Over the past decades a considerable interest has been concentrated on problems involving signals and systems that depend on more than one variable. 2-D signals and systems have been studied in relation to several modern engineering fields such as process control, multidimensional digital filtering, image enhancement, image deblurring, signal processing etc. Among the major results developed so far, 2-D digital filters are investigated as a description in frequency domain or as a convolution of the input and the unit response, which has a great potential for practical applications in 2-D image and signal processing. This monograph aims to address several problems of control and filtering of 2-D discrete systems. Specifically the problems of Hinfinity filtering, Hinfinity control, stabilization, Hinfinity model reduction as well as Hinfinity deconvolution filtering of 2-D linear discrete systems are treated.

Discover the exciting world of software-defined radio (SDR) through this hands-on, beginner-friendly introduction. Software-defined radio (SDR) is transforming wireless communications through flexible, inexpensive devices that can be programmed to receive AM and FM broadcasts, transmit signals over Wi-Fi, monitor GPS location data, communicate with the International Space Station, and more. This book provides a beginner-friendly introduction to this revolutionary technology. Its learn-by-doing approach will take you from total beginner to confident SDR practitioner, without confusing math or technical jargon. Working with intuitive, graphical software, you’ll explore how SDRs work, discover how to demodulate, filter, tune, and transmit analog radio signals—and get hooked on an exciting new hobby!

"An excellent introductory book" (Review of the First Edition in the International Journal of Electrical Engineering Education) "…it will serve as a reference book in this area for a long time" (Review of Revised Edition in Zentralblatt für Mathematik (Germany))

Firmly established over the last decade as the essential introductory Digital Signal Processing (DSP) text, this second edition reflects the growing importance of random digital signals and random DSP in the undergraduate syllabus by including two new chapters. The authors’ practical, problem-solving approach to DSP continues in this new material, which is packed up by additional worked examples and computer programs. The book now features:

• fundamentals of digital signals and systems
• time and frequency domain analysis and processing, including digital convolution and the Discrete and Fast Fourier Transforms
• design and practical application of digital filters
• description and processing of random signals, including correlation, filtering, and the detection of signals in noise

This book focuses on techniques for obtaining optimal detection algorithms for implementation on digital computers.KEY TOPICS:The book explains statistical and signal processing in the context of numerous practical examples, focusing on current detection applications - especially problems in speech and communications. The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Topics covered include: probability density functions and properties; statistical decision theory for both deterministic and random signals; signals with unknown parameters; white and colored Gaussian noise; non-Gaussian noise; detectors; model change detection; complex extensions; vector generalization and array processing. This is the perfect companion to Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory.MARKET:For practicing electrical engineers building detectors for real-world applications. Also for electronics students and researchers.

Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply artificial intelligence and machine learning to biosignal techniques.

Learn about an information-theoretic approach to managing interference in future generation wireless networks. Focusing on cooperative schemes motivated by Coordinated Multi-Point (CoMP) technology, the book develops a robust theoretical framework for interference management that uses recent advancements in backhaul design, and practical pre-coding schemes based on local cooperation, to deliver the increased speed and reliability promised by interference alignment. Gain insight into how simple, zero-forcing pre-coding schemes are optimal in locally connected interference networks, and discover how significant rate gains can be obtained by making cell association decisions and allocating backhaul resources based on centralized (cloud) processing and knowledge of network topology. Providing a link between information-theoretic analyses and interference management schemes that are easy to implement, this is an invaluable resource for researchers, graduate students and practicing engineers in wireless communications.

Here's a thorough overview of the state-of-the-art in design and implementation of advanced tracking for single and multiple sensor systems. This practical resource provides modern system designers and analysts with in-depth evaluations of sensor management, kinematic and attribute data processing, data association, situation assessment, and modern tracking and data fusion methods as applied in both military and non-military arenas.

