Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the Earth. This book provides an advanced overview of digital signal processing (DSP) and its applications to exploration seismology using real-world examples. The book begins by introducing seismic theory, describing how to identify seismic events in terms of signals and noise, and how to convert seismic data into the language of DSP. Deterministic DSP is then covered, together with non-conventional sampling techniques. The final part covers statistical seismic signal processing via Wiener optimum filtering, deconvolution, linear-prediction filtering and seismic wavelet processing. With over sixty end-of-chapter exercises, seismic data sets and data processing MATLAB codes included, this is an ideal resource for electrical engineering students unfamiliar with seismic data, and for Earth Scientists and petroleum professionals interested in DSP techniques.

Provides easy learning and understanding of DWT from a signal processing point of view * Presents DWT from a digital signal processing point of view, in contrast to the usual mathematical approach, making it highly accessible * Offers a comprehensive coverage of related topics, including convolution and correlation, Fourier transform, FIR filter, orthogonal and biorthogonal filters * Organized systematically, starting from the fundamentals of signal processing to the more advanced topics of DWT and Discrete Wavelet Packet Transform. * Written in a clear and concise manner with abundant examples, figures and detailed explanations * Features a companion website that has several MATLAB programs for the implementation of the DWT with commonly used filters This well-written textbook is an introduction to the theory of discrete wavelet transform (DWT) and its applications in digital signal and image processing. -- Prof. Dr. Manfred Tasche - Institut fur Mathematik, Uni Rostock Full review at https://zbmath.org/?q=an:06492561

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application. The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities. The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods. A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code. Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

Written using clear and accessible language, this text provides detailed coverage of the core mathematical concepts underpinning signal processing. All the core areas of mathematics are covered, including generalized inverses, singular value decomposition, function representation, and optimization, with detailed explanations of how basic concepts in these areas underpin the methods used to perform signal processing tasks. A particular emphasis is placed on the practical applications of signal processing, with numerous in-text practice questions and real-world examples illustrating key concepts, and MATLAB programs with accompanying graphical representations providing all the necessary computational background. This is an ideal text for graduate students taking courses in signal processing and mathematical methods, or those who want to establish a firm foundation in these areas before progressing to more advanced study.

A readable, understandable introduction to DSP for professionals and students alike . . .

This practical guide is a welcome alternative to more complicated introductions to DSP. It assumes no prior DSP experience and takes the reader step-by-step through the most basic signal processing concepts to more complex functions and devices, including sampling, filtering, frequency transforms, data compression, and even DSP design decisions. The guide provides clear, concise explanations and examples, while keeping mathematics to a minimum, to help develop a fundamental understanding of DSP.

Other features include:

• An extensive resource bibliography of more advanced DSP books.
• An example of a typical DSP system development cycle, including tool descriptions.
• A complete glossary of DSP-related acronyms

Whether you're a working engineer looking into DSP for the first time or an undergraduate struggling to comprehend the subject, this engaging introduction provides easy access to the basic knowledge that will lead to more advanced material.

Texas Instruments has been designing and manufacturing single-chip DSP devices since 1982 and now produces eight distinct generations as part of the industry-standard TMS320 family. Much of this book is based on the experience TI gained in developing DSPs and training first-time users.

A self-contained approach to DSP techniques and applications in radar imaging
The processing of radar images, in general, consists of three major fields: Digital Signal Processing (DSP); antenna and radar operation; and algorithms used to process the radar images. This book brings together material from these different areas to allow readers to gain a thorough understanding of how radar images are processed.
The book is divided into three main parts and covers:

* DSP principles and signal characteristics in both analog and digital domains, advanced signal sampling, and interpolation techniques
*
Antenna theory (Maxwell equation, radiation field from dipole, and linear phased array), radar fundamentals, radar modulation, and target-detection techniques (continuous wave, pulsed Linear Frequency Modulation, and stepped Frequency Modulation)
*
Properties of radar images, algorithms used for radar image processing, simulation examples, and results of satellite image files processed by Range-Doppler and Stolt interpolation algorithms

The book fully utilizes the computing and graphical capability of MATLAB? to display the signals at various processing stages in 3D and/or cross-sectional views. Additionally, the text is complemented with flowcharts and system block diagrams to aid in readers' comprehension.
Digital Signal Processing Techniques and Applications in Radar Image Processing serves as an ideal textbook for graduate students and practicing engineers who wish to gain firsthand experience in applying DSP principles and technologies to radar imaging.

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic.

Based on time-tested course material, this authoritative text examines the key topics, advanced mathematical concepts, and novel analytical tools needed to understand modern communication and radar systems. It covers computational linear algebra theory, VLSI systolic algorithms and designs, practical aspects of chaos theory, and applications in beamforming and array processing, and uses a variety of CDMA codes, as well as acoustic sensing and beamforming algorithms to illustrate key concepts. Classical topics such as spectral analysis are also covered, and each chapter includes a wealth of homework problems. This is an invaluable text for graduate students in electrical and computer engineering, and an essential reference for practitioners in communications and radar engineering.

Digital Signal Processing: Concepts and Applications, second edition covers the basic principles and operation of DSP devices. Its aim is to give the student the essentials of this mathematical subject in a form that can be easily understood and assimilated. The text concentrates on discrete systems, starting from digital filters and discrete Fourier transforms. These are then extended into adaptive filters and spectrum analysers with the minimum of mathematical derivation, concentrating on demonstrating the performance which is achievable from these processors in communications and radar system applications. This new edition has been updated to include learning outcomes and summaries and provide more examples. The text has been completely redesigned and is presented in a clear and easy-to-read style. Key features: * Self assessment questions within the text, with answers provided * Numerous practical worked examples on processor design and performance simulation<* MATLAB (R) code for animated simulations available to students via World Wide Web access This textbook is appropriate for undergraduate and MSc courses in signals and systems and signal processing, and for professional engineers who wish to have a simple, easy-to-read reference book on DSP techniques.

