIATE INTERPOLATION FOR IMAGE ENLARGEMENT

2009-03-18T04:59:00.000-07:00

INTERPLOATION:

Interpolation is a method of constructing new data points within the range of a discrete set of known data points. In engineering and science one often has a number of data points, as obtained by sampling or experimentation, and tries to construct a function which closely fits those data points. This is called curve fitting or regression analysis. Interpolation is a specific case of curve fitting, in which the function must go exactly through the data points.

DIGITAL IMAGE INTERPLOATION:

Digital image interpolation is the recovery of a continuous intensity surface from discrete image data samples. It is a link between the discrete world and the continuous one. In general, almost every geometric transformation requires interpolation to be performed on an image, e.g. translating, rotating, scaling, warping or other applications. Interpolation works by using known data to estimate values at unknown points.

For example: if you wanted to know the temperature at noon, but only measured it at 11AM and 1PM, you could estimate its value by performing a linear interpolation.If you had an additional measurement at 11:30AM, the bulk of the temperature rise occurred before noon, and could use this additional data point to perform a quadratic interpolation.

Image interpolation works in two directions, and tries to achieve a best approximation of a pixel's color and intensity based on the values at surrounding pixels.

TYPES OF INTERPOLATION ALGORITHMS

Common interpolation algorithms can be grouped into two categories: adaptive and non-adaptive. Adaptive methods change depending on what they are interpolating (sharp edges vs. smooth texture), whereas non-adaptive methods treat all pixels equally

Non-adaptive algorithms include: nearest neighbor, bilinear, bicubic, spline and others. Depending on their complexity, these use anywhere from 0 to 256 (or more) adjacent pixels when interpolating. The more adjacent pixels they include, the more accurate they can become, but this comes at the expense of much longer processing time. These algorithms can be used to both distort and resize a photo

NEAREST NEIGHBOR INTERPOLATION
Nearest neighbor is the most basic and requires the least processing time of all the interpolation algorithms because it only considers one pixel-- the closest one to the interpolated point. This has the effect of simply making each pixel bigger

BILINEAR INTERPOLATION
Bilinear interpolation considers the closest 2x2 neighborhood of known pixel values surrounding the unknown pixel. It then takes a weighted average of these 4 pixels to arrive at its final interpolated value. This results in much smoother looking images than nearest neighbor
The diagram to the left is for a case when all known pixel distances are equal, so the interpolated value is simply their sum divided by four

BICUBIC INTERPOLATION
Bicubic goes one step beyond bilinear by considering the closest 4x4 neighborhood of known pixels-- for a total of 16 pixels. Since these are at various distances from the unknown pixel, closer pixels are given a higher weighting in the calculation. Bicubic produces noticeably sharper images than the previous two methods, and is perhaps the ideal combination of processing time and output quality. For this reason it is a standard in many image editing programs, printer drivers and in-camera interpolation

HIGHER ORDER INTERPOLATION
There are many other interpolators which take more surrounding pixels into consideration, and are thus also much more computationally intensive. These algorithms include spline and sinc, and retain the most image information after an interpolation. They are therefore extremely useful when the image requires multiple rotations / distortions in separate steps. However, for single-step enlargements or rotations, these higher-order algorithms provide diminishing visual improvement as processing time is increased

VIDEO DEMO

SIMULATION MODEL OF VISIBLE WATERMARKING FOR JPEG IMAGE (3 D) USING VLSI/MATLAB

2009-03-17T10:55:00.000-07:00

Watermarking is the process that embeds data called a watermark, a tag, or a label into a multimedia object, such as images, video, or text, for their copyright protection. According to human perception, the digital watermarks can either be visible or invisible. A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a viewer on a careful inspection. The invisible watermark is embedded in such a way that the modifications made to the pixel value is perceptually not noticed, and it can be recovered only with an appropriate decoding mechanism. This project presents a new very large scale integration (VLSI) architecture for implementing two visible digital image watermarking schemes. The proposed architecture is designed to aim at easy integration into any existing digital camera framework.

Two fundamental operations performed by a digital camera are image capturing and storing. The images are subsequently transmitted in various forms over appropriate media. These images are always vulnerable to various forms of copyright attacks and ownership issues. The watermarking object may be an image, audio, video, or text .Whether the host data is in spatial domain, discrete cosine-transformed, or wavelet-transformed, watermarks of varying degree of visibility are added to present media as a guarantee of authenticity, ownership, source, and copyright protection.

According to human perception, the digital watermarks can be divided into four categories:

1) visible;

2) invisible-robust;

3) invisible-fragile;

4) dual

A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a casual viewer on careful inspection. The invisible-robust watermark is embedded in such a way that modifications made to the pixel value is perceptually not noticed, and it can be recovered only with appropriate decoding mechanism. The invisible-fragile watermark is embedded in such a way that any manipulation or modification of the image would alter or destroy the watermark. A dual watermark is a combination of a visible and an invisible watermark . In this type of watermark, an invisible watermark is used as a back-up for the visible watermark. There are numerous software-based watermarking schemes available in literature. A vast research community involving experts from computer science, cryptography, signal processing, and communications, etc., are working together to develop watermarks that can withstand different possible forms of attacks, each one of which has its own applications and thus is equally important. There is a gap between the image capture and image transmission in thewaywatermarking is used presently. Once the images are acquired,watermarks are inserted in them offline, and then images are made available. The objective of this research work is to implement hardware-based watermarking schemes so as to bridge that gap. The watermark chip will be fitted in the devices that acquire the image and watermark the images in real time while capturing.

VIDEO DEMO

CONTENTION-BASED QOS MAC MECHANISMS FOR VBR VOIP IN IEEE 802.11E WIRELESS LANS

2009-03-17T09:29:00.001-07:00

INTRODUCTION

In recent years, Voice over IP (VoIP) has become an attractive alternative to the traditional public switched telephone network (PSTN). Providing QoS for VoIP on increasingly heterogeneous computer networks brings up many challenging issues. As an emergent component in computer networks, wireless local area network (WLAN) attracts wide attentions from both academia and industry. Furthermore, convergence of WLANs with cellular systems makes VoIP over WLAN (VoWLAN) an important research topic.Typically, VoWLAN is implemented as a networking protocol stack shown in Fig. 1.

At the top of this protocol stack, a number of popular VoIP codec, such as ITU G711, G729, and G723 may be adopted at the application layer. A real-time transport protocol (RTP) stack packs various sizes of audio payload in various intervals. When a VoIP packet, also known as payload, passes through RTP, UDP, IP, and MAC protocol layers, some extra bytes of overhead are added as the header or trailer. Finally, voice frames are transmitted according to MAC function over a radio link at the physical (PHY) layer.

Voice Model and Voice over IP (VoIP)

There are two types of VoIP: constant bit rate (CBR) VoIP and variable bit rate (VBR) VoIP. For CBR VoIP, a codec generates constant audio payload during the whole voice conversation period. Interactive voice conversations have two parties and each party has many talk spurts and silent periods alternately. A silence suppression technique is adopted to stop sending RTP packets during silent periods. One way tohandle VBR VoIP is to regard VBR VoIP as CBR traffic with its peak throughput. Thus the QoS MAC scheme studied in , can be directly applied. However, since each VBR VoIP call experiences silent/talk periods independently, some form of statistical multiplexing may be integrated into the QoS MAC to provide QoS for VBR VoIP with efficient bandwidth utilization. This is the primary objective of this research.IEEE802.11 WLAN and Related Works The IEEE802.11 WLAN is being deployed widely and rapidly for many different environments, including enterprise, home, and public access networking.

In a broadcast network, such as WLAN, the MAC sub-layer is responsible for arbitrating multiple stations to access a shared transmission medium. There are two channel access functions defined in the IEEE802.11 MAC: a mandatory Distributed Coordination Function (DCF), which is based on CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) with binary exponential backoff, and an optional Point Coordination Function (PCF),where the AP controls all the transmissions based on a centralized polling scheme. In order to enhance the IEEE 802.11 and provide QoS support over WLAN, the IEEE working group is currently finalizing the IEEE 802.11e standard . The IEEE 802.11e has a Hybrid Coordination Function (HCF), which includes a contention-based channel access part and a centrally controlled channel access part. The contention-based channel access of the HCF is referred as Enhanced Distributed Channel Access (EDCA); and the centrally controlled channel access is referred as HCF controlled channel access (HCCA).

QoS MAC is based on contention-based IEEE802.11e EDCA.There are increasing interests in providing QoS to VoIP sessions in WLANs. In the literatures, the focus was on designing a centrally controlled polling-based MAC to provide QoS to VoIP in WLANs. In the maximum number of VoIP sessions supported by contention-based MAC is evaluated in IEEE 802.11 (a/b) WLANs, and the unbalance problem of downlink/uplink traffics and the relationship between system capacity and VoIP codec are also studied. In , authors proposed a generic approach that relates VoIP performance with the dynamics of priority MAC. This method improves VoIP capacity in WLANs.

FPGA Prototyping of a Digital Camera for Image Security and Authentication

2009-03-17T09:25:00.000-07:00

Two fundamental operations performed by a digital camera are image capturing and storing. The images are subsequently transmitted in various forms over appropriate media. These images are always vulnerable to various forms of copyright attacks and ownership issues. This paper introduces a digital camera with built-in copyright protection and security mechanism for images produced by it. Since the proposal of the trustworthy digital camera, significant research has been done in developing algorithms for watermarking and encryption with the aim of using them in digital cameras. However, only few of these efforts are involved with the architectural development of the entire digital camera. Incorporation of encryption and watermarking together in the digital camera will assist in protecting and authenticating image files.In this work, architecture and a hardware efficient FPGA based watermark module towards the development of the complete digital camera.

WATERMARKING is the process that embeds data called awatermark, a tag, or label into a multimedia object such that the watermark can be detected or extracted later to make an assertion about the object. The object may be an image, audio, video, or text [1]. Whether the host data is in spatial domain, discrete cosine-transformed, or wavelet-transformed, watermarks of varying degree of visibility are added to present media as a guarantee of authenticity, ownership, source, and copyright protection. In general, any watermarking scheme (algorithm) consists of three parts, such as the following:

1) watermark;

2) encoder (insertion algorithm);

3) decoder and comparator (verification or extraction or detection algorithm)

Whether each owner has a unique watermark or an owner wants to use different watermarks in different objects, the marking algorithm incorporates the watermark into the object. The verification algorithm authenticates the object determining both the owner and the integrity of the object. Watermarks and watermarking techniques can be divided into various categories. The watermarks can be applied either in spatial domain or in frequency domain. It has been pointed out that the frequency-domain methods are more robust than the spatial-domain techniques. On the other hand, the spatialdomain watermarking schemes have less computational overhead compared with frequency-domain schemes. According to human perception, the digital watermarks can be divided into four categories:

1) visible;

2) invisible-robust;

3) invisible-fragile;

4) dual

A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a casual viewer on careful inspection. The invisible-robust watermark is embedded in such a way that modifications made to the pixel value is perceptually not noticed, and it can be recovered only with appropriate decoding mechanism. The invisible-fragile watermark is embedded in such a way that any manipulation or modification of the image would alter or destroy the watermark. A dual watermark is a combination of a visible and an invisible watermark . In this type of watermark, an invisible watermark is used as a back-up for the visible watermark. There are numerous software-based watermarking schemes available in literature. A vast research community involving experts from computer science, cryptography, signal processing, and communications, etc., are working together to develop watermarks that can withstand different possible forms of attacks, each one of which has its own applications and thus is equally important. There is a gap between the image capture and image transmission in thewaywatermarking is used presently. Once the images are acquired,watermarks are inserted in them offline, and then images are made available. The objective of this research work is to implement hardware-based watermarking schemes so as to bridge that gap. The watermark chip will be fitted in the devices that acquire the image and watermark the images in real time while capturing.

VIDEO DEMO

An FPGA-based Architecture for Real Time Image Feature Extraction

2009-03-17T09:16:00.000-07:00

Introduction

Realtime image pattern recognition is a challenging task which involves image processing, feature extraction and pattern classification. It applies to a wide range of applications including multimedia, military and medical ones. Its high computational requirements force systems to use very expensive clusters, custom VLSI designs or even both. These approaches suffer from various disadvantages, such as high cost and long development times.

Recent advances in fabrication technology allow the manufacturing of high density and high performance Field Programmable Gate Arrays (FPGAs) capable of performing many complex computations in parallel while hosted by conventional computer hardware. A variety of architecture designs capable of supporting realtime pattern recognition have been proposed in the recent literature, such as implementations of algorithms for image and video processing, classification and image feature extraction algorithms .

Although texture plays a significant role in image analysis and pattern recognition only a few architectures implement on-board textural feature extraction. Most prominent approaches include the extraction of Gabor wavelet features for face/object recognition and the computation of mean and contrast Gray Level Cooccurrence Matrix (GLCM) features. In the second case the two features are approximated without computing GLCMs.In this project a novel FPGA-based architecture for realtime GLCM texture analysis.

The combines both software and hardware to raster scan input images with sliding windows and produce 16-dimensional feature vectors consisting of four GLCM features calculated for four directions.

VIDEO DEMO

INCREASING FAIRNESS AND EFFICIENCY USING THE MADMAC PROTOCOL IN AD HOC NETWORKS

2009-03-17T09:06:00.000-07:00

Introduction

Ad hoc networks have become more and more popular and many research problems, such as routing, quality of service and security, are now addressed. Most of the current ad hoc networks are based on the IEEE 802.11 standard owing to the fact that this is the most widespread technology in the field of wireless local networks and it provides a distributed medium access with the DCF mode.Recently, different studies have shown some performance issues with the DCF mode, used in ad hoc network. These studies show that the origin of the performance problems comes from the MAC layer of this mode. These performance problems often lead to unfair situations and global performance loss .Several solutions have been proposed to improve 802.11 performance in wireless ad hoc networks by reducing unfairness issues or by improving global throughput. Recently, several approaches try to increase both throughput and fairness by modifying the 802.11 MAC layer. Most of these solutions are based on rate and topology information exchanged between the nodes. The proposed protocols, not based on this kind of information, either reduce the fairness issues to the detriment of the aggregate throughput or increase the overall throughput without solving the fairness issues. In the investigate the trade-off between aggregate throughput and fairness. They propose a model to compute the maximum aggregate throughput under various fairness schemes, but their algorithm is based on information propagation. Therefore, it is still a real challenge to design a fair MAC protocol for ad hoc networks that is distributed, topology independent, that relies on no explicit information exchanges and that is efficient, i.e. that achieves a good aggregate throughput.

In this project, a solution to this challenge by designing a new protocol, called MadMac,that increases fairness in 802.11-based ad hoc network while maintaining a good aggregate throughput in the network. One of the main advantages of MadMac is that it is easy to implement because it is only based on information provided by the 802.11 MAC layer.

NOVEL CHANNEL INTERFERENCE REDUCTION IN OPTICAL SYNCHRONOUS FSK-CDMA NETWORK USING A DATA-FREE REFERENCE

2009-03-17T08:59:00.000-07:00

INTRODUCTION

OPTICAL communication plays a significant role in achieving high bit rate and reliable digital communications in networks’ backbone and high-speed local-area networks (LANs) using a fiber-distributed data interface (FDDI) in next-generation network (NGN) subscribers such as fiber-to-the-home/kerb/node (FTTx). Recently, incoherent optical code-division multiple-access (OCDMA) systems have been investigated widely to apply for high-speed LANs,since they allow multiple users’ accessing network resources simultaneously.Asynchronous OCDMA (A-OCDMA) transceivers do not require frame synchronization; hence an optical orthogonal code (OOC) family with a good cross-correlation property is used in such a system . However, it has a few numbers of available sequences as they are code-length and weight dependent.Thus, in A-OCDMA, the number of users is very restricted as the number of available code sequences is very small. An alternative solution to increase the number of users is the synchronous OCDMA (S-OCDMA) where all users are synchronized in time frame.

This means the frame synchronization can be performed by packet synchronization consisting of either a unique or a different data sequence padded to each data sequence stream. Therefore, at the receiver, the synchronization is acquired based on the correlation property of the frame synchronizing sequence. Also, in the S-OCDMA, the number of accommodated simultaneous users can be increased by employing different prime-code families as spreading codes.In a conventional OCDMA, each time-slot is divided into chips that are equals to spreading code-length consisting of 1/0 sequences (depending on spreading codes) addressed to each user. The data are modulated and assigned through optical pulses (OPs) at certain chips of each allocated slot either in on–off keying (OOK) or in pulse-position modulation (PPM) formats.The modulated signal is then transmitted after multiplied by the spreading code in the OCDMA encoder via optical tappeddelay lines (OTDLs), i.e., the output OP in the first chip of a slot is spread in time domain to several chips corresponding to 1s of the spreading codes.

The OP sequences transmitted from users are combined (multiplexed) in the star passive optical network (SPON) couplers as an infrastructure reference and then transmitted over fiber-to-the destination (FTTx). At the receiver, in order to obtain the intended signal from the received signal,despreading is performed in a decorrelator, which consists of OTDL with inverse tap coefficients. The OPs are merged at the last chip in a slot, and the desired data are extracted in the demodulator based on modulation scheme.When the number of simultaneous active users increases, the effect of channel interference also inherently raises in directdetection OCDMA. The reason is that the OPs from the intended user and the interfering users overlap and the bit error rate (BER)tends to have an error floor. Therefore, it is required to reduce the probability of overlapping pulses from interfering users to mitigate the effect of cochannel interference. The probability of overlapping pulses has been reduced by changing the modulation scheme from OOK to -ary PPM. Although the variable pulse positions of pulse occur in PPM, error floor still exists .

On the other hand, interference cancellation techniques using an interference canceller at the receiver have been widely studied with different modulation schemes such as OOK and PPM. When OOK is used, the interference canceller is unable to completely eliminate the channel interference, since the reference signal has the components of the desired user. Also,the PPM using the same interference canceller technique has been proposed in; however, interference elimination is notattained due to the presence of the desired signal components in the reference signal. Alternatively, the interference canceller in which a reference signal does not contain the component of the desired signal has been proposed for the PPM system to improve the BER performance.This was carried out to avoid the cancellation of the desired signal as well as interferences.

PERFORMANCE IMPROVEMENT OF MC-CDMA SYSTEM THROUGH DSTBC SITE DIVERSITY

2009-03-17T08:46:00.000-07:00

INTRODUCTION

Broadband wireless access for evolving mobile internet and multimedia services are driving a surge of research on future wireless communication systems, which have to be highly spectral efficient in order to support multi-user access and high data rates. Therefore, MC-CDMA formed by combining orthogonal frequency division multiplexing (OFDM) with code division multiple accesses (CDMA) became significant research topics.The former is well suited for high data rate applications in frequency selective fading channels and the later is a multiplexing technique where number of users is simultaneously available to access a channel. With its capability of synchronous transmission, MC-CDMA is suitable for downlink of cellular communication systems. High data rate MC-CDMA systems can additionally employ MIMO techniques, e.g., Alamouti codes and STBC .Data transmission involves spreading operations which are carried out by short channelisation code and long scrambling code.

Short channelisation code helps in separating the signals of different users present within the cell and long scrambling code mitigates the effects of interference produced by users belonging to other cells. However, the scrambling codes are generally not orthogonalised among cells. Therefore, since the signals from other cells cannot be orthogonalised to the signals of its own cell, multi-cell interference exists. In high data rate transmission system over frequency selective fading channel, multi-cell interference results in degradation of bit-error rate (BER) and this characteristic affects the performance of MCCDMA systems. Site diversity technique has been proposed for realizing CDMA and OFDM systems to minimize multi-cell interference. Site diversity system transmits encoded signals from several base stations and these signals are combined at the receiver with decoding operation. This method does not have inter-cell interference. Scrambling codes are assigned to each base station to maintain orthogonality among the signals between the cells and reduces interference among them. The same technique is applied to MC-CDMA system.

In this project STBC and DSTBC with multiple antennas are used in the base stations and also exploiting several base stations the site diversity is obtained.Moreover, by using various combining techniques the performance of the system is analyzed.

ACTIVE NOISE CANCELLATION WITH A FUZZY ADAPTIVE FILTERED-X ALGORITHM

2009-03-17T08:35:00.000-07:00

Introduction

The technique of active noise cancellation (ANC), which uses artificial signals to cancel undesired noise, has received much attention during the past decade because of recent advances in electronics and microcomputers. Conventional methods, called passive noise control (PNC), have the ability to suppress the higher frequency acoustic noise rather than the lower frequency noise, as proven by several researchers in many papers. However, industrial acoustic noise often has its main power on lower frequencies,where the wavelength of sound is so long that passive techniques are no longer cost-effective because they require material that is too bulky and heavy, such as the silencer of a car.In contrast to passive methods, active methods not only permit the cancellation of lower frequency noise, but also reduce the weight, volume and cost of the overall noise control system.

To put in place an ANC system, one has to identify some transfer functions of acoustic plants and transducers, such as microphone, speaker and duct plant, to generate the correct anti-noise signal. Many researchers have employed the filtered-X algorithm as an active noise controller because of its simplicity. The filtered-X algorithm is also an adaptive filter and its weighting parameters can be automatically updated by the least mean square (LMS) algorithm. This approach is effective at attenuating lower frequency noise, such as that from a fan, compressor, or engine noise in an acoustic duct. However, there are still several problems with the filtered-X strategy. One of the most critical disadvantages of the filtered-X LMS algorithm is the low convergence speed. This is because the filtered-X algorithm needs a small step gain to update the weighting parameters in order to maintain stable performance of the system. A concurrent difficulty is that small step gains cannot update the weights in time to keep up with the change of residual noise, which plays a leading role in the filtered-X algorithm. Hence, the tracking speed is very slow and an accurate anti-signal cannot be derived to cancel the undesired noise.

These decrease the performance of broadband noise reduction.A few ANC systems based on fuzzy logic have been proposed, most of which use FIR filters or PD controllers to suppress noise, and then use a fuzzy method to adapt the system parameters. However, these approaches still require mathematical information about the duct plant, and they are still very complex.Rather than another fuzzy ANC system, this project proposes a fuzzy adaptive filtered-X algorithm to enhance the performance of ANC systems.The fuzzy filtered-X algorithm is mainly composed of linguistic information from human experts, and only a little numerical information is needed. Moreover, this method can minimise the residual noise of a fuzzy adaptive ANC system by properly setting the initial weighting parameters. Hence, the critical problem of low convergence speed is overcome and the residual noise can be minimised. Compared with other ANC schemes, the proposed fuzzy approach provides a very easy way to develop an active noise controller. In addition, the proposed fuzzy adaptive system can be used for many other applications.

SPATIAL MULTIPLEXING IN CELLULAR MIMO-CDMA SYSTEMS WITH LINEAR RECEIVERS OUTAGE PROBABILITY AND CAPACITY

2009-03-17T03:23:00.000-07:00

INTRODUCTION

THE idea of using multiple receive and transmit antennas has emerged as one of the most significant technical breakthroughs in modern wireless communications. A large suite of techniques, known collectively as multiple input multiple output (MIMO) communications, have been developed in the past several years to exploit the resulting multidimensional channel. Significant spectral efficiency advantages can be achieved by exploiting the multidimensional MIMO channels in point-to-point communication.

For future cellular systems to compete in mobile data market with emerging technologies like 802.16e/WiMax in the medium to long-term, multi-antenna transmission and reception will be required to achieve the requisite high data rates. Since any well-designed cellular system is by nature interference-limited and it is in the strong interest of service providers to provide universal frequency reuse and high per-cell loading, MIMO systems, especially spatial multiplexing, will need to function reliably in an interference-limited environment.

Initial investigations on MlMO systems with co-channel interference can be seen in, which quantified the throughput of multicell MIMO systems with spatial multiplex¬ing by computer simulations. They showed that co-channel interference could seriously degrade the overall capacity of a spatial multiplexing system to the point of negligible im¬provement over single input multiple output (SIMO) systems.The authors showed that it is in fact preferable to have all users utilize only a fraction of the available substreams in an interference-limited MIMO system with linear receivers and single user detection.

The common conclusion of these papers is that the independent data streams effectively become independent interferers, and without any extra diversity, sufficient degrees of freedom to combat this co-channel interference are not available, unless the number of receive antennas is very large. Consequently, it is possible that although spatial multiplexing has a fundamental capacity advantage relative to transmit diversity, that this advantage is lost in cellular MIMO systems with linen receivers if any extra diversity is not provided. The most straightforward way to provide extra diversity is to exploit spatial diversity. However, there is a fundamental tradeoff between spatial multiplexing and spatial diversity in the limited degrees of freedom.

Spread spectrum is a likely candidate for the extra diversity because it can simultaneously provide frequency diversity and robustness to interference. It should also be noted that in MIMO spread spectrum (MIMO-CDMA) systems a larger number of transmit antennas can give a higher processing gain for a given data rate, which provides additional degrees of freedom for suppressing co-channel interference.

Furthermore, CDMA systems with low or unity spreading factors can be viewed as general interference-limited systems. For these reasons of current relevance and generality, the effectiveness of spatial multiplexing in cellular MIMO-CDMA systems with linear receivers needs to be carefully investigated. This paper provides a general framework for analyzing the outage capacityl of cellular MIMO-CDMA systems and investigates effectivenes of spatial multiplexing in terms of outage rapacity.

VIDEO DEMO

HIERARCHICAL CONTOUR MATCHING FOR DENTAL X-RAY RADIOGRAPHS

2009-03-16T12:45:00.000-07:00

Introduction

Human identification based on dental features has always played a very important role in forensics. The main goal of dental biometrics is to identify deceased individuals, where the conventional biometric features, i.e., iris, fingerprint, and face may not be applicable . The dental radiographs provide information about the teeth, such as shapes of the crowns and the roots, and dental works such as fillings and bridges. The radiographs acquired after death are called postmortem (PM) radiographs, and the radiographs acquired while the person is alive are called antemortem (AM) radiographs. In dental biometrics, the identification is carried out by analyzing and comparing PM dental records of a decedent against a database of AM records to find best matches. Sometimes the decedent’s teeth are compared to AM written records although the most accurate and reliable method is the comparison of AM and PM radiographs.Dental features survive most PM events that may disrupt or change other body tissues, e.g. bodies of victims of motor vehicle accidents, violent crimes, and work place accidents, whose bodies can be disfigured to such an extent that identification by a family member is neither reliable nor desirable. As a result, dental features are regarded as the best candidates for PM biometric identification; this is due to their survivability and diversity. Although there is a number of effective solutions for biometric identification that are currently available, new approaches and techniques are necessary to overcome some of the limitations of current systems. Currently we are building an automated dental identification system (ADIS) for identifying individuals using their dental X-ray records. The system can be used by law enforcement agencies to locate missing individuals using databases of X-ray dental radiographs.

Radiograph segmentation

The goal of radiograph segmentation is to localize the region of each tooth in a dental X-ray image. Dental radiographs may suffer from poor quality, low contrast and uneven exposure that complicate the task of segmentation. Dental X-ray images have three different regions: soft tissue regions and background with the lowest intensity values, bone regions with average intensity values, and teeth regions with the highest intensity values. In some cases the intensity of the bone areas is close to the intensity of the teeth, which makes it difficult to use a single threshold for segmenting the entire image. In this project used our segmentation technique introduced ,which starts by applying iterative thresholding followed by adaptive thresholding to segment the teeth from both the background and the bone areas. After thresholding, horizontal integral projection and vertical integral projection are applied to separate each individual tooth. The contour pixels for each tooth are then extracted and sampled to represent each tooth by an equal number of contour pixels. Fig. 1 shows the segmentation results on a few bite-wing images.

A REAL-TIME ADAPTIVE LEARNING METHOD FOR DRIVER EYE DETECTION

2009-03-16T12:36:00.000-07:00

Introduction

Driver drowsiness is one of the major causes of traffic accidents on road. Monitoring the driver’s vigilance level, and issuing an alert when he/she is not paying enough attention to the road is a promising way to reduce the accidents caused by driver factors. Thus it could become an important part in the development of the advanced safety vehicle. The driver’s facial information, especially the eye status is often believed to give some clues of his/her drowsiness level. The driver eye detection methods based on computer vision use camera to obtain the driver’s facial information, extract the parameters that are believed to be related to the drowsiness level of the driver. Many researchers use Percent of Eyelid Closure (PERCLOS) as an indicator to detect drowsiness. Ishii et al. build a system using driver’s facial expression to reflect the mental status. Ueno et al. described a method using eye open level to detect drowsiness . Other commonly used parameters also include the blinking interval, pupil position, etc .In developing those driver monitoring systems, a reliable real-time driver eye detection method is one of the essential parts. In developing the driver eye detection method, we use a driving simulator and a CCD camera mounted at the back of the steering wheel for capturing the driver’s facial image. The driver was asked to drive normally on a circular course with a constant speed of 80 km/h. The driving simulator and scenario in the test are shown in Figure 1.

