Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Field Programmable Gate Arrays (FPGAs) II

This Edited Volume Field Programmable Gate Arrays (FPGAs) II is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Computer and Information Science. The book comprises single chapters authored by various researchers and edited by an expert active in the Computer and Information Science research area. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on Computer and Information Science, and open new possible research paths for further novel developments

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible

Exploiting the GPU power for intensive geometric and imaging data computation.

Wang Jianqing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 81-86).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis --- p.3Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- Organization --- p.6Chapter 2 --- Programmable Graphics Hardware --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- Why Use GPU? --- p.9Chapter 2.3 --- Programmable Graphics Hardware Architecture --- p.11Chapter 2.4 --- Previous Work on GPU Computation --- p.15Chapter 3 --- Multilingual Virtual Performer --- p.17Chapter 3.1 --- Overview --- p.17Chapter 3.2 --- Previous Work --- p.18Chapter 3.3 --- System Overview --- p.20Chapter 3.4 --- Facial Animation --- p.22Chapter 3.4.1 --- Facial Animation using Face Space --- p.23Chapter 3.4.2 --- Face Set Selection for Lip Synchronization --- p.27Chapter 3.4.3 --- The Blending Weight Function Generation and Coartic- ulation --- p.33Chapter 3.4.4 --- Expression Overlay --- p.38Chapter 3.4.5 --- GPU Algorithm --- p.39Chapter 3.5 --- Character Animation --- p.44Chapter 3.5.1 --- Skeletal Animation Primer --- p.44Chapter 3.5.2 --- Mathematics of Kinematics --- p.46Chapter 3.5.3 --- Animating with Motion Capture Data --- p.48Chapter 3.5.4 --- Skeletal Subspace Deformation --- p.49Chapter 3.5.5 --- GPU Algorithm --- p.50Chapter 3.6 --- Integration of Skeletal and Facial Animation --- p.52Chapter 3.7 --- Result --- p.53Chapter 3.7.1 --- Summary --- p.58Chapter 4 --- Discrete Wavelet Transform On GPU --- p.60Chapter 4.1 --- Introduction --- p.60Chapter 4.1.1 --- Previous Works --- p.61Chapter 4.1.2 --- Our Solution --- p.61Chapter 4.2 --- Multiresolution Analysis with Wavelets --- p.62Chapter 4.3 --- Fragment Processor for Pixel Processing --- p.64Chapter 4.4 --- DWT Pipeline --- p.65Chapter 4.4.1 --- Convolution Versus Lifting --- p.65Chapter 4.4.2 --- DWT Pipeline --- p.67Chapter 4.5 --- Forward DWT --- p.68Chapter 4.6 --- Inverse DWT --- p.71Chapter 4.7 --- Results and Applications --- p.73Chapter 4.7.1 --- Geometric Deformation in Wavelet Domain --- p.73Chapter 4.7.2 --- Stylish Image Processing and Texture-illuminance De- coupling --- p.73Chapter 4.7.3 --- Hardware-Accelerated JPEG2000 Encoding --- p.75Chapter 4.8 --- Web Information --- p.78Chapter 5 --- Conclusion --- p.79Bibliography --- p.8

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Efficient architectures for multidimensional discrete transforms in image and video processing applications