For courses in Probability and Random Processes. Probability, Statistics, and Random Processes for Engineers, 4e is a comprehensive treatment of probability and random processes that, more than any other available source, combines rigor with accessibility. Beginning with the fundamentals of probability theory and requiring only college-level calculus, the book develops all the tools needed to understand more advanced topics such as random sequences, continuous-time random processes, and statistical signal processing. The book progresses at a leisurely pace, never assuming more knowledge than contained in the material already covered. Rigor is established by developing all results from the basic axioms and carefully defining and discussing such advanced notions as stochastic convergence, stochastic integrals and resolution of stochastic processes.

Signal processing is the analysis, interpretation, and manipulation of signals. Signals of interest include sound, images, biological signals such as ECG, radar signals, and many others. Processing of such signals includes storage and reconstruction, separation of information from noise (for example, aircraft identification by radar), compression (for example, image compression), and feature extraction (for example, speech-to-text conversion). This book presents the latest research in the field from around the world.

Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing. Providing a broad perspective that fully demonstrates the impact of these tools, the accessible and tutorial-like chapters cover topics such as clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar. Each chapter includes coverage of theoretical principles, a detailed review of current knowledge, and discussion of key applications, and also highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side. Written by leading experts from both academia and industry, this is the ideal text for researchers, graduate students and industry professionals working in signal processing and radar.

This book stems from a unique and a highly effective approach to introducing signal processing, instrumentation, diagnostics, filtering, control, system integration, and machine learning.It presents the interactive industrial grade software testbed of mold oscillator that captures the distortion induced by beam resonance and uses this testbed as a virtual lab to generate input-output data records that permit unravelling complex system behavior, enhancing signal processing, modeling, and simulation background, and testing controller designs.All topics are presented in a visually rich and mathematically well supported, but not analytically overburdened format. By incorporating software testbed into homework and project assignments, the narrative guides a reader in an easily followed step-by-step fashion towards finding the mold oscillator disturbance removal solution currently used in the actual steel production, while covering the key signal processing, control, system integration, and machine learning concepts.The presentation is extensively class-tested and refined though the six-year usage of the book material in a required engineering course at the University of Illinois at Urbana-Champaign.

Discover a fresh approach for designing more efficient and cooperative wireless communications networks with this systematic guide. Covering everything from fundamental theory to current research topics, leading researchers describe a new, network-aware coding strategy that exploits the signal interactions that occur in dense wireless networks directly at the waveform level. Using an easy-to-follow, layered structure, this unique text begins with a gentle introduction for those new to the subject, before moving on to explain key information-theoretic principles and establish a consistent framework for wireless physical layer network coding (WPNC) strategies. It provides a detailed treatment of Network Coded Modulation, covers a range of WPNC techniques such as Noisy Network Coding, Compute and Forward, and Hierarchical Decode and Forward, and explains how WPNC can be applied to parametric fading channels, frequency selective channels, and complex stochastic networks. This is essential reading whether you are a researcher, graduate student, or professional engineer.

Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on Sequential Bayesian Detection, a new section on Ensemble Kalman Filters as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to fill-in-the gaps of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical sanity testing lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: * Classical Kalman filtering for linear, linearized, and nonlinear systems; modern unscented and ensemble Kalman filters: and the next-generation Bayesian particle filters * Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems * Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics * New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving * MATLAB(R) notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available * Problem sets included to test readers knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.

This cohesive treatment of cognitive radio and networking technology integrates information and decision theory to provide insight into relationships throughout all layers of networks and across all wireless applications. It encompasses conventional considerations of spectrum and waveform selection and covers topology determination, routing policies, content positioning and future hybrid architectures that fully integrate wireless and wired services. Emerging flexibility in spectrum regulation and the imminent adoption of spectrum-sharing policies make this topic of immediate relevance both to the research community and to the commercial wireless community. * Features specific examples of decision-making structures and criteria required to extend network density and scaling to unprecedented levels * Integrates sensing, control plane and content operations into a single cohesive structure * Provides simpler and more powerful models of network operation * Presents a unique approach to decision-making and to mechanisms for adjusting control plane activity to ensure network scaling * Generalises the concepts of shared and adaptive spectrum policies * Addresses network transport operations and dynamic management of cognitive wireless networks' own information seeking behaviour

This book gives a complete overview of the scientific and engineering aspects of radio and radar pertaining to studies of the Earth environment. The book opens with an analysis of wire antennas, antenna arrays, and aperture antennas suitable for radar applications. Following a treatment of sources of noise, the book moves on to give a detailed presentation of the most important scattering mechanisms exploited by radar. It then provides an overview of basic signal processing strategies, including coherent and incoherent strategies. Pulse compression, especially binary phase coding and frequency chirping, are then analyzed, and the radar range-Doppler ambiguity function is introduced. This is followed by a comprehensive treatment of radio wave propagation in the atmosphere and ionosphere. The remainder of the book deals with radar applications. The book will be valuable for graduate students and researchers interested in antenna and radar applications across the Earth and environmental sciences and engineering.

This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications.

The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as multisource and multiscale integration of information for physiology and clinical decision; the impact of advanced methods of signal processing in cardiology and neurology; the integration of signal processing methods with a modelling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; advanced methods of signal and data processing in genomics and proteomics; and classification and parameter enhancement.

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.