To adapt to the variances in eye shape and size of different individuals, the algorithm for eye positioning is composed of learning and non-learning mode. The system starts from the learning mode, in which face detection is firstly performed to narrow the search region. Then contour detection and heuristic rules are used to identify the Region Of Interest (ROI) of the eye. By learning the eye region, a set of images that satisfy the pre-set rules are obtained to form the eye templates. When the number of successful learning exceeds a preset threshold, the learning mode swatches to the nonlearning mode, in which the eye templates obtained from the previous stage are used to get the eye position. If the eye position does not meet certain rules, the method will swatch back to the learning mode to re-learn the eye region.

NON-SYMMETRIC DECOMPANDING FOR IMPROVED PERFORMANCE OF COMPANDED OFDM SYSTEMS

2009-03-16T11:50:00.000-07:00

INTRODUCTION

ORTHOGONAL Frequency Division Multiplexing (OFDM) has been widely accepted and is being considered for future high data rate wireless access. Despite its popularity, OFDM does have its own peculiar problems,of which high PAPR happens to be one. In order to reduce PAPR of OFDM signals, two schools of method have been suggested. A good account of these techniques is given in and references cited therein. One group intends to reduce the occurrence of large peaks before multicarrier modulation by breaking the independence among signals on different subchannels. The methods include coding,selective mapping and partial transmitting . However, in these methods, either redundancy or computational complexity is relatively high, which may result in large delay or overhead in practical systems. The second group processes the OFDM signals directly. Deliberate clipping before D/A conversion is seen as the simplest way to achieve low PAPR. Nevertheless, digital clipping suffers from three problems: in-band distortion (IBR), which causes significant performance penalty; out-of-band radiation (OBR), which reduces the spectral efficiency; and peak regrowth after D/A conversion. Recently, companding transforms have been proposed by several authors to reduce PAPR .

It has been claimed that the companding transforms outshine the performance of the clipping by a fair margin. However, in these works, the impact of filtering out OBR has not been considered in detail. In this letter, we explore the impact of filtering out OBR on the performance of companded OFDM systems. Using computer simulations, we show that filtering does deteriorate
the performance significantly. We outline the reasons for this performance degradation and further propose a method to compensate for the degradation. This method is based on the use of curve fitting method to find out a suitable polynomial to be used for decompanding at the receiver. As a result, the companding and decompanding transforms are no longer exact inverse of each other. To the best knowledge of the authors, this non-symmetric companding and decompanding method has not been considered in the literature yet. We will show through simulations that this method indeed improves the performance in comparison to existing symmetric methods when filtering is necessary for bandlimited conditions.

AUTOMATIC RECOGNITION OF EXUDATIVE MACULOPATHY USING FUZZY CMEANS CLUSTERING AND NEURAL NETWORKS

2009-03-12T03:48:00.000-07:00

Introduction

Blindness is a common outcome of diabetic-related eye diseases. When background changes occur in the central retina, the condition is termed diabetic maculopathy, and visual acuity is at risk. Much of the blindness can be prevented if the condition is detected early enough for laser treatment. Unfortunately, because visual loss is often a late symptom of advanced diabetic maculopathy, many patients remain undiagnosed even as their disease is causing severe retinal damage. Hence, there is an urgent need for mass-screening retinal examination for the early detection and treatment of such diseases.Current methods of detection and assessment of diabetic maculopathy is manual, expensive, potentially inconsistent, and require highly trained personnel to facilitate the process by searching large numbers of fundus images.

In contrast, a good, automatic method based on modern digital image processing techniques will be faster, will need less, perhaps no human intervention, and will yield consistent results. The aim of our work is to extend the capabilities and productivity of the ophthalmologist and to provide decision support to physicians. We hope to develop a system that will perform our overall aims and objectives including identifying the proportion of the colour retinal image that contains exudates (EXs), and separating them from the other retinal anomalies and pathologies. In this paper, we report a method that first normalises the colours of the retinal image, since this can vary between different races. It then performs local contrast enhancement followed by Fuzzy C-Means (FCM) clustering to highlight salient regions, extracts relevant features, and finally classifies those regions using a multi-layer perceptron neural network.Most of the work carried out so far in this area consider either Fluorescein Angiogram images or gray level images. The former is time consuming for physicians, inconvenient for patients, costly, and cause nonuniform illumination across the image due to varying amounts of background fluorescence. In the latter,monochrome images of the retina do not always capture all the available information for a more accurate segmentation. Other semi-automated methods for measuring EXs have been developed that need human intervention for defining a threshold, thus reducing the objectivity of the technique. Gardner et al used artificial neural networks for identification of EXs by classifying whole regions of size 20x20 pixels.

VIDEO DEMO

GRID POWER QUALITY WITH VARIABLE SPEED WIND TURBINES

2009-03-10T09:51:00.000-07:00

INTRODUCTION

RENEWABLE sources often produce power and voltage varying with natural conditions (wind speed, sun light etc.,) and grid connection of these sources is essential if they are ever to realize their potential to significantly alleviate the present day problems of atmospheric pollution and global warming. However, electric utility grid systems cannot readily accept connection of new generation plant without strict conditions placed on voltage regulation due to real power fluctuation and reactive power generation or absorption, and on voltage waveform distortion resulting from harmonic currents injected by nonlinear elements of the plant.

The project describes a wind farm comprising a number of turbines housing direct-drive, variable-speed permanent- magnet generators of a novel type proposed and whose variable-speed capability is achieved through the use of an advanced power electronic converter.The modeling of the wind power converter with the network is addressed using case studies of voltage fluctuation and harmonics propagation. The studies have demonstrated that the impacts on voltage fluctuation and harmonic distortion can be minimized and furthermore, the network voltage control could also be improved by the advanced power electronic converters proposed.

DOUBLY FED INDUCTION GENERATOR USING BACK-TO-BACK PWM CONVERTERS

2009-02-18T21:50:00.000-08:00

Introduction

The doubly fed induction machine using an AC-AC converter iin the rotor circuit (Scherbius drive) has long been a standard drive option for high-power applications involving a limited speed range. The power converter need only be rated to handle the rotor power.Vector-control techniques for the independent control of torque and rotor excitation current are well known, whilst Jones and Jones, for example,that a vector-control strategy can be used for decoupled control of active and reactive power drawn from the supply. Wind-energy generation is regarded as anatural application for the Scherbius DFIG system, since the speed range (from cut-in to rated wind velocity)may be considered restricted.

Most Scherbius FIG systems reported employ either a current-fed(naturally commutated) DC-Link converter or cycloconverter in the rotor circuit. Smith et al.describe the rated speed settings, gearbox ratios, and machine and converter ratings for variable-speed wind generation using the DFIG. Cardici and Ermis, and Uctug et al,have presented strategies aimed at maximising the total electrical power output from the DFIG. The use of a current-fed DC-link converter has a number of disadvantages: the DC-link choke is expensive, and an extra commutation circuit is required for operation at synchronous speed (which lies within the operational speed range), and this has resulted in poor performance at low slip speeds. In addition, such a converter draws rectangular current waveforms from the supply.

The problem at synchronous speed may be overcome by use of a cycloconverter, and vector- controlled Scherbius schemes with 6-pulse cycloconverters have been described by Leonhard and Walczyna. Yamamoto and Motoyoshi have presented a detailed analysis of the current harmonics drawn from the supply, which is still a problem in this type of drive. Machmoum et al,have presented an implementation with a simpler 3-pulse cycloconverter,whilst Holmes and Elsonbaty describe a similar converter to excite a divided-winding doubly-fed machine, which improves the speed range to 50% slip at the expense of increased machine complexity. Both of these schemes have the disadvantage of requiring a transformer to form the neutral; in addition, naturally commutated DC-link and cycloconverter schemes may, in many cases, require a transformer for voltage matching.The disadvantages of the naturally commutated DClink and cycloconverter schemes can be overcome by the use of two PWM voltage-fed current-regulated inverters connected back-to-back in the rotor circuit.The characteristics of such a Scherbius scheme,IN follows:
operation below, above and through synchronous speed with the speed range restricted only by the rotorvoltage ratings of the DFIG operation at synchronous speed, with DC currents injected into the rotor with the inverter working in chopping mode low distortion stator, rotor and supply currents independent control of the generator torque and rotor excitation Control of the displacement factor between the voltage and the current in the supply converter, and hence control over the system power factor.

A ZERO VOLTAGE SWITCHING SINGLE-PHASE INVERTER USING HYBRID PULSE-WIDTH MODULATION TECHNIQUE

2009-02-18T21:41:00.000-08:00

INTRODUCTION

Pulse width modulation at constant switching frequency is widely used in inverter applications.To reduce the size of filter components and improve the harmonic characteristics of inverters, higher switching frequency is preferred. However, a drawback of higher switching frequency is the increase of switching losses. Switching losses are due to diode reverse recovery and hard turn off and turn on of active switches. As a result, the system efficiency is reduced. In higher power applications, where relatively slow switches are used, the switching loss is one of the major concerns. Various soil switching techniques (zero voltage switching (ZVS) and zero current switching (ZCS)) have been proposed to reduce the switching losses and improve the efkiency in inverters. DC link switches are commonly used to achieve ZVS in different power electronic applications. The switch voltage stress limitation is one of the major issues in using DC link or clamp switches .The inverter with a clamp switch proposed can achieve zero voltage switching while limiting the voltage stress of the DC link to the input voltage.

In this project, to improve the system efficiency further, the singlephase inverter with the previously proposed DC-link switch is extended to operate with the hybrid pulse width modulation (HPWM) control scheme. With HPWM control, only two of the four switches in the full bridge inverter are pulse width modulated at high frequency.Compared to the case when all four switches are pulse width modulated, the switching loss of the inverter is reduced to one half.

AN IMAGE TRANSFORM APPROACH FOR HMM BASED AUTOMATIC LIPREADING

2009-02-15T11:08:00.000-08:00

INTRODUCTION

Automatic recognition of speech by using the video sequence of the speaker's lips, namely automatic lipreading, or speech-reading, has recently attracted signicant interest.Much of this interest focuses on ways of combining the video channel information with its audio counterpart, in the quest for an audio-visual automatic speech recognition (ASR) system that outperforms audio-only ASR. Such a performance improvement depends on both the audio-visual fusion architecture, as well as on the visual front end, namely, on the extraction of appropriate visual This features that contain relevant information about the spoken word sequence. In this project, we concentrate on the latter.We consider a number of visual features, propose new ones,compare them on the basis of lipreading performance, and investigate their robustness to video degradations.Various visual features have been proposed in the literature that, in general, can be grouped into lip contour based and pixel based ones. In the rst approach, the speaker's lip contours are extracted from the image sequence. A parametric or statistical lip contour model is then obtained, and the model parameters are used as visual features. Alternatively, lip contour geometric features are used. In the second approach, the entire image containing the speaker's mouth is considered as informative for lipreading, and appropriate transformations of its pixel values are used as visual features.

Lip Contour Extraction

The lip contour extraction system is described in detail else-where.In its current implementation, for each video eld, two channels of processing are used: A combination of shape and texture analysis, and a color segmentation, to rst locate the mouth and then the precise lip shape. Estimated outer and inner lip contours are depicted.For a single speaker, part of the outer lip contour is missed in less than 0.25% of the processed images. However, inner lip and multi-speaker contour estimation are less robust.

VIDEO DEMO

FAULT LOCATION IN EHV TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS

2009-02-13T05:26:00.000-08:00

Introduction

An overhead transmission line is one of the main components in every electric power system. The transmission line is exposed to the environment and the possibility of experiencing faults on the transmission line is generally higher than that on other main components. Line faults are the most common faults, they may be triggered by lightning strokes, trees may fall across lines, fog and salt spray on dirty insulators may cause the insulator strings to flash over, and ice and snow loadings may cause insulator strings to fail mechanically. When a fault occurs on an electrical transmission line, it is very important to detect it and to find its location in order to make necessary repairs and to restore power as soon as possible. The time needed to determine the fault point along the line will affect the quality of the power delivery. Therefore, an accurate fault location on the line is an important requirement for a permanent fault. Pointing to a weak spot, it is also helpful for a transient fault, which may result from a marginally contaminated insulator, or a swaying or growing tree under the line.

Fault location in transmission lines has been a subject of interest for many years. During the last decade a number of fault location algorithms have been developed, including the steady-state phasor approach, the differential equation approach and the traveling-wave approach (Lian and Salama, 1994), as well as two-end (Sheng and Elangovan, 1998) and one-end (Zhang et al., 1999) algorithms.

In the last category, synchronized (Kezunovic and Mrkic, 1994) and non-synchronized (Novosel et al., 1996) sampling techniques are used. However, two-terminal data are not widely available. From a practical viewpoint, it is desirable for equipment to use only one-terminal data. The one-end algorithms, in turn, utilize different assumptions to replace the remote end measurements. Most of fault locators are only based on local measurements. Currently, the most widely used method of overhead line fault location is to determine the apparent reactance of the line during the time that the fault current is flowing and to convert the ohmic result into a distance based on the parameters of the line.

It is widely recognized that this method is subject to errors when the fault resistance is high and the line is fed from both ends, and when parallel circuits exist over only parts of the length of the faulty line. Many successful applications of artificial neural networks (ANNs) to power systems have been demonstrated, including security assessment, load forecasting, control, etc. Recent applications in protection have covered fault diagnosis for electric power systems (Mohamed and Rao,1995), transformer protection (Zaman and Rahman, 1998) and generator protection (Megahed and Malik, 1999).However, almost all of these applications in protection merely use the ANN ability of classification, that is, ANNs only output 1 or 0.Various approaches have been published describing applications of ANNs to fault detection and location in transmission lines (Oleskovicz et al., 2001; Purushothama et al., 2001; Osowski and Salat, 2002).

In this project, a single-end fault detector and three fault locators are proposed for on-line applications using ANNs. A feedforward neural network based on the supervised backpropagation learning algorithm was used to implement the fault detector and locators. The neural fault detector and locators were trained and tested with a number of simulation cases by considering various fault conditions (fault types, fault locations, fault resistances and fault inception angles) and various power system data (source capacities, source voltages, source angles, time constants of the sources) in a selected network model.

AUTOMATIC GENERATION CONTROL OF INTERCONNECTED POWER SYSTEM USING ARTIFICAL NEURAL NETWORK TECHNIQUE BASED ON µ–SYNTHESIS

2009-02-13T05:19:00.000-08:00

Automatic Generation Control (AGC) is one of the most important issues in electric power system design and operation. The objective of the AGC in an interconnected power system is to maintain the frequency of each area and to keep tie-line power close to the scheduled values by adjusting the MW outputs the AGC generators so as to accommodate fluctuating load demands. The automatic generation controller design with better performance has received considerable attention during the past years and many control strategies have been developed for AGC problem.

The availability of an accurate model of the system under study plays a crucial role in the development of the most control strategies like optimal control. However, an industrial process, such as a power system, contains different kinds of uncertainties due to changes in system parameters and characteristics, loads variation and errors in the modeling. On the other hand, the operating points of a power system may change very much randomly during a daily cycle. Because of this, a fixed controller based on classical theory is certainly not suitable for AGC problem. Thus, some authors have suggested a variable structure and neural networks methods for dealing with parameter variations. All the proposed methods are based on the state-space approach and require information about the system states which are not usually known or available.

On the other hand, various adaptive techniques have been introduced for AGC controller design. Due to the requirement of a prefect model which has to track the state variables and satisfy system constraints, it is rather difficult to apply these adaptive control techniques to AGC in practical implementations. Recently, several authors have applied robust control methodologies to the solution of AGC problem. Although via these methods, the uncertainties are directly introduced to the synthesis. But models of large scalar power systems have several features that preclude direct application of robust control methodologies. Among these properties, the most prominent are: very high (and unknown) model order, uncertain connection between subsystems, broad parameter variation and elaborate organizational structure.

In this project, because of the inherent nonlinearity ofpower systems we address a new nonlinear Artificial Neural Network (ANN) controller based on µ-synthesis technique. The motivation of using the µ-based robust controller for training the proposed controller is to take the large parametric uncertainties and modeling error into account. To improve the stability of the overall system and also its good dynamic performance achievement, the ANN controller has been reconstructed with applying the µ- based robust controller to power systems in different op- erating points under different load disturbances by using the learning capability of the neural networks. Moreover, the proposed controller also makes use of a piece of information which is not used in the conventional and µ-based robust controllers (an estimate of the electric load perturbation, ie an estimate of the change in electric load when such a change occurs on the bus). The load perturbation estimate could be obtained either by a linear estimator or by a nonlinear neural network estimator in certain situations. It could also be measured directly from the bus.

We will show by simulation that when a load estimator is available, the ANN controller can achieve an extremely dynamic response. In the work, a two-area power system is considered as a test system. Each area of the power system consists of steam turbines, which include reheaters.Therefore, there are the effects of reheaters and generating rate boundaries in each area. For comparison, the considered system is controlled by using:

(i) Conventional integral controller

(ii) ANN controller

for different cases of the plant parameter changes under various step load disturbances. The simulation results show that the proposed controller is very effective and gives a good dynamic response compared to the conventional PI and µ-based robust controllers even in the presence of the plant parameters changes and Generation Rate Constraint (GRC).

WAVELET TRANSFORM FOR TEXTURAL IMAGE CLASSIFICATION

2009-02-11T03:23:00.000-08:00

INTRODUCTION

Texture analysis is still considered an interesting but challenging problem in image processing field. In computer vision, several approaches have been proposed in the past for texture analyse. Recently researchers are motivated by human version system to develop multiresolution space/scale texture models such as Gabor filter and wavelet tansforu. Gabor filters require proper tuning of filter parameters at different scales; their transformations are usually not reversible, and finally, there is a significant correlation between their texture features. Wavelet transform on the other hand, has the ability to perform local analysis fur revealing various aspects of data like trends, breakdown points, discontinuities in higher derivatives, and self-similarities. A major drawback of two-dimensional wavelets is their limited capability in capturing directional information which has a significant role in analysts of the images, including feature extraction and classification.

To overcome this deficiency,a new family of wavelet methods that can capture the intrinsic geometrical structures such as curvelet transform and eontourlet transform. Curvelets are very successful in detecting image activities along curves, while analyzing images at multiple scales, locations, and orientations. Contourlet transform proposed by Do and Vetterli , uses a structure similar to that of curvelets, except at discrete domain. The contourlet expansion is composed of basis images oriented at various directions in multiple scales, with flexible aspect ratios which effectively capture smooth contours of images.

In many remote sensing applications such as aerial or satellite photography, and underwater acoustic imaging systems. textural images that may be acquired from the same scene but with different slope, direction. distance. noise level and illumination, should be classified consistently. It was shown that wavelet transform is suitable for this task. However, computer vision literature has paid less attention to the contourlet domain texture segmentation and classification.

WAVELET TRANSFORM

The transform of a signal is just another form of representing the signal. It does not change the information content present in the signal. The Wavelet Transform provides a time-frequency representation of the signal. It was developed to overcome the short coming of the Short Time Fourier Transform (STFT), which can also be used to analyze non-stationary signals. While STFT gives a constant resolution at all frequencies, the Wavelet Transform uses multi-resolution technique by which different frequencies are analyzed with different resolutions. A wave is an oscillating function of time or space and is periodic. In contrast, wavelets are localized waves. They have their energy concentrated in time or space and are suited to analysis of transient signals. While Fourier Transform and STFT use waves to analyze signals, the Wavelet Transform uses wavelets of finite energy.

The wavelet analysis is done similar to the STFT analysis. The signal to be analyzed is multiplied with a wavelet function just as it is multiplied with a window function in STFT, and then the transform is computed for each segment generated. However, unlike STFT, in Wavelet Transform, the width of the wavelet function changes with each spectral component. The Wavelet Transform, at high frequencies, gives good time resolution and poor frequency resolution, while at low frequencies, the Wavelet Transform gives good frequency resolution and poor time resolution.

COMPARATIVE STUDY BETWEEN WAVELET AND CONTOURLET TRANSFORM FEATURES FOR TEXTURAL IMAGE CLASSIFICATION

2009-02-01T03:12:00.000-08:00

INTRODUCTION

CONTOURLET TRANSFROM

The contourlet transform is a new two-dimensional extension of the wavelet transform proposed by Do and Vetterli using multiscale and directional filter banks. The contourlet expansion is composed of basis images oriented at various directions in multiple scales, with flexible aspect ratio that could effectively capture smooth contours of seabed images. The contourlet transform employs an efficient tree structured Implementation which is an iterated combination of the Laplacian Pyramid (LP) for capturing the point discontinuities and the Directional Filter Bank, to gather the nearby basis functions and link point discontinuities into linear structures. Since the DFB was designed to capture the high frequency directionality of the input image and it. is poor on handling low frequency content., hence the DFB is combined with the LP, where low frequency of the input image is removed before applying DFB.

WAVELET TRANSFORM

CODE SHIFT KEYING IMPULSE MODULATION FOR UWB COMMUNICATIONS

2009-01-23T23:23:00.000-08:00

INTRODUCTION

ULTRA wideband impulse radio (UWB-IR) technology is an attractive choice to support high-rate data communications and low-rate precise location and ranging. Time-hopping M-ary pulse position modulation (TH-MPPM) has been considered as the main modulation format to meet the demand for higher data rates. In the conventional implementation of MPPM, a single pulse is transmitted in one of the fixed M consecutive pulse positions. In a multipath channel, energy collected from consecutive pulse locations may be interfered by a large portion of multipath-delayed received pulses. This may generate noticeable interference components for the M decision variables, and hence may affect the system performance. To reduce the effect of interference components, one approach is to randomize the consecutive pulse transmit locations using M orthogonal TH codes.

With this approach, (i) the separation between consecutive pulse positions can be increased while the data rate is fixed, and (ii) multiple-access capability can still be maintained with the random selection of user specific TH codes. We refer to this new modulation format as M-ary code shift keying (MCSK) impulse modulation. We initially proposed MCSK as a combined modulation with binary PPM (BPPM) in order to increase the data rate of the conventional TH-BPPM. Combined MCSK/BPPM provided improved system performance at higher data rate if the system design parameters were properly selected.

In this project, MCSK impulse modulation is considered by itself, and studied in detail for comparison to TH-MPPM. MCSK is considered here for both single- and multi-user cases. In the study of single-user case, the effect of multipathdelayed pulses on M decision variables is explicitly provided in terms of channel impulse response coefficients. In the study of multi-user case, an accurate semi-analytic symbolerror rate (SER) expression is derived by considering the generalized Gaussian distribution (GGD) presented in for multi-user interference (MUI) modelling.

Some approximations to MUI modelling are provided in the Results Section. These approximations increase the computational efficiency of numerical analysis significantly with respect to simulations, while still providing accurate results. For both single- and multi-user cases, it is shown that MCSK can provide about 2 dB performance gain over MPPM as it reduces the effects of multipath delays on the decision variables by randomizing locations of the transmit pulse. This performance gain is mainly a result of separatedM decision variables experiencing less interference due to the decaying power delay profile.

ROBUST IMAGE WATERMARKING BASED ON MULTIBAND WAVELETS AND EMPIRICAL MODE DECOMPOSITION

2009-01-22T02:07:00.000-08:00

INTRODUCTION

With the rapid development of internet and wireless networks, multimedia security and digital rights management (DRM) are becoming increasingly important issues,.Te watermarking system has been viewed as a possible solution to control unauthorized duplication and redistribution of those multimedia data. Robustness, perceptually invisibility,and security are the basic requirements for a robust watermarking system. Seeking new watermark embedding strategy to achieve performance is a very challenging problem. In this project, a proposed new blind image watermarking scheme, which is based on the multiband wavelet transform and the empirical mode decomposition.

The watermark bits can be embedded either in the spatial domain or in the transform domain, while the latter watermark embedding strategy has been demonstrated to be more robust against most of attacks. We take that latter watermarking embedding strategy in our image watermark embedding scheme, particularly we embed watermark bits indirectly in the multiband wavelet domain with the dilation factor M>2 . For M=2 , there are lots of watermarking schemes available. For instance, Prayoth et al. introduced a semi-blind watermarking scheme based on the two-band multiwavelet transform.Hsieh et al proposed a nonblind watermarking scheme based on the two-band wavelet transform and the qualified significant wavelet tree (QSWT), which is robust to JPEG compression, image cropping, median filter etc., Lahouari et al suggested a watermarking algorithm based on the balanced two-band multiwavelet transform and the well-established perceptual model, which is adaptive and highly robust.

Ng et al put forward a maximum-likelihood detection scheme that is based on modelling the distribution of the image DWT coefficients using a Laplacian probability distribution function. In Bao et al. proposed a watermarking scheme by using a quantization-index-modulation (QIM) process via wavelet domain singular value decomposition (SVD). That scheme is robust against JPEG compression but extremely sensitive to filtering and random noising.

In this project, we use the multiband wavelet domain, instead of the two-band wavelet domain, to embed the watermark bits for the reason that the multiband wavelet domain provides more capacity for watermarking and more flexible tiling of the scale-space plane. Particularly, applying the MWT with the dilation factor an image is decomposed into subimages with narrower frequency bandwidth in different scales and directions. The subimages thus generated with middle frequency are favorable blocks to embed watermark bits in our watermark embedding strategy due to the robustness against JPEG compression and various noise attacks.

For the robustness of an image watermarking system, the watermark bits are usually embedded in the perceptually significant components, mostly the low or middle frequency components of the image . he EMD, first proposed in and later demonstrated to be very useful in many areas , provides a self-adaptive decomposition of a signal, and the mean trend, the coarsest component, of the signal is highly robust under noise attack and JPEG compression. So, we select the mean trend of each subimage in the multiband wavelet domain, instead of the subimage itself, to embed the watermark bits. Our experimental results show that the watermarking based on the MWT and EMD is robust against JPEG compression, Gaussian noise, Salt and Pepper noise, median filtering and ConvFilter (Gaussian filtering and sharpening) attacks.

A NOVEL VESSEL SEGMENTATION ALGORITHM FOR PATHOLOGICAL RETINA IMAGES BASED ON THE DIVERGENCE OF VECTOR FIELDS

2009-01-21T02:20:00.000-08:00

INTRODUCTION

IN retinal images, blood vessels are landmarks for localizing the optic nerve, the fovea and lesions, which are useful for medical diagnosis. However, in these images, many vessels are narrow and close to each other, forming a network-like structure. Also, due to the reflection on the tiny uneven surface of the soft tissue in the image, the low contrast between the vessel and background, and the pathological variations, detecting blood vessels automatically from a retinal image is a challenging problem. A number of techniques have been proposed to solve this problem. They can be classified into unsupervised and supervised methods. In an unsupervised method, a pixel is assigned to a candidate vessel according to several predefined criteria. Chaudhuri et al. propose a matched filter response (MFR) method , which applies rotated Gaussian filters to the image.

If the pixel has a large filtered value, it is a part of a vessel. Jiang and Mojon propose an adaptive thresholding technique for vessel segmentation. The detection is conducted in different levels of image intensities. For example, the pixels with intensity values from 80 to 100 are grouped into one level while the pixels with the intensity values from 110 to 140 are grouped into another level. In each level, candidate vessels are obtained by thresholding. In a supervised method, the criteria are determined by the ground truth data based on given features. However, a prerequisite for a supervised method is the availability of the ground truth data that are already classified, which may not be available in real life applications. An average of 2 h is needed to label a single retinal image. Staal et al. employ more than 10 features, including width of the vessel, intensity, and edge strength.

Soares et al. make use of the Gabor wavelet transform. As supervised methods are designed based on preclassified data, their performance is usually better than that of unsupervised ones and can produce very good results for healthy retinal images.Although existing methods are robust for many retinal images,there is still room for further improvement, especially for pathological retina images. A pathological retina may suffer from a certain disease and there may contain some spots (light or dark). Existing methods may recognize those spots as part of the vessels. Due to the unknown characteristics of a pathological region, widely used features such as intensity are not effective for solving the problem. The supervised method of Soares et al. has the same limitation. In their paper, the authors stated “Though very good ROC results are presented, visual inspection shows some typical difficulties of the method that must be solved by future work.

The major errors are in false detection of noise and other artifacts. False detection occurs in some images for the border of the optic disc, haemorrhages, and other types of pathologies that present strong contrast”.Researchers have made many proposals to analyze pathological retina images. Chanwimaluang et al. suggest that more constraints should be added in order to remove the spots .However, there is no discussion on how we can select the constraints.One of the widely used constraints for noise removal is the split-and-merge system. If the size of an object is small
enough, it will be treated as noise.

An implicit assumption for this pruning operation is that the size of the vessel should be larger than that of noise. However, many blood vessels after splitting are very short and can be removed easily. Staal et al. suggest to solve the problem by removing a pathological region in a preprocessing step or by selecting more training data sets that include pathological features in a supervised approach .The removal of a pathological region in a preprocessing step can be difficult. Actually, as we do not know where a pathological region will be, it is not easy to remove it in advance. One way is to use an adaptive thresholding technique proposed by Jiang and Mojon . They separate the image into several levels by a thresholding method based on pixel intensities.

However, if there are large intensity variations within the spots, this method may not work well. To produce more training data, one possible solution is to trace the vessels from the optic nerve, a user-defined point or a given labeled vessel on the image. However, if the spots are close to the blood vessels, which are not connected to the optic nerve or the given information, it may not be possible to remove them.

A NORMALIZATION FRAMEWORK FOR MULTIMEDIA DATABASES

2009-01-21T02:13:00.000-08:00

INTRODUCTION

IN the last decade, multimedia databases have been used in many application fields. The Internet boom has increased this trend, introducing many new interesting issues related to the storage and management of distributed multimedia data. For these reasons, data models and database management systems (DBMSs) have been extended in order to enable the modeling and management of complex data types, including multimedia data. In particular, other than working on the extension of data models, the research community has focused on indexing techniques enabling content-based retrieval of multimedia information, query paradigms and languages, clustering techniques, and support for distributed multimedia information management.

Examples of DBMSs extended with functionalities to support multimedia data management (MMDBMSs) include CORE , OVID , VODAK , QBIC ,ATLAS , MIRROR , DISIMA, and so forth, each providing enhanced support for one or more media domains among text, sound, image, and video. In the beginning, many DBMS producers relied on the objectoriented data model to face the complexity of modeling multimedia data, but there have also been examples of MMDBMSs based on the relational data model and on specific nonstandard data models. However, in order to facilitate the diffusion of multimedia databases within industrial environments, researchers have been seeking solutions based on the relational data model, possibly associated to some standard design paradigms, like those used with traditional relational DBMSs (RDBMSs).

Extensible RDBMSs have been an attempt in this direction. In the last decade, DBMS vendors have produced extended versions of RDBMSs , with added capabilities to manage complex data types, including multimedia. In particular, these new products extend traditional RDBMSs with mechanisms for implementing the concept of object/relational universal server. In other words, they provide a means to enable the construction of user-defined Data Types (UDTs), and user-defined Functions (UDFs) for manipulating them. New standards for SQL have been created, and SQL3 has become the standard for RDBMSs extended with object-oriented capabilities .

The standard includes UDTs, UDFs, large objects (LOBs; a variant of binary large objects (BLOBs)), and type checking on UDTs, which are accessed through SQL statements. Early examples of extensible RDBMSs include Postgres, IBM/DB2 version 5 , Informix , and ORACLE 8 .As MMDBMSs technology has started becoming more mature, the research community has started focusing on multimedia software engineering issues, with particular emphasis on multimedia databases. In particular, main efforts have been devoted to multimedia data indexing and content-based retrieval, which has led to the development of many data indexing and organization approaches, each specialized on a particular media type, all aiming at guaranteeing an efficient retrieval of multimedia data based on their contents. Thus, we have had many indexing techniques for images and videos: some are based on physical characteristics of media types, and others based on their semantics.

However, in spite of these efforts, little attention has been devoted to multimedia databases and multimedia software engineering methodologies in the direction of providing paradigms for designing information systems capable of processing many different types of multimedia data together with traditional alphanumeric data. In particular, multimedia software engineering methodologies should embed not only data indexing issues but also techniques for database schema design, with guidelines on evaluating their quality and on refactoring them. In this project, we present a generic normalization framework for multimedia databases, providing guidelines and normal forms to evaluate and improve the quality of schemas. The framework applies in a seamless way to images and to other media types.

It is based on a new definition of imprecise dependency for multimedia data, named type-M dependency, which is parameterized upon the distance functions used for comparing multimedia data , and it has been exploited to define five new normal forms. The concept of type-M dependency generalizes similar concepts of imprecise or fuzzy functional dependencies (ffds) existing in the literature, which turned out to be inadequate to capture some important aspects of multimedia data.

COMPUTER-AIDED SHAPE ANALYSIS AND CLASSIFICATION OF WELD DEFECTS IN INDUSTRIAL RADIOGRAPHY BASED INVARIANT ATTRIBUTES AND NEURAL NETWORKS

2009-01-21T02:05:00.000-08:00

Introduction

The welded joints radiogram often contain defects which the interpreter must identify and quantify, before he decides on their acceptability, by referring to non destructive testing standards and codes. Once the radiographic segmentation was accomplished providing a description in term of regions (defect and background), the problem is then to interpret their contents. It is thus question of determining effective attributes which permit to characterize these defect regions and to even recognize them like class elements easily identifiable. In industrial radiography, we can obtain radiograms on which weld defects, if they exist, can have various sizes and orientations.

For an example, a crack is identified as crack whatever its size and its orientation may be, and an inclusion is recognized as being an inclusion in spite of its position and its dimension. A major problem in the recognition of such defects is that these defects can be viewed from several angles and this, according to the orientation and the distance of the irradiated welded joint in regard to the radiation source. To characterize a given weld defect represented by its boundary or its region, the simplest attributes which be computed are the area and the perimeter . The latter cannot be used because of their sensitivity to geometric transformations.

For this reason, we will employ features which are invariant regardless geometric transformations of translation, rotation and scaling. A set of attributes satisfying the above conditions will be proposed in this paper. These geometric invariant attributes will follow from the calculation of geometric parameters (area, perimeter, etc.) and spatial moments. They will be implemented on binarized images issued from real radiographic films of welded joints. The main idea behind the principal component analysis (PCA) is to represent multidimensional data with less number of variables retaining main features of the data. It is inevitable that by reducing dimensionality some features of the data will be lost. It is hoped that these lost features are comparable with the “noise” and they do not tell much about underlying population.

For this purpose, in this work, the principal component analysis technique will be used to reduce the number of the attribute variables. When the expert knowledge is not explicitly defined or cannot be represented in terms of statistically independent rules, artificial neural networks (ANN) may provide a better solution than expert systems, and they can efficiently learn nonlinear mappings through examples contained in a training set, and conduct complex decision making. Then, the ANN can be effectively updated to learn new features. In this project, a feed forward neural network trained by the backpropagation algorithm will be used for the weld defect classification task . This neuronal classification consists in assigning the usual types of weld defects met in practice to four categories according to their morphological characteristics. Other work was the subject of the use of ANN in the radiographic testing area. Authors in and use ANN in the weld defect segmentation in edges and regions respectively. ANNs were also used in the planer and volumetric weld defect classification using Hu’s invariant moments as features .

DETECTION OF DIGITAL FORGERIES USING AN IMAGE INTERPOLATION FROM DIGITAL IMAGES

2009-01-09T00:03:00.000-08:00

INTRODUCTION

A field of detecting digital forgery of digital images taken by a digital camera is one of a new research topic in the area of a digital photography. Recently, digital images or moving pictures can easily be created by image editors. Software tools used generally to image editors are Adobe Photoshop, illustrator, Vegas, and Avid and so on. Digital forgeries are nowadays given prominence to a social problem to happen personal affairs. It is therefore very most important to detect the forgery images used to each filters of Adobe Photoshop.

Our contribution of the developed system is to detect the forgery images in order to protect each person from images used to each filters of Adobe Photoshop.A few works have been published in the domain of digital forgery. A.C Popescu and H. Farid proposed themethods to quantify and detect statistical perturbations found in different types of forgery images.

The first proposed method is to detect re-sampled images, the second is to detect color filter array interpolated images, and the third is to detect duplicated image regions .Unfortunately, these techniques is to detect only the forgery images of the first interpolation and cannot be applied to each filters of Adobe Photoshop.A.C Popescu and H. Farid also describe a technology to detect the forgery images exploiting a lighting correlation of the highlights and shadows from the photographic image.The method should be however not very accurate because of using only the region of the highlight built up by the image in order to estimate the lighting direction and be constrained to only the object of a sphere from the digital image. J.B Lee et al proposed a method for digital forensic using a technique to detect a lighting direction.

The method can however detect only the forgery images of a straight illumination. If the images of diverse direction illumination is used to the tampered images, it is difficult to detect the forgery images. Our approach is that the proposed algorithm is used to the interpolation technique and can detect the forgery images to process the filters of Adobe Photoshop from the digital image taken by the photographic image. This project proposes a new method for detecting digital forgery using an interpolation technique. We present experimental results demonstrate detecting the forgery images used to each filters for a tool of Adobe Photoshop and show a ratio of detecting forgery.

IMPLEMENTATION OF IEEE 802.11A WLAN BASEBAND PROCESSOR

2009-01-01T21:33:00.000-08:00

INTRODUCTION

Due to the low-cost and high-data-rate, the popularity of IEEE 802.11-based Wireless Local Area Networks (WLAN) is growing exponentially. There are three major physical layer standards available in the 802.11 family: the Complementary Code Keying (CCK)-based 802.11b , the Orthogonal Frequency Division Multiplex (OFDM)-based 802.11a , and the OFDM-based 802.11g The 802.11b standard uses the 2.4GHz band and supports data rates of 1, 2, 5.5, and 11 Mbits/s. The 802.11a standard operates in the 5GHz band with possible data rates of 6, 9, 12, 18, 24, 36, 48, and 54 Mbits/s. The 802.11g standard released in 2003 operates in the 2.4GHz band and supports all the data rates defined in the 802.11a and 802.11b standards. For the higher data rates in 802.11a, the 802.11g standard uses the same OFDM technology in 802.11a, while backward compatibility is added to support the lower data rates of 802.11b . To support the high-data-rate requirements in the 802.11a and 802.11g standards, application specific integrated circuits (ASIC) and field programmable gate arrays (FGPA) designs have been used. However, hardware-based implementations often lack of flexibility and the hardware development cycle is onerous. On the other hand, software based implementations enable elegant reuse of silicon area and dramatically reduce time-to-market through software modification, but are typically much slower than hardware implementations based on comparable technologies.

An existing software implementation for a fully-compliant 802.11a full-rate digital baseband transmitter requires the use of a 22- processor array running at a 1.0GHz clock frequency to reach 54Mbits/s performance . Digital signal processors (DSPs) are a special class of processor optimized for signal-processing applications in communication systems. Although DSPs have been used to implement the 802.11a standard , they can only support limited data rates due to the lack of global parallelism found at the application level. Hence, it is still a major challenge to develop a software implementation for the 802.11a standard on a DSP to meet the high-data-date requirements.

In this project, a software-based 802.11a digital baseband transmitter implementation on the TI TMS320C64x DSP. The transmitter can operate over all data rates defined in the 802.11a standard and is compatible with the high-rate portions of the 802.11g standard. Two major optimizations have been introduced to explore the parallelism within and between the individual functions of the transmitter to achieve the high-data-rate requirements: 1) parallelizing the scrambler function and 2) concatenating the FEC encoder, puncturing,and interleaver functions. Experimental results show that the optimized software implementation on a single C64x DSP with a clock frequency of 1.0GHz can operate at a maximum of 136Mbits/s, which is twice as fast as the software implementation in at the same clock frequency.

A HYBRID LARGE VOCABULARY HANDWRITTEN WORD RECOGNITION SYSTEM USING NEURAL NETWORKS WITH HIDDEN MARKOV MODELS

2008-12-31T21:40:00.001-08:00

Introduction

During the last few years, HMMs have become a very popular approach in handwriting recognition. One of the reasons is their higher performance in medium to large vocabulary applications where segmentation recognition methods are used to cope with the difficulties of segmenting words into characters. Segmentation–recognition methods first loosely segment (oversegment) words into graphemes that ideally consist of either characters or parts of characters, and use dynamic programming techniques together with a lexicon to find the definitive segmentation as well as the best word hypotheses. Many systems use HMMs to model sub–word units (characters) and the Viterbi algorithm to find the best match between a sequence of observations and the models.

The Viterbi algorithm is optimal in the sense of maximum likelihood and it looks at the match of the whole sequence of features (observations) before deciding on the most likely state sequence. This is particularly valuable in applications such as handwritten word recognition where an intermediate character may be garbled or lost, but the overall sense of the word may be detectable. On the other hand, the local information is somewhat overlooked. Furthermore, the conditional independence imposed by the Markov Model (each observation is independent of its neighbors) prevents an HMM from taking full advantage of the correlation that exists among the observations of a single character. Neural network classifiers exhibit powerful discriminative properties and they have been used in handwriting recognition particularly with digits, isolated characters, and words in small vocabularies .

However, the use of NNs in the recognition of handwritten words from larger vocabularies depends heavily on a very efficient segmentation scheme. Due to the lack of such an efficient segmentation scheme, NNs are usually employed in combination with other classifiers, e.g. hybrid NN/HMM approaches that use NNs to estimate a priori probabilities, or that use NNs to validate grapheme hypotheses generated by HMM classifiers.

In this project an approach to integrate NNs and HMMs in a probabilistic framework that takes advantage of the good properties of both methods: the generation of an N–best list of word hypotheses by the HMM classifier together with the segmentation of each hypothesis into characters and the character modeling properties of the NN classifier. The NN classifier uses the segmentation information provided by the HMM classifier to go back to the input image and extract new features more suitable for isolated character recognition. The NN classifier scores the segments of each N–best word hypothesis and such scores are further combined with the scores generated by the HMM classifier. Finally, the N–best list is reordered according to the new composite scores, shifting up the correct word hypothesis.

DSP PROJECT DOMAINS

2008-12-27T22:13:00.000-08:00

Verilog Course Team ,now Provides Project Guidance in the foloowing Areas,

MATLAB APPLICATION AREA:

Control system
Digital signal processing
Digital image processing
Communications system
Partial differential equations
MODELSIM link with MATLAB
Optimization process
Fuzzy logic

MATLAB TOOLBOX:

Communication toolbox
Signal processing toolbox
Image processing toolbox
Neural network toolbox
Fuzzy logic toolbox
Filter design toolbox
Bioinformatics Toolbox
Control System Toolbox
Database Toolbox
Genetic Algorithm and Direct Search Toolbox
Statistics Toolbox
Wavelet Toolbox

MATLAB SIMULINK:

Simulink Control Design
Video and Image Processing Blockset
Signal Processing Blockset
Communications Blockset
Aerospace Blockset

Low Complexity Turbo Space-Time Equalization for Broadband MIMO Systems

2008-12-09T08:36:00.000-08:00

INTRODUCTION

In this paper, we consider turbo equalization formultiinput multi-output (MIMO) broadband wireless transmission that is aected by multipath fading. Note that, due to fading eects at high data rates, the wireless link between each transmit/receive antenna pair is represented by a long channel impulse response. This, along with the interference from other users (which can be observed as a noise coloring eect), make impossible the use of conventional trellis-based turbo equalization.This problem is addressed in where compact receive antenna arrays and broadband beamformers (space-time lters) are employed for interference suppression and channel shortening so as to make possible subsequent use of trellis-based turbo equalization. The large computational load of conventional turbo equalizers, even with small channel lengths, makes their use unfeasible for MIMO wireless systems which require a separate turbo-type receiver for each transmit signal.

