This book reviews the most powerful attack strategies and potential defense mechanisms, always approaching the interplay between the Fusion Center and the Byzantines from a game-theoretic perspective. For each of the settings considered, the equilibria of the game and the corresponding payoffs are derived, shedding new light on the achievable performance level and the impact that the presence of the Byzantines has on the accuracy of decisions made by the Fusion Center. Accordingly, the book offers a simple yet effective introduction to the emerging field of adversarial information fusion, providing a wealth of intuitive take-home lessons for practitioners interested in applying the most basic notions to the design of practical systems, while at the same time introducing researchers and other readers to the mathematical details behind the theory.

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.

The rapid development in various fields of Digital Audio Effects, or DAFX, has led to new algorithms and this second edition of the popular book, "DAFX: Digital Audio Effects" has been updated throughout to reflect progress in the field. It maintains a unique approach to DAFX with a lecture-style introduction into the basics of effect processing. Each effect description begins with the presentation of the physical and acoustical phenomena, an explanation of the signal processing techniques to achieve the effect, followed by a discussion of musical applications and the control of effect parameters. Topics covered include: filters and delays, modulators and demodulators, nonlinear processing, spatial effects, time-segment processing, time-frequency processing, source-filter processing, spectral processing, time and frequency warping musical signals.

Updates to the second edition include: Three completely new chapters devoted to the major research areas of: Virtual Analog Effects, Automatic Mixing and Sound Source Separation, authored by leading researchers in the field .Improved presentation of the basic concepts and explanation of the related technology.Extended coverage of the MATLABTM scripts which demonstrate the implementation of the basic concepts into software programs. Companion website (www.dafx.de) which serves as the download source for MATLABTM scripts, will be updated to reflect the new material in the book.

Discussing DAFX from both an introductory and advanced level, the book systematically introduces the reader to digital signal processing concepts, how they can be applied to sound and their use in musical effects. This makes the book suitable for a range of professionals including those working in audio engineering, as well as researchers and engineers involved in the area of digital signal processing along with students on multimedia related courses.

Discrete-time signal processing has had a momentous impact on advances in engineering and science over recent decades. The rapid progress of digital and mixed-signal integrated circuits in processing speed, functionality and cost-effectiveness has led to their ubiquitous employment in signal processing and transmission in diverse milieux.

The absence of training or pilot signals from many kinds of transmission a" in, for example, speech analysis, seismic exploration and texture image analysis a" necessitates the widespread use of blind equalization and system identification. There have been a great many algorithms developed for these purposes, working with one- or two-dimensional (2-d) signals and with single-input single-output (SISO) or multiple-input multiple-output (MIMO), real or complex systems. It is now time for a unified treatment of this subject, pointing out the common characteristics and the sometimes close relations of these algorithms as well as learning from their different perspectives. Blind Equalization and System Identification provides such a unified treatment presenting theory, performance analysis, simulation, implementation and applications.

Topics covered include:

a [ SISO, MIMO and 2-d non-blind equalization (deconvolution) algorithms;

a [ SISO, MIMO and 2-d blind equalization (deconvolution) algorithms;

a [ SISO, MIMO and 2-d blind system identification algorithms;

a [ algorithm analyses and improvements;

a [ applications of SISO, MIMO and 2-d blind equalization/identification algorithms.

Each chapter is completed by exercises and computer assignments designed to further understanding and to give practical experiencewith the algorithms discussed.

This is a textbook for graduate-level courses in discrete-time random processes, statistical signal processing, and blind equalization and system identification. It contains material which will also interest researchers and practicing engineers working in digital communications, source separation, speech processing, image processing, seismic exploration, sonar, radar and other, similar applications.

This book discusses non-conventional digital signal processing based on direct processing of delta-sigma modulated bit-stream. The main attributes of low-pass delta-sigma analog-to-digital converters are: simple and inexpensive design, robustness of design to component tolerances, low-power consumption, high input impedance, high resolution (more than 20 bits) and possibility of direct arithmetic operation on its bit-stream. The author presents a number of theoretical and simulation results related to newly proposed linear and non-linear circuits such as delta-sigma adders, delta-sigma rectifiers, delta-sigma RMS and AGC circuits, delta-sigma frequency deviation meters, etc. The proposed circuits are not application limited and can be used in instrumentation, sensor application, bio-medical application, communications, etc. Presents novel linear and nonlinear circuits for direct processing of delta-sigma modulated bit-stream; The proposed circuits are supported by theoretical and simulation results; Recommends potential applications of the proposed circuits, and proposes ideas for further investigation.

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.

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

Arduino 101 houses an Intel Curie module which offers a better performance at a lower power footprint. The module has two 32-bit MCUs - an x86 Intel Quark processor and an ARC EM4 processor along with 384kB flash memory and 80kB SRAM. These onboard MCUs combine a variety of new technologies including wireless communication via Bluetooth Low Energy, 6 axis motion sensor with an accelerometer, and a gyroscope. With this book, you will: Explore neural net pattern matching Have the Arduino learn gesture recognition Perfect for students, teachers, and hobbyists who need just enough information to get started with the Arduino 101.

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.

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.

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.

"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.

A distillation of technical material culled from key radar publications and conferences that have occurred over the past five years, this book provides access to the answers to common design problems with designer crib sheets. William Morchin has also written Airborne Early Warning Radar.

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.

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 detailed, up-to-date introduction to heterogeneous cellular networking introduces its characteristic features, the technology underpinning it and the issues surrounding its use. Comprehensive and in-depth coverage of core topics catalogue the most advanced, innovative technologies used in designing and deploying heterogeneous cellular networks, including system-level simulation and evaluation, self-organisation, range expansion, cooperative relaying, network MIMO, network coding and cognitive radio. Practical design considerations and engineering tradeoffs are also discussed in detail, including handover management, energy efficiency and interference management techniques. A range of real-world case studies, provided by industrial partners, illustrate the latest trends in heterogeneous cellular networks development. Written by leading figures from industry and academia, this is an invaluable resource for all researchers and practitioners working in the field of mobile communications.

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.

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.