An up-to-date analysis of the SAR wavefront reconstruction signal theory and its digital implementation With the advent of fast computing and digital information processing techniques, synthetic aperture radar (SAR) technology has become both more powerful and more accurate. Synthetic Aperture Radar Signal Processing with MATLAB Algorithms addresses these recent developments, providing a complete, up-to-date analysis of SAR and its associated digital signal processing algorithms. This book introduces the wavefront reconstruction signal theory that underlies the best SAR imaging methods and provides clear guidelines to system design, implementation, and applications in diverse areas—from airborne reconnaissance to topographic imaging of ocean floors to surveillance and air traffic control to medical imaging techniques, and numerous others. Enabling professionals in radar signal and image processing to use synthetic aperture technology to its fullest potential, this work:

• Includes M-files to supplement this book that can be retrieved from The MathWorks anonymous FTP server at ftp://ftp.mathworks.com/pub/books/soumekh
• Provides practical examples and results from real SAR, ISAR, and CSAR databases
• Outlines unique properties of the SAR signal that cannot be found in other information processing systems
• Examines spotlight SAR, stripmap SAR, circular SAR, and monopulse SAR modalities
• Discusses classical SAR processing issues such as motion compensation and radar calibration

The principles of signal processing are fundamental to the operation of many everyday devices. This book introduces the basic theory of digital signal processing, with emphasis on real-world applications. Sampling, quantisation, the Fourier transform, filters, Bayesian methods and numerical considerations are covered, then developed to illustrate how they are used in audio, image, and video processing and compression, and in communications. The book concludes with methods for the efficient implementation of algorithms in hardware and software. Intuitive arguments rather than mathematical ones are used wherever possible, and links between various signal processing techniques are stressed. The advantages and disadvantages of different approaches are presented in the context of real-world examples, enabling the reader to choose the best solution to a given problem. With over 200 illustrations and over 130 exercises (including solutions), this book will appeal to practitioners working in signal processing, and undergraduate students of electrical and computer engineering.

A practical, hands-on guide to embedded signal processing

This unique reference covers the basic concepts, principles, and theory behind digital signal processing (DSP), but emphasizes a learn-by-doing approach and focuses on real-time applications. Readers quickly learn to use the latest tools to design, simulate, and implement the algorithms for applications. Part A explores fundamental DSP concepts using software tools from MathWorks, Analog Devices, Inc. (ADI), and National Instruments (NI) Part B focuses on the design and implementation of embedded systems based on the Blackfin processor and introduces many design and development tools Part C focuses on designing and implementing real-time DSP applications The book includes experiments and exercises based on the Blackfin BF533 and BF537 EZ-KIT Lite boards; an accompanying FTP site provides additional exercises using the Blackfin simulator and other EZ-KIT Lite boards The companion Web site is updated continually to reflect the latest upgrades to Blackfin tools and processors Hands-on examples, quizzes, experiments, exercises, exercise problems, and application projects accelerate and enhance learning

This is an excellent desktop reference for practicing engineers and a combination workbook and textbook for undergraduate and graduate students in embedded signal processing design and/or real-time signal processing courses.

This book provides an expert introduction to audio forensics, an essential specialty in modern forensic science, equipping readers with the fundamental background necessary to understand and participate in this exciting and important field of study. Modern audio forensic analysis combines skills in digital signal processing, the physics of sound propagation, acoustical phonetics, audio engineering, and many other fields. Scientists and engineers who work in the field of audio forensics are called upon to address issues of authenticity, quality enhancement, and signal interpretation for audio evidence that is important to a criminal law enforcement investigation, an accident investigation board, or an official civil inquiry. Expertise in audio forensics has never been more important. In addition to routine recordings from emergency call centers and police radio dispatchers, inexpensive portable audio/video recording systems are now in widespread use. Forensic evidence from the scene of a civil or criminal incident increasingly involves dashboard recorders in police cars, vest-pocket personal recorders worn by law enforcement officers, smart phone recordings from bystanders, and security surveillance systems in public areas and businesses. Utilizing new research findings and both historical and contemporary casework examples, this book blends audio forensic theory and practice in an informative and readable manner suitable for any scientifically-literate reader. Extensive examples, supplementary material, and authoritative references are also included for those who are interested in delving deeper into the field.

This book provides anyone needing a primer on random signals and processes with a highly accessible introduction to these topics. It assumes a minimal amount of mathematical background and focuses on concepts, related terms and interesting applications to a variety of fields. All of this is motivated by numerous examples implemented with MATLAB, as well as a variety of exercises at the end of each chapter."

This textbook is unique because of its in-depth treatment of the applications of wavelets and wavelet transforms to many areas, across many disciplines. The book is written to serve the needs of a one or two semester course at either the undergraduate or graduate level. The author uses a very simplified, accessible approach that de-emphasizes mathematical rigor. The presentation includes many diagrams to illustrate points being discussed and uses MATLAB for all of application code. The author reinforces concepts introduced in the book with easy to grasp review questions and problems, tailored to each specific chapter for better mastery of the subject matter. This book enables students to understand the fundamental concepts of wavelets and wavelet transforms, as well as how to use them for problem solutions in digital signal and image processing, mixed-signal testing, space applications, aerospace applications, biomedical, cyber security, homeland security and many other application areas.

< p="" style=""> This highly informative and carefully presented book focuses on the fields of ergonomics/human factors and discusses the future of the community vis-a-vis health problems, productivity, aging, etc. Ergonomic intercession must be seen in light of its effect on productivity because ergonomic solutions will improve productivity as the reduction of environmental stressors, awkward postures and efforts lead to a reduction in task execution time. The book provides promising evidence that the field of ergonomics continues to thrive and develop deeper insights into how work environments, products and systems can be developed to meet needs, demands and limitations of humans and how they can support productivity improvements. Some of the themes covered are anthropometry and workplace design, biomechanics and modelling in ergonomics, cognitive and environmental ergonomics, ergonomic intervention and productivity, ergonomics in transport, mining, agriculture and forestry, health systems, work physiology and sports ergonomics, etc. This book is beneficial to academicians, policymakers and the industry alike. ^

This second volume on a popular topic brings together the works of scholars working on the latest techniques, standards, and emerging deployment on "living in the age of wireless communications and smart vehicular systems." The format of this work centers on four themes: driver and driving environment recognition, telecommunication applications, noise reduction, dialogue in vehicles. Will interest researchers and professionals working in signal processing technologies, next generation vehicle design and networks for mobile platforms.

Since its emergence as an important research area in the early 1980s, the topic of wavelets has undergone tremendous development on both theoretical and applied fronts. Myriad research and survey papers and monographs have been published on the subject, documenting different areas of applications such as sound and image processing, denoising, data compression, tomography, and medical imaging. The study of wavelets remains a very active field of research, and many of its central techniques and ideas have evolved into new and promising research areas.

This volume, a collection of invited contributions developed from talks at an international conference on wavelets, is divided into three parts: Part I is devoted to the mathematical theory of wavelets and features several papers on wavelet sets and the construction of wavelet bases in different settings. Part II looks at the use of multiscale harmonic analysis for understanding the geometry of large data sets and extracting information from them. Part III focuses on applications of wavelet theory to the study of several real-world problems.

Overall, the book is an excellent reference for graduate students, researchers, and practitioners in theoretical and applied mathematics, or in engineering.

This book introduces the basic concepts of signal processing for scientists and students with no engineering background. The book presents the concepts with minimum use of mathematical formulations and more emphasis on visual illustrations. The idea is to present an intuitive approach to understanding the basics of signal processing and exemplify some practical applications of the concepts by which the readers achieve basic knowledge and skills in signal processing. Most of illustrations in the book have been created by computer programming in MATLAB (R); thus, the reader will learn the basics of using computers in signal processing applications.

This book comprises a collection of papers by international experts, presented at the International Conference on NextGen Electronic Technologies (ICNETS2-2017). ICNETS2 encompassed six symposia covering all aspects of electronics and communications engineering domains, including relevant nano/micro materials and devices. Featuring the latest research on computational signal processing and analysis, the book is useful to researchers, professionals, and students working in the core areas of electronics and their applications, especially signal processing, embedded systems, and networking.

Tensor signal processing is an emerging field with important applications to computer vision and image processing.

This book presents the state of the art in this new branch of signal processing, offering a great deal of research and discussions by leading experts in the area. The wide-ranging volume offers an overview into cutting-edge research into the newest tensor processing techniques and their application to different domains related to computer vision and image processing.

This comprehensive text will prove to be an invaluable reference and resource for researchers, practitioners and advanced students working in the area of computer vision and image processing.

This fully updated and expanded textbook covers designing working systems at very high frequencies. The updated book includes new chapters on Circuit Board Layout Process and Circuit-Board Attacks and Security and more in-depth material on all the original chapters. As with the first edition, this book combines an intuitive, physics-based approach to electromagnetics with a focus on solving realistic problems. The book emphasizes an intuitive approach to electromagnetics, and then uses this foundation to show the reader how both physical phenomena can cause signals to propagate incorrectly; and how to solve commonly encountered issues. Emphasis is placed on real problems that the author has encountered in his professional career, integrating problem-solving strategies and real signal-integrity case studies throughout the presentation. Students are challenged to think about managing complex design projects and implementing successful engineering and manufacturing processes. For the new edition, the author designed a circuit board that illustrates many of the principles in the book, created instructor materials including PowerPoint slides, a homework bank, and a test bank, and created materials that departments can use for ABET assessment.

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: * Highlights signal processing and machine learning as key approaches to quantitative finance. * Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. * Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. * Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths, and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering, and automotive engineering.

The last two decades have produced tremendous developments in the mathematical theory of wavelets and their great variety of applications in science and engineering. Wavelets allow complex information, such as music, speech, images, and patterns, to be decomposed into an elementary form called building blocks at different positions and scales. The information is reconstructed with high precision. In an effort to acquaint researchers in applied mathematics, physics, statistics, computer science, and engineering and to stimulate further research, a regional research conference was organized at the University of Central Florida in May 1998. Many distinguished applied mathematicians and engineering scientists participated in the conference and presented a digest of recent developments, open questions, and unsolved problems in this rapidly growing and important field.

The carefully selected chapters in this new text will appeal to the reader interested in a broad perspective of wavelet analysis and time-frequency signal analysis. Wavelet Transforms and Time-Frequency Signal Analysis brings together recent developments in theory and applications of wavelet transforms that are likely to determine fruitful directions for future advanced study and research.

The book is designed as a new source for modern topics dealing with wavelets, wavelet transforms, time-frequency signal analysis, and other applications for future development of this new, important, and useful subject for mathematics, science and engineering.

Topics and Features:

* Offers broad coverage of recent material on wavelet analysis and time-frequency signal analysis that is not covered in other recent reference books

* Provides the reader with a thorough mathematical background and a wide variety of applications that are sufficient for interdisciplinary collaborative research in applied mathematics

* Presents information that puts the reader at the forefront of current research

Wavelet Transforms and Time-Frequency Signal Analysis will serve as a research reference or as a text for an advanced course in wavelet analysis and time-frequency signal analysis. Professionals working on modern applied mathematics, computer science, computer engineering, electrical engineering, physics, and biomedical engineering will also find this book useful.

This book presents watermarking algorithms derived from signal processing methods such as wavelet transform, matrix decomposition and cosine transform to address the limitations of current technologies. For each algorithm, mathematical foundations are explained with analysis conducted to evaluate performances on robotness and efficiency. Combining theories and practice, it is suitable for information security researchers and industrial engineers.

This book proposes tools for analysis of multidimensional and metric data, by establishing a state-of-the-art of the existing solutions and developing new ones. It mainly focuses on visual exploration of these data by a human analyst, relying on a 2D or 3D scatter plot display obtained through Dimensionality Reduction. Performing diagnosis of an energy system requires identifying relations between observed monitoring variables and the associated internal state of the system. Dimensionality reduction, which allows to represent visually a multidimensional dataset, constitutes a promising tool to help domain experts to analyse these relations. This book reviews existing techniques for visual data exploration and dimensionality reduction such as tSNE and Isomap, and proposes new solutions to challenges in that field. In particular, it presents the new unsupervised technique ASKI and the supervised methods ClassNeRV and ClassJSE. Moreover, MING, a new approach for local map quality evaluation is also introduced. These methods are then applied to the representation of expert-designed fault indicators for smart-buildings, I-V curves for photovoltaic systems and acoustic signals for Li-ion batteries.

Volume 3 of the second edition of the fully revised and updated Digital Signal and Image Processing using MATLAB, after first two volumes on the "Fundamentals" and "Advances and Applications: The Deterministic Case", focuses on the stochastic case. It will be of particular benefit to readers who already possess a good knowledge of MATLAB, a command of the fundamental elements of digital signal processing and who are familiar with both the fundamentals of continuous-spectrum spectral analysis and who have a certain mathematical knowledge concerning Hilbert spaces. This volume is focused on applications, but it also provides a good presentation of the principles. A number of elements closer in nature to statistics than to signal processing itself are widely discussed. This choice comes from a current tendency of signal processing to use techniques from this field. More than 200 programs and functions are provided in the MATLAB language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.

This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG).

Fully focused on addressing the missing connection between signal processing and ML. Provides one-stop guide reference for the readers. Oriented towards the material and flow with regard to general introduction, technical aspects. Comprehensively elaborates on the material with examples and.

Considerable amount of effort has been devoted, over the recent years, towards the development of electronic skin (e-skin) for many application domains such as prosthetics, robotics, and industrial automation. Electronic Skin: Sensors and Systems focuses on the main components constituting the e-skin system. The e-skin system is based on: i) sensing materials composing the tactile sensor array, ii) the front end electronics for data acquisition and signal conditioning, iii) the embedded processing unit performing tactile data decoding, and iv) the communication interface in charge of transmitting the sensors data for further computing. Technical topics discussed in the book include: * Tactile sensing material; * Electronic Skin systems; * Embedded computing and tactile data decoding; * Communication systems for tactile data transmission; * Relevant applications of e-skin system; The book takes into account not only sensing materials but it also provides a thorough assessment of the current state of the art at system level. The book addresses embedded electronics and tactile data processing and decoding, techniques for low power embedded computing, and the communication interface. Electronic Skin: Sensors and Systems is ideal for researchers, Ph.D. students, academic staff and Masters/research students in sensors/sensing systems, embedded systems, data processing and decoding, and communication systems.

Appropriate for courses in Signals and Systems. A market leader in previous editions, this book continues to offer complete, separate treatment survey of continuous and discrete linear systems. It utilizes a systems approach to solving practical engineering problems, rather than using the framework of traditional circuit theory. Numerous examples from circuit theory appear throughout, however, to illustrate the various systems techniques introduced. The Fourth Edition has been thoroughly updated to effectively integrate the use of computers and to accurately reflect the latest theoretical advances.

This book presents a synthesis of the research carried out in the Laboratory of Signal Processing and Communications (LaPSyC), CONICET, Universidad Nacional del Sur, Argentina, since 2003. It presents models and techniques widely used by the signal processing community, focusing on low-complexity methodologies that are scalable to different applications. It also highlights measures of the performance and impact of each compensation technique. The book is divided into three parts: 1) basic models 2) compensation techniques and 3) applications in advanced technologies. The first part addresses basic architectures of transceivers, their component blocks and modulation techniques. It also describes the performance to be taken into account, regardless of the distortions that need to be compensated. In the second part, several schemes of compensation and/or reduction of imperfections are explored, including linearization of power amplifiers, compensation of the characteristics of analog-to- digital converters and CFO compensation for OFDM modulation. The third and last part demonstrates the use of some of these techniques in modern wireless-communication systems, such as full-duplex transmission, massive MIMO schemes and Internet of Things applications.