Whether you desire background information to get you up-to-speed or if you want to access only the most recently developed advanced methods, the book's modular chapter structure makes its easy for you to get the specific information you're looking for quickly. You get full coverage of tracking topics such as:
Passive ranging and interactive multiple model (IMM) filtering
Multiple hypothesis tracking (MHT) data association
Bayesian and Dempster-Shafer attribute fusion
Multiple sensor tracking methods for distributed systems such as for space based surveillance systems
Use of tracking data for situation assessment and sensor management
Track fusion and track-before-detect (TBD) methods
Efficient allocation of agile beam radar resources

This work addresses this problem in the short-time Fourier transform (STFT) domain. We divide the general problem into five basic categories depending on the number of microphones being used and whether the interframe or interband correlation is considered. The first category deals with the single-channel problem where STFT coefficients at different frames and frequency bands are assumed to be independent. In this case, the noise reduction filter in each frequency band is basically a real gain. Since a gain does not improve the signal-to-noise ratio (SNR) for any given subband and frame, the noise reduction is basically achieved by liftering the subbands and frames that are less noisy while weighing down on those that are more noisy. The second category also concerns the single-channel problem. The difference is that now the interframe correlation is taken into account and a filter is applied in each subband instead of just a gain. The advantage of using the interframe correlation is that we can improve not only the long-time fullband SNR, but the frame-wise subband SNR as well. The third and fourth classes discuss the problem of multichannel noise reduction in the STFT domain with and without interframe correlation, respectively. In the last category, we consider the interband correlation in the design of the noise reduction filters. We illustrate the basic principle for the single-channel case as an example, while this concept can be generalized to other scenarios. In all categories, we propose different optimization cost functions from which we derive the optimal filters and we also define the performance measures that help analyzing them.

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.

Over the last 50 years there have been an increasing number of applications of algebraic tools to solve problems in communications, in particular in the fields of error-control codes and cryptography. More recently, broader applications have emerged, requiring quite sophisticated algebra - for example, the Alamouti scheme in MIMO communications is just Hamilton's quaternions in disguise and has spawned the use of PhD-level algebra to produce generalizations. Likewise, in the absence of credible alternatives, the industry has in many cases been forced to adopt elliptic curve cryptography. In addition, algebra has been successfully applied to problems in signal processing such as face recognition, biometrics, control design, and signal design for radar. This book introduces the reader to the algebra they need to appreciate these developments and to various problems solved by these techniques.

"Steganography" is the art of communicating a secret message, hiding the very existence of a secret message. This is typically done by hiding the message within a non-sensitive document. S"teganalysis" is the art and science of detecting such hidden messages. The task in steganalysis is to take an object (communication) and classify it as either a steganogram or a clean document. Most recent solutions apply classification algorithms from machine learning and pattern recognition, which tackle problems too complex for analytical solution by teaching computers to learn from empirical data.

Part 1of the book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context. Part 2 is a survey of a wide range of feature vectors proposed for steganalysis with performance tests and comparisons. Part 3 is an in-depth study of machine learning techniques and classifier algorithms, and presents a critical assessment of the experimental methodology and applications in steganalysis.

Key features: Serves as a tutorial on the topic of steganalysis with brief introductions to much of the basic theory provided, and also presents a survey of the latest research.Develops and formalises the application of machine learning in steganalysis; with much of the understanding of machine learning to be gained from this book adaptable for future study of machine learning in other applications. Contains Python programs and algorithms to allow the reader to modify and reproduce outcomes discussed in the book.Includes companion software available from the author's website.

Spectral analysis is widely used to interpret time series collected in diverse areas. This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time series from oceanography, metrology, atmospheric science and other areas are used in running examples throughout, to allow clear comparison of how the various methods address questions of interest. All major nonparametric and parametric spectral analysis techniques are discussed, with emphasis on the multitaper method, both in its original formulation involving Slepian tapers and in a popular alternative using sinusoidal tapers. The authors take a unified approach to quantifying the bandwidth of different nonparametric spectral estimates. An extensive set of exercises allows readers to test their understanding of theory and practical analysis. The time series used as examples and R language code for recreating the analyses of the series are available from the book's website.

Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics.

While previous EW exploited flaws in the analogue equipment to corrupt or degrade the sensor detection or localisation capabilities, EW is now an information battle. Modern autonomous threat sensors can readily detect and locate targets by incorporating state of the art high speed digital signal processing (DSP) algorithms that focus on the classification of targets via target physical features. As a result the autonomous threat has a significant advantage over attacking forces consisting of armoured vehicles, aircraft or ships. To elucidate the state of EW, this book focuses on the example of autonomous anti ship missiles (ASM) attacking a naval fleet rather than airborne battles, thus filling a significant gap in the EW literature. It describes modern DSP algorithms that have been published by ASM development personnel from several nations, including the People's Republic of China and the Russian federation and outlines instances where it has been successfully used against ships. The book elaborates on the mathematical techniques employed and the advantages of incorporating digital signal processing algorithms into the autonomous sensor. With straight forward DSP algorithms, ASM can rapidly identify and distinguish electronically generated false targets, passive decoys, chaff and true targets. Moreover, special sensor waveforms now proactively probe the targets for enhanced feature measurements, and modern multi-channel optimal DSP readily mitigates noise jamming.

Master the usage of s-parameters in signal integrity applications and gain full understanding of your simulation and measurement environment with this rigorous and practical guide. Solve specific signal integrity problems including calculation of the s-parameters of a network, linear simulation of circuits, de-embedding, and virtual probing, all with expert guidance. Learn about the interconnectedness of s-parameters, frequency responses, filters, and waveforms. This invaluable resource for signal integrity engineers is supplemented with the open-source software SignalIntegrity, a Python package for scripting solutions to signal integrity problems.

This book is devoted to the application of advanced signal processing on event-related potentials (ERPs) in the context of electroencephalography (EEG) for the cognitive neuroscience. ERPs are usually produced through averaging single-trials of preprocessed EEG, and then, the interpretation of underlying brain activities is based on the ordinarily averaged EEG. We find that randomly fluctuating activities and artifacts can still present in the averaged EEG data, and that constant brain activities over single trials can overlap with each other in time, frequency and spatial domains. Therefore, before interpretation, it will be beneficial to further separate the averaged EEG into individual brain activities. The book proposes systematic approaches pre-process wavelet transform (WT), independent component analysis (ICA), and nonnegative tensor factorization (NTF) to filter averaged EEG in time, frequency and space domains to sequentially and simultaneously obtain the pure ERP of interest. Software of the proposed approaches will be open-accessed.

Computer vision seeks a process that starts with a noisy, ambiguous signal from a TV camera and ends with a high-level description of discrete objects located in 3-dimensional space and identified in a human classification. This book addresses the process at several levels. First to be treated are the low-level image-processing issues of noise removaland smoothing while preserving important lines and singularities in an image. At a slightly higher level, a robust contour tracing algorithm is described that produces a cartoon of the important lines in the image. Thirdis the high-level task of reconstructing the geometry of objects in the scene. The book has two aims: to give the computer vision community a new approach to early visual processing, in the form of image segmentation that incorporates occlusion at a low level, and to introduce real computer algorithms that do a better job than what most vision programmers use currently. The algorithms are: - a nonlinear filter that reduces noise and enhances edges, - an edge detector that also finds corners and produces smoothed contours rather than bitmaps, - an algorithm for filling gaps in contours.

This book offers a user friendly, hands-on, and systematic introduction to applied and computational harmonic analysis: to Fourier analysis, signal processing and wavelets; and to their interplay and applications. The approach is novel, and the book can be used in undergraduate courses, for example, following a first course in linear algebra, but is also suitable for use in graduate level courses. The book will benefit anyone with a basic background in linear algebra. It defines fundamental concepts in signal processing and wavelet theory, assuming only a familiarity with elementary linear algebra. No background in signal processing is needed. Additionally, the book demonstrates in detail why linear algebra is often the best way to go. Those with only a signal processing background are also introduced to the world of linear algebra, although a full course is recommended. The book comes in two versions: one based on MATLAB, and one on Python, demonstrating the feasibility and applications of both approaches. Most of the MATLAB code is available interactively. The applications mainly involve sound and images. The book also includes a rich set of exercises, many of which are of a computational nature.