For this reason an alternative turbo space-time equalization scheme consisting of a broadband beamformer at its frontend and followed by a soft-input soft-output (SISO) decoder at its back-end. In the proposed receiver a soft interference cancellation operation is performed before beamforming using a priori symbol expectations and then the beamformer targeting a particular user employs minimum mean-square error (MMSE) equalization. Moreover each element of the beamformer decision sequence is mapped onto extrinsic symbol probabilities so as to be used by the SISO back-end decoder. Soft-interference cancellation, beamforming and probability mapping al together form a composite SISO module that is suitable for iterative processing.

The design considerations and simulation results for turbo equalization of broadband signals with 8-PSK trellis coded modulation (TCM). Note that our turbo equalizer design is an extension of the linear ltering approach of to high-order signal constellations and diversity receivers within a MIMO transmission context. Because the proposed receiver does not rely on any trellis-search techniques for channel equalization, it oers signicant reduction in computational complexity over conventional iterative approaches using maximum a posteriori (MAP) equalization.

Estimation and Direct Equalization of Doubly Selective Channels

2008-12-09T08:29:00.000-08:00

INTRODUCTION

Over the last decade, the mobile wireless telecommunication industry has undergone tremendous changes and experienced rapid growth. The reason behind this growth is the increasing demand for bandwidth hungry multimedia applications. This demand for even higher data rates at the user’s terminal is expected to continue for the coming years asmore and more applications are emerging. Therefore, current cellular systems have been designed to provide date rates that range from a few megabits per second for stationary or low mobility users to a few hundred kilobits per second for high mobility users. In addition to the frequency-selectivity characteristics caused by multipath propagation, the channel often exhibits time-variant characteristics caused by the user’s mobility.

This results in the so-called doubly selective (timeand frequency-selective) channels. In linear and decision feedback equalizers have been developed for single carrier transmission over doubly selective channels. There, the time-varying channel was approximated using the basis expansion model (BEM). The BEM coefficients are then used to design the equalizer (linear or decision feedback). So far, it was assumed that the BEM coefficients are perfectly known at the receiver, and that they were obtained by a least-squares (LS) fitting to the noiseless underlying communication channel (modeled using Jakes’ model). In other words, perfect channel state information (CSI) was assumed to be known at the receiver side. This is, however, far from being realistic, since a more realistic approach is to estimate the channel or directly obtain the equalizer coefficients.

This can be achieved by using training symbols, or blindly or semiblindly by combining training with blind techniques. In this paper we will focus on pilot-symbol-assisted-modulation- (PSAM-) based, blind, and semiblind techniques for channel estimation and direct equalization of rapidly time-varying channels. PSAM techniques rely on time multiplexing data symbols and known pilot symbols at known positions, which the receiver utilizes to either estimate the channel or obtain the equalizer coefficients directly. In this context, we first derive the optimal minimum mean-squared error (MMSE) interpolation filter. Then we derive the conventional BEM channel

estimation technique based on LS fitting. While the MMSE interpolation filter requires the channel statistics, the latter does not require a priori knowledge of the channel statistics. It was shown that the modeling error between the true channel and the BEM channel model is quite large for the case when the BEM period equals the time window. This case corresponds to a critical sampling of the Doppler spectrum.

Reducing this modeling error can be achieved by setting the BEM period equal to a multiple of the time window. In other words, we can reduce the modeling error by oversampling the Doppler spectrum. In the authors treated the first case ignoring the modeling error. However, when BEM oversampling is used, LS fitting of the BEM channel based on pilot symbols only is sensitive to noise. Here, we show that robust-PSAM-based channel estimation can be obtained by combining the optimal-MMSE-interpolation based channel estimation with the LS fitting of the BEM.Although this can be applied to the critically sampled case as well as to the oversampled case with oversampling factor greater than one, little gain is obtained for the critically sampled case. In addition, we show that the channel estimation step can be skipped and obtain the equalizer coef ficients directly based on the pilot symbols. This is referred to as PSAM-based direct equalization. The training overhead imposed on the system can be completely eliminated by using blind techniques for channel estimation and direct equalization.

Due to the poor performance of blind techniques and their high implementation complexity, better performance and reduced complexity semiblind techniques can be obtained. Semiblind techniques are obtained by combining blind techniques with training.For our blind techniques we focus on deterministic approaches. For time-invariant (TI) channels, a least-squaresbased deterministic channel estimation method is discussed , and deterministic mutually referenced equalization is proposed .

Subspace-based methods have also been proposed for channel identification/equalization for TI channels. For doubly selective channels, deterministic blind identification/equalization techniques are proposed where for a zero-forcing (ZF) FIR solution to exist, the number of subchannels (receive antennas) is required to be greater than the number of basis functions used for BEM channel modeling. In blind techniques based on linear prediction are proposed for doubly selective channels, where second-order statistics of the data are used. However, these techniques also require the number of receive antennas to be greater than the number of basis functions of the BEM channel. However, we propose an approach for which the ZF solution already exists when only two subchannels (receive antennas) are used.

Vibration Rejection using Notch Filter in Servo Drive System

2008-12-05T06:23:00.000-08:00

INTRODUCTION

The mechanical systems have vibration in torque transmission from servo motor to mechanical load due to the mechanical resonance. This vibration makes it difficult to achieve quick speed responses and may result in damage to the mechanical plant. In the Computerized Numerical Control (CNC) and other dedicated machines, used in industrial applications like robots and factory automation, require more precision speed control.

A filter has to be added to servo drive system to avoid the above said problems. The primary problem of low-pass filters is that they introduce phase lag in the loop, reducing the phase margin at the gain crossover. If the cut-off frequency of low pass filter is lower than the mechanical resonance frequency, we can avoid the mechanical vibration from resonance. But, the output control signal is reduced by the low pass filter; the fast dynamic response can not be implemented

This project is selected for elimination/reduction of resonance vibration in the servo drive system and it can be achieved by adding the notch filter in closed loop with PID controller. The resonance frequency is calculated by performing a fast Fourier transform on the motor speed error signal. The advantage of using FFT method is its fastness in achieving the response with greater precision.

In this project, the simulation model is developed with the MATLAB SIMULINK tool.

Performance Analysis of Iterative Channel Estimation and Multiuser Detection in Multipath DS-CDMA Channels

2008-12-02T05:30:00.000-08:00

DIRECT sequence code-division multiple-access (DSCDMA) has been selected as the fundamental signaling technique for third generation (3G) wireless communication systems, due to its advantages of soft user capacity limit and inherent frequency diversity. However, it suffers from multiple- access interference (MAI) caused by the nonorthogonality of spreading codes, particularly for heavily loaded systems. Therefore, techniques for mitigating the MAI, namely multiuser detection, have been the subject of an intensive research effort over the past two decades. It is well known that multiuser detection can substantially suppress MAI, thus improving system performance.

Maximum likelihood (ML) multiuser detection was proposed in the early 1980s, and achieves the optimal performance at the cost of prohibitive computational cost when the number of users is large. For practical implementation, suboptimal algorithms, such as the linear minimum mean square error (LMMSE) detector or decorrelator, allow a tradeoff between complexity and performance. It should be noted that, with the development of interference cancellation (IC) techniques, multiuser detection is being applied in practical systems, such as the EV-DO Revision A systems in recent years, the turbo principle, namely the iterative exchange of soft information among different blocks in a communication system to improve the system performance, has been applied to combine multiuser detection with channel decoding. In such turbo multiuser detectors, the outputs of channel decoders are fed back to the multiuser detector, thus enhancing the performance iteratively.

Turbo multiuser detection based on the maximum a posteriori probability (MAP) detection and decoding criterion has been proposed in together with a lower complexity technique based on interference cancellation and LMMSE filtering. Further simplification is obtained by applying parallel interference cancellation (PIC) for multiuser detection, where the decisions of the decoders are directly subtracted from the original signal to cancel the MAI.

An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks

2008-12-02T05:27:00.000-08:00

Data mining is generally part of a larger business intelligence or knowledge management initiative. Since state governments are complex organizations that collect and process massive amounts of information, data mining can help provide value to state government operations and taxpayers by extracting useful information out of mountains of collected data. In addition, data mining can be predictive and uncover hidden patterns that states can strategically use to reduce costs, increase business expansion opportunities, and detect fraud, waste and abuse that drains away taxpayer dollars.

Data mining is the process of sorting through large amounts of data and picking out relevant information. It is normally used by large corporations employing Business Intelligence integrated with an ERP system to help make managerial decisions based on the patterns and forecasts generated from the data collected. It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases."

Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making. Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of business processes and target opportunities. However, abdicating control of this process from the statistician to the machine may result in false-positives or no useful results at all. Although data mining is a relatively new term, the technology is not. For many years, businesses have used powerful computers to sift through volumes of data such as supermarket scanner data to produce market research reports (although reporting is not always considered to be data mining). Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of data analysis.

The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user (e.g., association rule mining). Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g., rule-based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery. Metadata, or data about a given data set, are often expressed in a condensed data-minable format, or one that facilitates the practice of data mining.

Data mining relies on the use of real world data. These data are extremely vulnerable to collinearity precisely because data from the real world may have unknown interrelations. An unavoidable weakness of data mining is that the critical data that may expose any relationship might have never been observed. Alternative approaches using an experiment-based approach such as Choice Modelling for human-generated data may be used. Inherent correlations are either controlled for or removed altogether through the construction of an experimental design.

Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases.

This includes:

• operational or transactional data such as, sales, cost, inventory, payroll, and accounting

• nonoperational data, such as industry sales, forecast data, and macro economic data

• meta data — data about the data itself, such as logical database design or data dictionary definitions

The purpose of data mining is to identify patterns in order to make predictions from information contained in databases. It allows the user to be proactive in identifying and predicting trends with that information. Common uses of data mining in government include knowledge discovery, fraud detection, analysis of research, decision support, and website personalization.

• Improving service or performance

• Detecting fraud, waste, and abuse

• Analyzing scientific and research information

• Managing human resources

• Detecting criminal activities or patterns

• Analyzing intelligence and detecting terrorist activities

Data Mining Algorithms

The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set of data and looks for specific patterns and trends. The algorithm uses the results of this analysis to define the parameters of the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics.

The mining model that an algorithm creates can take various forms, including:

• A set of rules that describe how products are grouped together in a transaction.

• A decision tree that predicts whether a particular customer will buy a product.

• A mathematical model that forecasts sales.

• A set of clusters that describe how the cases in a dataset are related.

The problem of mining sensors association rules is inspired by the definition of the association rules proposed in the domain of transactional databases. However, there is not much work done

on the way to define association rules for wireless sensor networks in which the sensors themselves are the main object in the extracted rules.

VECTOR QUANTIZATION USING NEURAL NETWORKS

2008-12-01T12:45:00.000-08:00

Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms.

The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensioned data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. It can also be used for lossy data correction and density estimation. Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model.

A simple training algorithm for vector quantization is:

1. Pick a sample point at random

2. Move the nearest quantization vector centroid towards this sample point, by a small fraction of the distance

3. Repeat

A more sophisticated algorithm reduces the bias in the density matching estimation, and ensures that all points are used, by including an extra sensitivity parameter:

1. Increase each centroid's sensitivity by a small amount

2. Pick a sample point at random

3. Find the quantization vector centroid with the smallest

1. Move the chosen centroid toward the sample point by a small fraction of the distance

2. Set the chosen centroid's sensitivity to zero

4. Repeat

The algorithm can be iteratively updated with 'live' data, rather than by picking random points from a data set, but this will introduce some bias if the data is temporally correlated over many samples.

APPLICATIONS:

Vector quantization is used for lossy data compression, lossy data correction and density estimation. Lossy data correction, or prediction, is used to recover data missing from some dimensions. It is done by finding the nearest group with the data dimensions available, then predicting the result based on the values for the missing dimensions, assuming that they will have the same value as the group's centroid. For density estimation, the area/volume that is closer to a particular centroid than to any other is inversely proportional to the density

THE ADAPTIVE EQUALIZER

2008-12-01T12:44:00.000-08:00

The task of a receiver is to retrieve the information send by the transmitter. In order to do that it has to determine what has been send. To accomplish this task, it tries to extract from the receive signal the parameters related to the transmitted information. However there are some parameters that are not the actual part of the transmit data, but are included by the medium which is used for transmission of the signal.

One of such parameters is the channel. The transmit signal passes through the channel before reaching the receiver, or in other words the transmit signal convolves with the channel. This convolution bring distortion in the transmit signal called the inter-symbol interference. The effect of the channel can be nullified by a de-convolution operation. A simple deconvolution can be realized in Fourier domain.

But, the problem here is that neither the transmit signal nor the channel are known. Therefore, a training signal is required which is known at the receiver. But, the problem with the Fourier domain approach is that it is for deterministic signal and deterministic system, while the input signal here is stochastic in nature plus the channel is time varying, and there is also channel noise. The extraction of channel parameters from the retrieved signal can therefore be done using Wiener solution.

STEGANOGRAPHY

2008-12-01T12:42:00.000-08:00

Steganography, coming from the Greek words stegos, meaning roof or covered and graphia which means writing, is the art and science of hiding the fact that communication is taking place. Using steganography, you can embed a secret message inside a piece of unsuspicious information and send it without anyone knowing of the existence of the secret message. Steganography and cryptography are closely related.

Cryptography scrambles messages so they cannot be understood. Steganography on the other hand, will hide the message so there is no knowledge of the existence of the message in the first place. In some situations, sending an encrypted message will arouse suspicion while an ”invisible” message will not do so. Both sciences can be combined to produce better protection of the message. In this case, when the steganography fails and the message can be detected, it is still of no use as it is encrypted using cryptography techniques.

New technology

Steganographic techniques have been used for centuries. A different method from that time used wax tables as a cover source. Text was written on the underlying wood and the message was covered with a new wax layer. The tablets appeared to be blank so they passed inspection without question.

Uses of steganography

With steganography you can send messages without anyone having knowledge of the existence of the communication. There are many countries where it is not possible to speak as freely as it is in some more democratic countries. Steganography can be a solution which makes it possible to send news and information without being censored and without the fear of the messages being intercepted and traced back to you. While sending messages can be useful, it is also possible to simply use steganography to store information on a location. For example, several information sources like your private banking information, some military secrets and your mothers special pancake recipe, can be stored in a cover source. When you are required to unhide the secret information in your cover source, you can easily reveal your banking data and the recipe and it will be impossible to prove the existence of the military secrets inside. Steganography can offer deniable storage of information.

The offers an implementation of this principle. Because you can hide information without the cover source changing, steganography can also be used to implement watermarking. Although the concept of watermarking is not necessarily steganography, there are several steganographic techniques that are being used to store watermarks in data. The main difference is on intent, while the purpose of steganography is hiding information, watermarking is merely extending the cover source with extra information. Since people will not accept noticeable changes in images, audio or video files because of a watermark, steganographic methods can be used to hide this.

Implementing steganography

Secrets can be hidden inside all sorts of cover information: text, images, audio, video and more. Most steganographic utilities nowadays, hide information inside images, as this is relatively easy to implement. However, there are tools available to store secrets inside almost any type of cover source. It is also possible to hide information inside texts, sounds and video films for example. The most important property of a cover source is the amount of data that can be stored inside it, without changing the noticeable properties of the cover. When an image is distorted or a piece of music sounds different than the original, the cover source will be suspicious and may be checked more thoroughly.

SPREAD SPECTRUM METHOD USED FOR DYNAMIC CODE ACQUISITION

2008-12-01T12:41:00.000-08:00

Spread-spectrum techniques are methods by which energy generated in a particular bandwidth is deliberately spread in the frequency domain, resulting in a signal with a wider bandwidth. These techniques are used for a variety of reasons, including the establishment of secure communications, increasing resistance to natural interference and jamming, and to prevent detection. Spread Spectrum techniques have some powerful properties which make them an excellent candidate for networking applications. There are a number of good practical reasons why spread spectrum modulation is technically superior to the intuitively more obvious techniques such as AM and FM, and all of the hybrids which lie in between.

• The Ability to Selectively Address. Spread the signal, and use the proper encoding method, then the signal can only be decoded by a receiver which knows the transmitter's code. Therefore by setting the transmitter's code, target a specific receiver in a group, or vice versa. This is termed Code Division Multiple Access.

• Bandwidth Sharing. It is entirely feasible to have multiple pairs of receivers and transmitters occupying the same bandwidth. This would be equivalent to having say ten TV channels all operating at the same frequency. In a world where the radio spectrum is being busily carved up for commercial broadcast users, the ability to share bandwidth is a valuable capability.

• Security from Eavesdropping. If an eavesdropper does not know the modulation code of a spread spectrum transmission, all the eavesdropper will see is random electrical noise rather than something to eavesdrop. If done properly, this can provide almost perfect immunity to interception.

• Immunity to Interference. If an external radio signal interferes with a spread spectrum transmission, it will be rejected by the demodulation mechanism in a fashion similar to noise. Therefore we return to the starting point of this discussion, which is that spread spectrum methods can provide excellent error rates even with very faint signals.

• Difficulty in Detection. Because a spread spectrum link puts out much less power per bandwidth than a conventional radio link, having spread it over a wider bandwidth, and a knowledge of the link's code is required to demodulate it, spread spectrum signals are extremely difficult to detect. This means that they can coexist with other more conventional signals without causing catastrophic interference to narrowband links.

SPEECH CODING WITH LINEAR PREDICTIVE CODING(LPC)

2008-12-01T12:40:00.000-08:00

Wideband speech signals of 2 males and 2 females were coded using an improved version of Linear Predictive Coding (LPC). The sampling frequency was at 16 kHz and the bit rate was at 15450 bits per second, where the original bit rate was at 128000 bits per second. The tradeoffs between the bit rate, end-to-end delay, speech quality and complexity were analyzed. Simulations as well as Segmental SNR evaluations were done to analyze the performance of the implemented algorithm.

Speech coding has been and still is a major issue in the area of digital speech processing. Speech coding is the act of transforming the speech signal at hand, to a more compact form, which can then be transmitted with a considerably smaller memory. The motivation behind this is the fact that access to unlimited amount of bandwidth is not possible. Therefore, there is a need to code and compress speech signals. Speech compression is required in long-distance communication, high-quality speech storage, and message encryption.

For example, in digital cellular technology many users need to share the same frequency bandwidth. Utilizing speech compression makes it possible for more users to share the available system. Another example where speech compression is needed is in digital voice storage. For a fixed amount of available memory, compression makes it possible to store longer messages.

Speech coding is a lossy type of coding, which means that the output signal does not exactly sound like the input. The input and the output signal could be distinguished to be different. Coding of audio however, is a different kind of problem than speech coding. Audio coding tries to code the audio in a perceptually lossless way. This means that even though the input and output signals are not mathematically equivalent, the sound at the output is the same as the input. This type of coding is used in applications for audio storage, broadcasting, and Internet streaming .

SPEAKER RECOGNITION SYSTEM

2008-12-01T12:39:00.000-08:00

Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers. Speaker recognition can be classified into identification and verification.

Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker verification, on the other hand, is the process of accepting or rejecting the identity claim of a speaker. Speaker recognition methods can also be divided into text-independent and textdependent methods.

In a text-independent system, speaker models capture characteristics of somebody’s speech which show up irrespective of what one is saying. In a text-dependent system, on the other hand, the recognition of the speaker’s identity is based on his or her speaking one or more specific phrases, like passwords, card numbers, PIN codes, etc. All technologies of speaker recognition, identification and verification, textindependent and text-dependent, each has its own advantages and disadvantages and may requires different treatments and techniques.

The choice of which technology to use is application-specific. The system that we will develop is classified as textindependent speaker identification system since its task is to identify the person who speaks regardless of what is saying. At the highest level, all speaker recognition systems contain two main modules: feature extraction and feature matching.

Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers.

IMPLEMENTATION OF SINGLE PHOTON QUANTUM CRYPTOGRAPHY IN COMMUNICATION

2008-12-01T12:36:00.000-08:00

Need of Number Plate Recognition System

This system is advanced in surveillance of cars in parking and at toll Plazas. this will automatically generate the number of a Vehicle and that can be used on the bills or may be to monitor the usage of the parking lot by a car. This system can be extremely useful for gathering statistics on road or at a check point for custom checking or to recognize a stolen vehicle. This system takes a vehicle image of any size breaks it into smaller image pieces. These pieces are then analyzed to locate the exact location of number plate in the image. Once the area of the number plate (its x and y coordinates) is found the plate is parsed to extract the character from it. These characters are then given to the OCR module. OCR program recognizes those characters and converts them in text format.

Components of the system

Vehicle number plate recognition system has three main components in it.

1. Breaking the image into smaller pieces of images which are the high frequency parts of the original image.

2. Choosing the number plate from the image pieces returned by the above module, and parsing the plate to extract out the character part.

3. Recognizing the characters in the image pieces.

TECHNIQUE USED

Signature technique is used for the implementation of this project. Taking row wise or column wise signature of an image gives the information about the less detail and more detail areas of the image. So it becomes easy to find out the areas with high frequencies.

How Signature is Used

Signature technique helps in locating high frequency areas. If the image is binarised then most of the detail is lost from the image,leaving our area of interest more prominent.

Finding Probable Number Plate In The Image

Once the image is binarised its row wise histogram (sum of white or black pixels in each row) or signature is taken to find out which number of rows is showing ridges. These ridges are basically high frequency areas and one of these ridges will definitely be a number plate. A threshold value is used to indicate the starting and ending point of the ridge. The best results were shown by taking the average of the minimum point and the median of the row signature as the threshold. Once the ridges in the row signature of the image are obtained column wise signature of those row ridges is calculated. This will further refine the candidate image by removing those columns from the row ridge which do not possess much detail. This is done by choosing the ridges our of the column histogram of the ridge area in the row histogram.

Recognizing Number Plate From The Candidate Images

After this task, the x and y coordinates of all the high frequency pieces which are the candidates of number plate are known. As we can see that on the number plate there would be 4 to seven characters. So each character will show a ridge in the row signature of the image piece, secondly most of the information is lost because of binarising the image so only number plate area will show maximum number of ridges. Now if we take the row wise histogram of those binarised pieces we can see that number plate image shows more number of ridges as compared to any other candidate image. So image with maximum number of ridges in its row signature is chosen as the number plate.Then the same signature technique is applied to extract the numbers from the number plate image. The difference was in the threshold value. Because here we needed to pick each ridge in the histogram therefore the minimum value of the histogram was chosen as the thresholding value. And the reason is that all characters might not show ridges with equal peak (highest point in the ridge). Or a character like ‘X’ might be broken into two ridges. As it is obvious that the center of the character X will show very small peak.

OPTICAL CHARACTER RECOGNIZER - OCR

What is OCR?

The goal of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a digital image) corresponding to alphanumeric or other characters. The process of OCR involves several steps including segmentation, feature extraction, and classification.

Character Recognition: OCR By 2D Correlation

Description of method: Given the chosen license plate and the coordinates that indicate where the characters are, we begin the OCR process by 2D correlation. Correlate each character with either the alphabet or the numeral templates then choose the value of each character based on the result of the correlation. The first three characters on the standard License Plates are alphabets; therefore, correlate each one of them with the 26 alphabet templates. The latter four characters on the standard License Plates are numerals; therefore, we correlate each one of them with the 10 numeral templates. The result OCR is chosen based on the maximum values of the correlation for each character.

However, realized that certain characters are frequently confused. As a quick solution, we implanted a scheme to display possible alternatives. Those characters that are identified as easily misinterpreted are subjected to a correlation comparison with an array of other characters that is historically known to be easily confused with the originals. If there is a possibility that a letter or number can be confused with more than one character, the characters are listed in the output in the order of decreasing likelihood. This likelihood is based on the correlation values of the character with the various templates, where high correlation denotes a good possibility. However, this scheme only occurs if the given letter does not have a very high correlation value (does not land above a nominal threshold).

Rationale

OCR by 2D correlation is the option that seems to strike the best balance between performance and difficulty in implementation. Details can be observed in the character isolation portion of the source code.

Possible problems/Weaknesses

OCR by 2D correlation is sensitive to the size of the license plate, which meant bigger or smaller alphabets and numbers in the picture. The 2-d correlation was very sensitive to this and frequently gave back wrong results due to different size license plates.

Template matching

The steps for this process are:

1. Build a template for each of the letters to be recognized. A good first approximation for a template is to the intersection of all instances of that letter in the number plate. However, more fine-tuning of this template must be done for good performance.

2. Erode the original image using this template as structuring element. All 1 pixels in the resulting image correspond to all matches found for the given template.

3. Find the objects in the original image corresponding to these 1 pixels (e.g., using4 the function 'bwselect' in MATLAB). Another way of doing this is to implement Step 3 as a closing operation.

Advantages.

• Translation-invariant

• Simple and easy to implement

Disadvantages

• Requires considerable tweaking to find the right templates

• Not scale or rotation invariant

• Performance deteriorates rapidly for incomplete or noisy data.

IMAGE COMPRESSION USING JPEG STANDARD

2008-12-01T12:34:00.000-08:00

JPEG is a commonly used method of compression for photographic images. The degree of compression can be adjusted, allowing a selectable tradeoff between storage size and image quality. JPEG typically achieves 10 to 1 compression with little perceivable loss in image quality. In addition to being a compression method, JPEG is often considered to be a file format. JPEG/Exif is the most common image format used by digital cameras and other photographic image capture devices; along with JPEG/JFIF, it is the most common format for storing and transmitting photographic images on the World Wide Web. These format variations are often not distinguished, and are simply called JPEG. The compression method is usually lossy compression, meaning that some visual quality is lost in the process and cannot be restored. There are variations on the standard baseline JPEG that are lossless; however, these are not widely supported. There is also an interlaced "Progressive JPEG" format, in which data is compressed in multiple passes of progressively higher detail. This is ideal for large images that will be displayed while downloading over a slow connection, allowing a reasonable preview after receiving only a portion of the data. However, progressive JPEGs are not as widely supported, and even some software which does support them only displays the image once it has been completely downloaded. There are also many medical imaging systems that create and process 12-bit JPEG images. The 12-bit JPEG format has been part of the JPEG specification for some time, but again, this format is not as widely supported.

LOSSLESS EDITING

A number of alterations to a JPEG image can be performed losslessly (that is, without recompression and the associated quality loss) as long as the image size is a multiple 1 MCU block (Minimum Coded Unit).

Blocks can be rotated in 90 degree increments, flipped in the horizontal, vertical and diagonal axes and moved about in the image. Not all blocks from the original image need to be used in the modified one.

The top and left of a JPEG image must lie on a block boundary, but the bottom and right need not do so. This limits the possible lossless crop operations, and also what flips and rotates can be performed on an image whose edges do not lie on a block boundary for all channels.

When using lossless cropping, if the bottom or right side of the crop region is not on a block boundary then the rest of the data from the partially used blocks will still be present in the cropped file and can be recovered relatively easily by anyone with a hex editor and an understanding of the format.

It is also possible to transform between baseline and progressive formats without any loss of quality, since the only difference is the order in which the coefficients are placed in the file.

JPEG CODEC EXAMPLE

Although a JPEG file can be encoded in various ways, most commonly it is done with JFIF encoding. The encoding process consists of several steps:

1. The representation of the colors in the image is converted from RGB to YCbCr, consisting of one luma component (Y), representing brightness, and two chroma components, (Cb and Cr), representing color. This step is sometimes skipped.

2. The resolution of the chroma data is reduced, usually by a factor of 2. This reflects the fact that the eye is less sensitive to fine color details than to fine brightness details.

3. The image is split into blocks of 8×8 pixels, and for each block, each of the Y, Cb, and Cr data undergoes a discrete cosine transform (DCT). A DCT is similar to a Fourier transform in the sense that it produces a kind of spatial frequency spectrum.

4. The amplitudes of the frequency components are quantized. Human vision is much more sensitive to small variations in color or brightness over large areas than to the strength of high-frequency brightness variations. Therefore, the magnitudes of the high-frequency components are stored with a lower accuracy than the low-frequency components. The quality setting of the encoder (for example 50 or 95 on a scale of 0–100 in the Independent JPEG Group's library affects to what extent the resolution of each frequency component is reduced. If an excessively low quality setting is used, the high-frequency components are discarded altogether.

5. The resulting data for all 8×8 blocks is further compressed with a loss-less algorithm, a variant of Huffman encoding.

IMPLEMENTATION OF SINGLE PHOTON QUANTUM CRYPTOGRAPHY IN COMMUNICATION

2008-12-01T12:32:00.000-08:00

Quantum cryptography, or quantum key distribution (QKD), uses quantum mechanics to guarantee secure communication. It enables two parties to produce a shared random bit string known only to them, which can be used as a key to encrypt and decrypt messages. An important and unique property of quantum cryptography is the ability of the two communicating users to detect the presence of any third party trying to gain knowledge of the key.