PhD ThesisThis thesis introduces new image compression algorithms, their related architectures and data transforms architectures. The proposed architectures consider the current hardware architectures concerns, such as power consumption, hardware usage, memory requirement, computation time and output accuracy. These concerns and problems are crucial in multidimensional image and video processing applications. This research is divided into three image and video processing related topics: low complexity non-transform-based image compression algorithms and their architectures, architectures for multidimensional Discrete Cosine Transform (DCT); and architectures for multidimensional Discrete Wavelet Transform (DWT). The proposed architectures are parameterised in terms of wordlength, pipelining and input data size. Taking such parameterisation into account, efficient non-transform based and low complexity image compression algorithms for better rate distortion performance are proposed. The proposed algorithms are based on the Adaptive Quantisation Coding (AQC) algorithm, and they achieve a controllable output bit rate and accuracy by considering the intensity variation of each image block. Their high speed, low hardware usage and low power consumption architectures are also introduced and implemented on Xilinx devices. Furthermore, efficient hardware architectures for multidimensional DCT based on the 1-D DCT Radix-2 and 3-D DCT Vector Radix (3-D DCT VR) fast algorithms have been proposed. These architectures attain fast and accurate 3-D DCT computation and provide high processing speed and power consumption reduction. In addition, this research also introduces two low hardware usage 3-D DCT VR architectures. Such architectures perform the computation of butterfly and post addition stages without using block memory for data transposition, which in turn reduces the hardware usage and improves the performance of the proposed architectures. Moreover, parallel and multiplierless lifting-based architectures for the 1-D, 2-D and 3-D Cohen-Daubechies-Feauveau 9/7 (CDF 9/7) DWT computation are also introduced. The presented architectures represent an efficient multiplierless and low memory requirement CDF 9/7 DWT computation scheme using the separable approach. Furthermore, the proposed architectures have been implemented and tested using Xilinx FPGA devices. The evaluation results have revealed that a speed of up to 315 MHz can be achieved in the proposed AQC-based architectures. Further, a speed of up to 330 MHz and low utilisation rate of 722 to 1235 can be achieved in the proposed 3-D DCT VR architectures. In addition, in the proposed 3-D DWT architecture, the computation time of 3-D DWT for data size of 144×176×8-pixel is less than 0.33 ms. Also, a power consumption of 102 mW at 50 MHz clock frequency using 256×256-pixel frame size is achieved. The accuracy tests for all architectures have revealed that a PSNR of infinite can be attained

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

ECG analysis and classification using CSVM, MSVM and SIMCA classifiers

Reliable ECG classification can potentially lead to better detection methods and increase accurate diagnosis of arrhythmia, thus improving quality of care. This thesis investigated the use of two novel classification algorithms: CSVM and SIMCA, and assessed their performance in classifying ECG beats. The project aimed to introduce a new way to interactively support patient care in and out of the hospital and develop new classification algorithms for arrhythmia detection and diagnosis. Wave (P-QRS-T) detection was performed using the WFDB Software Package and multiresolution wavelets. Fourier and PCs were selected as time-frequency features in the ECG signal; these provided the input to the classifiers in the form of DFT and PCA coefficients. ECG beat classification was performed using binary SVM. MSVM, CSVM, and SIMCA; these were subsequently used for simultaneously classifying either four or six types of cardiac conditions. Binary SVM classification with 100% accuracy was achieved when applied on feature-reduced ECG signals from well-established databases using PCA. The CSVM algorithm and MSVM were used to classify four ECG beat types: NORMAL, PVC, APC, and FUSION or PFUS; these were from the MIT-BIH arrhythmia database (precordial lead group and limb lead II). Different numbers of Fourier coefficients were considered in order to identify the optimal number of features to be presented to the classifier. SMO was used to compute hyper-plane parameters and threshold values for both MSVM and CSVM during the classifier training phase. The best classification accuracy was achieved using fifty Fourier coefficients. With the new CSVM classifier framework, accuracies of 99%, 100%, 98%, and 99% were obtained using datasets from one, two, three, and four precordial leads, respectively. In addition, using CSVM it was possible to successfully classify four types of ECG beat signals extracted from limb lead simultaneously with 97% accuracy, a significant improvement on the 83% accuracy achieved using the MSVM classification model. In addition, further analysis of the following four beat types was made: NORMAL, PVC, SVPB, and FUSION. These signals were obtained from the European ST-T Database. Accuracies between 86% and 94% were obtained for MSVM and CSVM classification, respectively, using 100 Fourier coefficients for reconstructing individual ECG beats. Further analysis presented an effective ECG arrhythmia classification scheme consisting of PCA as a feature reduction method and a SIMCA classifier to differentiate between either four or six different types of arrhythmia. In separate studies, six and four types of beats (including NORMAL, PVC, APC, RBBB, LBBB, and FUSION beats) with time domain features were extracted from the MIT-BIH arrhythmia database and the St Petersburg INCART 12-lead Arrhythmia Database (incartdb) respectively. Between 10 and 30 PCs, coefficients were selected for reconstructing individual ECG beats in the feature selection phase. The average classification accuracy of the proposed scheme was 98.61% and 97.78 % using the limb lead and precordial lead datasets, respectively. In addition, using MSVM and SIMCA classifiers with four ECG beat types achieved an average classification accuracy of 76.83% and 98.33% respectively. The effectiveness of the proposed algorithms was finally confirmed by successfully classifying both the six beat and four beat types of signal respectively with a high accuracy ratio

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks