Contents:
PartI: Multidimensional Systems - General 1. Two Decades of Multidimensional Systems Research and Future Trends 2. MATLAB Package for n-D Sampling-Based Modelling of Nonlinear Systems 3. Behaviours, Modules, and Duality. Part II: Control of Multidimensional Systems and Applications 4. Output Feedback Stabilizability and Stabilization of Linear n-D Systems 5. A Review of n-D System Control Theory in a Certain Practical Sense 6. Two Decades of Research on Linear Repetitive Processes 7. Analysis and Control of Discrete Linear Repetitive Processes with Dynamic Boundary Conditions 8. Entire Function Methods for Optimisation Problems in Continuous-Discrete 2D Control Systems 9. Existence of an Optimal Solution for a Continuous Roesser Problem with a Terminal Condition. Part III: Multidimensional Circuits and Signal Processing 10. On The Structure of Linear Phase Perfect Reconstruction Quincunx Filter Banks 11. Deadbands in m-D Delta Operator Based Digital Filters with Floating Point Arithmetic 12. Cascade Synthesis of Two-Variable Lossless Two-Port Networks with Lumped Elements and Transmission Lines: A Semi-Analytic Procedure 13. Reconstruction of Positron Emission Tomography Images by Using MAP Estimation 14. Inspection of Fixing Devices of Railroad Lines Using Image Processing Algorithms 15. Increase of Frame Rate in Virtual Environments by Using 2D Image Warping

Summarizing the latest research results for mitigating intersymbol interference (ISI), this pioneering reference/text introduces the new technique of modulated coding (MC) and considers three cases of MC encoding and decoding in which ISI channel information is: 1) available for both encoding and decoding, 2) not available for either encoding or decoding, and 3) available for encoding but not for decoding. Includes previously unpublished information and open problems on MC for ISI channels Focusing on transmitter-assisted equalization methods for ISI mitigation, Modulated Coding for Intersymbol Interference Channels reviews current ISI mitigation methods and formulates the capacity and information rates of an ISI channel details basic concepts related to MC and describes the combination of an MC and an ISI channel compares the coding of an MC and ISI channel to that of an uncoded Additive White Gaussian Noise (AWGN) channel considers the case of joint maximum-likelihood sequence estimation (MLSE) encoding and decoding of an MC coded ISI channel illustrates situations of suboptimal MC design given an ISI channel, such as Zero-Forcing Decision Feedback Equalizer (ZF-DFE) and Minimum Mean Square Error Decision Feedback Equalizer (MMSE-DFE) with corresponding MC designs considers multiple transmit and multiple receive antenna systems studies a channel-independent MC-coded orthogonal frequency division multiplexing (OFDM) system and also covers vector OFDM systems analyzes and applies polynomial ambiguity resistant MC (PARMC) to single-antenna and multiple-antenna systems and more Illustrated with over 900 equations and drawings, Modulated Coding for Intersymbol Interference Channels makes an excellent reference for electrical, electronics, signal processing, mechanical, image filtering and processing, computer circuit and systems, digital design, and communication engineers; and applied mathematicians; and a useful text

"In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, ""father of the MRI,"" and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: * Mathematical fundamentals Signal generation and detection principles* Signal characteristics* Signal localization principles* Image reconstruction techniques* Image contrast mechanisms Image resolution, noise, and artifacts* Fast-scan imaging* Constrained reconstruction. Complete with a comprehensive set of examples and homework problems, PRINCIPLES OF MAGNETIC RESONANCE IMAGING is the must-read book to improve your knowledge of this revolutionary technique. For more information on the IEEE Press Series in Biomedical Engineering edited by Metin Akay, go to http://www caip.rutgers.edu/ per cent7Eakay/book/ Professors: To request an examination copy simply e-mail [email protected]." Sponsored by: IEEE Engineering in Medicine and Biology Society.

This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.

An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

Signal processing is a broad and timeless area. The term "signal" includes audio, video, speech, image, communication, geophysical, sonar, radar, medical, and more. Signal processing applies to the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
Handbook of Formulas and Tables for Signal Processing a must-have reference for all engineering professionals involved in signal and image processing. Collecting the most useful formulas and tables - such as integral tables, formulas of algebra, formulas of trigonometry - the text includes:
o Material for the deterministic and statistical signal processing areas
o Examples explaining the use of the given formula
o Numerous definitions
o Many figures that have been added to special chapters
Handbook of Formulas and Tables for Signal Processing brings together - in one textbook - all the equations necessary for signal and image processing for professionals transforming anything from a physical to a manipulated form, creating a new standard for any person starting a future in the broad, extensive area of research.

Recovering the phase of the Fourier transform is a ubiquitous problem in imaging applications from astronomy to nanoscale X-ray diffraction imaging. Despite the efforts of a multitude of scientists, from astronomers to mathematicians, there is, as yet, no satisfactory theoretical or algorithmic solution to this class of problems. Written for mathematicians, physicists and engineers working in image analysis and reconstruction, this book introduces a conceptual, geometric framework for the analysis of these problems, leading to a deeper understanding of the essential, algorithmically independent, difficulty of their solutions. Using this framework, the book studies standard algorithms and a range of theoretical issues in phase retrieval and provides several new algorithms and approaches to this problem with the potential to improve the reconstructed images. The book is lavishly illustrated with the results of numerous numerical experiments that motivate the theoretical development and place it in the context of practical applications.

Synthesis of Computational Structures for Analog Signal Processing focuses on analysis and design of analog signal processing circuits. The author presents a multitude of design techniques for improving the performances of analog signal processing circuits, and proposes specific implementation strategies that can be used in CMOS technology. The author's discussion proceeds from the perspective of signal processing as it relates to analog. Included are coverage of low-power design, portable equipment, wireless nano-sensors and medical implantable devices.
The material is especially appropriate for researchers and specialists in the area of analog and mixed-signal CMOS VLSI design, as well as postgraduate or Ph.D. students working on analog microelectronics.

Presenting statistical and stochastic methods for the analysis and design of technological systems in engineering and applied areas, this work documents developments in statistical modelling, identification, estimation and signal processing. The book covers such topics as subspace methods, stochastic realization, state space modelling, and identification and parameter estimation.

The book systematically introduces theories of frequently-used modern signal processing methods and technologies, and focuses discussions on stochastic signal, parameter estimation, modern spectral estimation, adaptive filter, high-order signal analysis and non-linear transformation in time-domain signal analysis. With abundant exercises, the book is an essential reference for graduate students in electrical engineering and information science.

This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to "experiment and learn" as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.

In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.

This book provides the reader with empirical findings on innovative signal processing approaches to detecting pathologies in infant cries, by comparing new technological approaches to standard ones. The contributors examine novel approaches to machine adaptation to dysarthric speech.

This book discusses statistical modeling of single- and multi-channel synthetic aperture radar (SAR) images and the applications of these newly developed models in land and ocean monitoring, such as target detection and terrain classification. It is a valuable reference for researchers and engineers interested in information processing of remote sensing, radar signal processing, and image interpretation.

This book covers newly developed and novel Steganography techniques and algorithms. The book outlines techniques to provide security to a variety of applications using Steganography, with the goal of both hindering an adversary from decoding a hidden message, and also preventing an adversary from suspecting the existence of covert communications. The book looks into applying these newly designed and improved algorithms to provide a new and efficient Steganographic system, called Characteristic Region-Based Image Steganography (CR-BIS). The algorithms combine both the robustness of the Speeded-Up Robust Features technique (SURF) and Discrete Wavelet Transform (DWT) to achieve characteristic region Steganography synchronization. The book also touches on how to avoid hiding data in the whole image by dynamically selecting characteristic regions for the process of embedding. Applies and discusses innovative techniques for hiding text in a digital image file or even using it as a key to the encryption; Provides a variety of methods to achieve characteristic region Steganography synchronization; Shows how Steganography improves upon cryptography by using obscurity features.

Starting with an overview of current research progresses on multiple access technology, the book then presents the theoretical fundamentals, technical principles, transmission scheme, key technologies and evaluation results of new multi-access technologies, especially focusing on its typical applications 5G communication systems. With extensive practical cases, it is an essential reference for researchers, engineers and graduate students.

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.

With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.

This book provides the basic concepts and fundamental principles of dynamic systems including experimental methods, calibration, signal conditioning, data acquisition and processing as well as the results presentation. How to select suitable sensors to measure is also introduced. It is an essential reference to students, lecturers, professionals and any interested lay readers in measurement technology.