This results from a fundamental part of quantum mechanics: the process of measuring a quantum system in general disturbs the system. A third party trying to eavesdrop on the key must in some way measure it, thus introducing detectable anomalies. By using quantum superpositions or quantum entanglement and transmitting information in quantum states, a communication system can be implemented which detects eavesdropping. If the level of eavesdropping is below a certain threshold a key can be produced which is guaranteed as secure (i.e. the eavesdropper has no information about), otherwise no secure key is possible and communication is aborted.

The security of quantum cryptography relies on the foundations of quantum mechanics, in contrast to traditional public key cryptography which relies on the computational difficulty of certain mathematical functions, and cannot provide any indication of eavesdropping or guarantee of key security.

Quantum cryptography is only used to produce and distribute a key, not to transmit any message data. This key can then be used with any chosen encryption algorithm to encrypt (and decrypt) a message, which can then be transmitted over a standard communication channel. The algorithm most commonly associated with QKD is the one-time pad, as it is provably secure when used with a secret, random key

IMPLEMENTATION OF EDGE DETECTION METHOD

2008-12-01T12:30:00.000-08:00

Edge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.

The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. It can be shown that under rather general assumptions for an image formation model, discontinuities in image brightness are likely to correspond to:

• discontinuities in depth,

• discontinuities in surface orientation,

• changes in material properties and

• variations in scene illumination.

In the ideal case, the result of applying an edge detector to an image may lead to a set of connected curves that indicate the boundaries of objects, the boundaries of surface markings as well curves that correspond to discontinuities in surface orientation. Thus, applying an edge detector to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image. If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may therefore be substantially simplified.

Unfortunately, however, it is not always possible to obtain such ideal edges from real life images of moderate complexity. Edges extracted from non-trivial images are often hampered by fragmentation, meaning that the edge curves are not connected, missing edge segments as well as false edges not corresponding to interesting phenomena in the image -- thus complicating the subsequent task of interpreting the image data.

The edges extracted from a two-dimensional image of a three-dimensional scene can be classified as either viewpoint dependent or viewpoint independent. A viewpoint independent edge typically reflects inherent properties of the three-dimensional objects, such as surface markings and surface shape.

A viewpoint dependent edge may change as the viewpoint changes, and typically reflects the geometry of the scene, such as objects occluding one another.

A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line.

Edges play quite an important role in many applications of image processing, in particular for machine vision systems that analyze scenes of man-made objects under controlled illumination conditions. During recent years, however, substantial (and successful) research has also been made on computer vision methods that do not explicitly rely on edge detection as a pre-processing step.

Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:

• focal blur caused by a finite depth-of-field and finite point spread function.

• penumbral blur caused by shadows created by light sources of non-zero radius.

• shading at a smooth object edge.

• local specularities or interreflections in the vicinity of object edges.

Although the following model does not capture the full variability of real-life edges, the error function has been used by a number of researchers as the simplest extension of the ideal step edge model for modeling the effects of edge blur in practical applications (Zhang and Bergholm 1997, Lindeberg 1998).

IMAGE MOSAICING

2008-12-01T12:27:00.000-08:00

INTRODUCTION

Many problems require finding the coordinate transformation between two images of the same scene or object. One of them is Image Mosaicing. It is important to have a precise description of the coordinate transformation between a pair of images. Image mosaics are collection of overlapping images together with coordinate transformations that relate the different image coordinate systems. By applying the appropriate transformations via a warping operation and merging the overlapping regions of a warped images, it is possible to construct a single image covering the entire visible area of the scene. This merged single image is the motivation for the term ``mosaic''.

Image mosaics allow one to compensate for differences in viewing geometry. Thus they can be used to simplify version tasks by simulating the condition in which the scene is viewed from a fixed position with single camera. Mosaic are therefore quite useful in tasks involving motion or change detection or determining the relative pose of the new images that are acquired. They can be used to determine what parts of the scene visible from that point have been observed. There are lots of paper about motion parameter estimation about which can be used in image mosaicing.

A coordinate transformation maps the image coordinatesx=[x,y]T, to new set of coordinatesx'=[x',y']T .

The most common assumption especially in motion estimation for coding and optical flow, is that the coordinate transformation between frames is only translation. Although it is easy to implement, it is very poor to handle large changes due to camera rotation, panning and tilting. The other technique is Affine Model which contains translation, rotation and scale. However, the affine model can not capture camera pan and tilt and therefore cannot accurately express the seen that we see in the world. 8-parameter projective model gives the exact eight desired parameters to account for all the possible camera motions.

However, its parameters have traditionally mathematically and computationally too hard to find. Going from first order to second order, gives the 12-parameter biquadratic model. Increasing the order and number of parameters doesn't help us too much, because the physical camera model fits exactly 8-parameter projective model. Therefore, biquadratic model is not suitable for our purposes. The 8-parameter bilinear model is the most widely-used in the field of image processing.

IMAGE COMPRESSION USING WAVELET TRANSFORM

2008-12-01T12:26:00.000-08:00

Fourier Analysis

Signal analysts already have at their disposal an impressive arsenal of tools. Perhaps the most well-known of these is Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies. Another way to think of Fourier analysis is as a mathematical technique for transforming the signal from time-based to frequency-based.

For many signals, Fourier analysis is extremely useful because the signal’s frequency content is of great importance. Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost. When looking at a Fourier transform of a signal, it is impossible to tell when a particular event took place. If the signal properties do not change much over time that is, if it is what is called a stationary signal—this drawback isn’t very important. However, most interesting signals contain numerous nonstationary or transitory Characteristics: drift, trends, abrupt changes, and beginnings and ends of events. These characteristics are often the most important part of the signal, and Fourier analysis is not suited to detecting them.

Short-Time Fourier Analysis

The Fourier transform to analyze only a small section of the signal at a time a technique called windowing the signal. The Short-Time Fourier Transform (STFT), maps a signal into a two-dimensional function of time and frequency.

The STFT represents a sort of compromise between the time- and frequency-based views of a signal. It provides some information about both when and at what frequencies a signal event occurs. However, you can only obtain this information with limited precision, and that precision is determined by the size of the window. While the STFT compromise between time and frequency information can be useful, the drawback is that once you choose a particular size for the time window, that window is the same for all frequencies. Many signals require a more flexible approach—one where we can vary the window size to determine more accurately either time or frequency.

IMAGE COMPRESSION USING THE DISCRETE COSINE TRANSFORM

2008-12-01T12:25:00.000-08:00

The discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency components. It is widely used in image compression. Here we develop some simple functions to compute the DCT and to compress images.

These functions illustrate the power of Mathematica in the prototyping of image processing algorithms. The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and high-definition television (HDTV) has increased the need for effective and standardized image compression techniques.

Among the emerging standards are JPEG, for compression of still images, MPEG, for compression of motion video, and CCITT H.261 (also known as Px64), for compression of video telephony and teleconferencing. All three of these standards employ a basic technique known as the discrete cosine transform (DCT).

The DCT is a close relative of the discrete Fourier transform (DFT). Its application to will develop some simple functions to compute the DCT and show how it is used for image compression. We have used these functions to explore methods of optimizing image compression for the human viewer, using information about the human visual system .

HISTOGRAM EQUALIZATION

2008-12-01T12:24:00.000-08:00

Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. This method usually increases the local contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast without affecting the global contrast. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values.

The method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of bone structure in x-ray images, and to better detail in photographs that are over or under-exposed. A key advantage of the method is that it is a fairly straightforward technique and an invertible operator. If the histogram equalization function is known, then the original histogram can be recovered. The calculation is not computationally intensive. A disadvantage of the method is that it is indiscriminate. It may increase the contrast of background noise, while decreasing the usable signal.

In scientific imaging where spatial correlation is more important than intensity of signal (such as separating DNA fragments of quantized length), the small signal to noise ratio usually hampers visual detection. Histogram equalization provides better detect ability of fragment size distributions, with savings in DNA replication, toxic fluorescent markers and strong UV source requirements, whilst improving chemical and radiation risks in laboratory settings, and even allowing the use of otherwise unavailable techniques for reclaiming those DNA fragments unaltered by the partial fluorescent marking process.

Histogram equalization often produces unrealistic effects in photographs; however it is very useful for scientific images like thermal, satellite orx-ray images, often the same class of images that user would apply false-color to. Also histogram equalization can produce undesirable effects (like visible image gradient) when applied to images with low color depth. For example if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images.

There are two ways to think about and implement histogram equalization, either as image change or as palette change. The operation can be expressed as P(M(I)) where I is the original image, M is histogram equalization mapping operation and P is a palette. If we define new palette as P'=P(M) and leave image I unchanged than histogram equalization is implemented as palette change. On the other hand if palette P remains unchanged and image is modified to I'=M(I) than the implementation is by image change. In most cases palette change is better as it preserves the original data.

Generalizations of this method use multiple histograms to emphasize local contrast, rather than overall contrast. Examples of such methods include adaptive histogram equalization and contrast limiting adaptive histogram equalization or CLAHE. Histogram equalization also seems to be used in biological neural networks so as to maximize the output firing rate of the neuron as a function of the input statistics. Histogram equalization is a specific case of the more general class of histogram remapping methods. These methods seek to adjust the image to make it easier to analyze or improve visual quality

GENETIC ALGORITHMS FOR SIMULATION OPTIMIZATION

2008-12-01T12:22:00.000-08:00

GENETIC ALGORITHMS

Physics, Biology, Economy or Sociology often have to deal with the classical problem of optimization. Economy particularly has become specialist of that field1. Generally speaking, a large part of mathematical development during the XVIIIth century dealt with that topic (remember those always repeated problems where you had to obtain the derivative of a function to find its extremes). Purely analytical methods widely proved their efficiency.

EVOLUTION AND OPTIMIZATION

The Basilosaurus was quite a prototype of a whale. It was about 15 meters long for 5 tons. It still had a quasi-independent head and posterior paws. He moved using undulatory movements and hunted small preys3. Its anterior members were reduced to small flippers with an elbow articulation. Movements in such a viscous element (water) are very hard and require big efforts. People concerned must have enough energy to move and control its trajectory. The anterior members of basilosaurus were not really adapted to swimming. To adapt them, a double phenomenon must occur : the shortening of the "arm" with the locking of the elbow articulation and the extension of the fingers which will constitute the base structure of the flipper.

The image shows that two fingers of the common dolphin are hypertrophied to the detriment of the rest of the member. The basilosaurus was a hunter, he had to be fast and precise. Through time, subjects appeared with longer fingers and short arms. They could move faster and more precisely than before, and therefore, live longer and have many descendants. Meanwhile, other improvements occurred concerning the general aerodynamic like the integration of the head to the body, improvement of the profile, strengthening of the caudal fin ... finally producing a subject perfectly adapted to the constraints of an aqueous environment. This process of adaptation, this morphological optimization is so perfect that nowadays, the similarity between a shark, a dolphin or a submarine is striking.

Darwinian mechanism hence generate an optimization process, Hydrodynamic optimization for fishes and others marine animals, aerodynamic for pterodactyls, birds or bats. This observation is the basis of genetic algorithms

EVOLUTION AND GENETIC ALGORITHMS

To improve the understanding of natural adaptation process, and to design artificial systems having properties similar to natural systems The basic idea is as follow : the genetic pool of a given population potentially contains the solution, or a better solution, to a given adaptive problem. This solution is not "active" because the genetic combination on which it relies is split between several subjects. Only the association of different genomes can lead to the solution. Simplistically speaking, we could by example consider that the shortening of the paw and the extension of the fingers of our basilosaurus are controlled by 2 "genes". No subject has such a genome, but during reproduction and crossover, new genetic combination occur and, finally, a subject can inherit a "good gene" from both parents : his paw is now a flipper. Holland method is especially effective because he not only considered the role of mutation (mutations improve very seldom the algorithms), but he also utilized genetic recombination, (crossover). These recombination, the crossover of partial solutions greatly improve the capability of the algorithm to approach, and eventually find, the optimum.

FUNCTIONING OF A GENETIC ALGORITHM

As an example, we're going to enter a world of simplified genetic. The "chromosomes" encode a group of linked features. "Genes" encode the activation or deactivation of a feature. Let us examine the global genetic pool of four basilosaurus belonging to this world. We will consider the "chromosomes" which encode the length of anterior members. The length of the "paw" and the length of the "fingers" are encoded by four genes : the first two encode the "paw" and the other two encode the fingers. In our representation of the genome, the circle on blue background depict the activation of a feature, the cross on green background depict its deactivation.

GAUSSIAN LOW PASS FILTER

2008-12-01T12:21:00.000-08:00

Gaussian is a widely used effect in graphics software, typically to reduce image noise and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales.

Mathematically, applying a Gaussian blur to an image is the same as convolving the image with a Gaussian function; this is also known as a two-dimensional Weierstrass transform. By contrast, convolving by a circle (i.e., a circular box blur) would more accurately reproduce the bokeh effect. Since the Fourier transform of a Gaussian is another Gaussian, applying a Gaussian blur has the effect of reducing the image's high-frequency components; a Gaussian blur is thus a low pass filter.

The Gaussian blur is a type of image-blurring filter that uses a Gaussian function (which is also used for the normal distribution in statistics) for calculating the transformation to apply to each pixel in the image.

FINGERPRINTS AUTHENTICATION

2008-12-01T12:19:00.000-08:00

Fingerprints are the oldest and most widely used form of biometric identification. Despite the widespread use of fingerprints, there is little statistical theory on the Uniqueness of fingerprint minutiae. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However, fingerprint images are rarely of perfect quality. They may be degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations.

In this dissertation, I firstly provide discussion on the methodology and implementation of techniques for fingerprint image enhancement and minutiae extraction. Experiments using a mixture of both synthetic test images and real fingerprint images are then conducted to evaluate the performance of the implemented techniques. In combination with these techniques, preliminary results on the statistics of fingerprint images are then presented and discussed.

Aim of the Project

 To match a Fingerprint image with a one already stored in the database.

 A fingerprint image essentially consists of a set of minutiae on the plane.

 Minutiae are the terminations and bifurcations of ridge lines in a fingerprint image.

 A new approach towards fingerprint recognition is to match the distribution and orientation of such points.

Motivation behind it

 Finger-print recognition is used in various systems for Verification, Identification etc.

 Recognizing manually can be very time consuming and costly.

 There are systems already in use which use similar technology and a lot of research is going on to improve the technique.

Algorithm

This particular method of fingerprint matching consists mainly of six stages ….

(i) Image Enhancement,

(ii) Ridge extraction

(iii) Binarization

(iv) Thinning

(v) Minutiae extraction

(vi) Post processing.