Adaptive Control (second edition) shows how a desired level of system performance can be maintained automatically and in real time, even when process or disturbance parameters are unknown and variable. It is a coherent exposition of the many aspects of this field, setting out the problems to be addressed and moving on to solutions, their practical significance and their application. Discrete-time aspects of adaptive control are emphasized to reflect the importance of digital computers in the application of the ideas presented. The second edition is thoroughly revised to throw light on recent developments in theory and applications with new chapters on: multimodel adaptive control with switching, direct and indirect adaptive regulation and adaptive feedforward disturbance compensation. Many algorithms are newly presented in MATLAB (R) m-file format to facilitate their employment in real systems. Classroom-tested slides for instructors to use in teaching this material are also now provided. All of this supplementary electronic material can be downloaded from fill in URL. The core material is also up-dated and re-edited to keep its perspective in line with modern ideas and more closely to associate algorithms with their applications giving the reader a solid grounding in: synthesis and analysis of parameter adaptation algorithms, recursive plant model identification in open and closed loop, robust digital control for adaptive control; * robust parameter adaptation algorithms, practical considerations and applications, including flexible transmission systems, active vibration control and broadband disturbance rejection and a supplementary introduction on hot dip galvanizing and a phosphate drying furnace. Control researchers and applied mathematicians will find Adaptive Control of significant and enduring interest and its use of example and application will appeal to practitioners working with unknown- and variable-parameter plant. Praise for the first edition: ...well written, interesting and easy to follow, so that it constitutes a valuable addition to the monographies in adaptive control for discrete-time linear systems... suitable (at least in part) for use in graduate courses in adaptive control.

In diesem Band der Reihe Fachwissen Technische Akustik wird das Verfahren der experimentellen Modalanalyse vorgestellt. Mit diesem Verfahren koennen die von der Ausbreitung von Luft- und Koerperschall bestimmten dynamischen Eigenschaften von Systemen untersucht werden. Beispiele fur solche Systeme sind Strukturen im Maschinen- und Fahrzeugbau oder auch kleinere Innenraume, deren akustischen Verhalten von Interesse ist. In einer Einfuhrung wird zunachst auf den Zusammenhang des physikalischen Modells und des systemtheoretischen Modells eingegangen sowie der Nutzen des modalen Modells fur die Beschreibung der Systemeigenschaften erlautert. Danach wird die dem modalen Modell zugrunde liegende Theorie sowie der Zusammenhang der modalen Parameter mit den im Systemmodell verwendeten Frequenzgangen dargestellt. Verschiedene Verfahren der experimentellen Modalanalyse werden diskutiert, darunter sowohl solche zur getrennten Bestimmung einzelner modaler Parameter als auch solche, bei denen eine Vielzahl modaler Parameter gleichzeitig aus den gemessenen Frequenzgangen ermittelt wird. Zusatzlich wird auf das praktische Vorgehen bei der Gewinnung der dazu notwendigen Messdaten und die Moeglichkeiten zur UEberprufung der Ergebnisse eingegangen. Zur Demonstration der verschiedenen Moeglichkeiten und Verfahren wird ein einfaches praktisches Beispiel ausfuhrlich behandelt. Das umfasst die Vorgehensweise bei der Messung ebenso wie die Anwendung unterschiedlich aufwandiger Verfahren zur Extraktion der modalen Parameter. Dazu werden zahlreiche Ergebnisse gezeigt, so dass Moeglichkeiten und Grenzen der experimentellen Modalanalyse deutlich werden.