Power System Small Signal Stability Analysis and Control, Second Edition analyzes severe outages due to the sustained growth of small signal oscillations in modern interconnected power systems. This fully revised edition addresses the continued expansion of power systems and the rapid upgrade to smart grid technologies that call for the implementation of robust and optimal controls. With a new chapter on MATLAB programs, this book describes how the application of power system damping controllers such as Power System Stabilizers and Flexible Alternating Current Transmission System controllers-namely Static Var Compensator and Thyristor Controlled Series Compensator -can guard against system disruptions. Detailed mathematical derivations, illustrated case studies, the application of soft computation techniques, designs of robust controllers, and end-of-chapter exercises make it a useful resource to researchers, practicing engineers, and post-graduates in electrical engineering.

This Springerbreif introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs. The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity. Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where 'scalable' means that the computational and implementation complexities do not grow rapidly with the network size. This Springerbrief will be target researchers and professionals working in the Cloud Radio Access Network (C-Ran) field, as well as advanced-level students studying electrical engineering.

Achieve faster and more efficient network design and optimization with this comprehensive guide. Some of the most prominent researchers in the field explain the very latest analytic techniques and results from stochastic geometry for modelling the signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks. This book will help readers to understand the effects of combining different system deployment parameters on key performance indicators such as coverage and capacity, enabling the efficient allocation of simulation resources. In addition to covering results for network models based on the Poisson point process, this book presents recent results for when non-Poisson base station configurations appear Poisson, due to random propagation effects such as fading and shadowing, as well as non-Poisson models for base station configurations, with a focus on determinantal point processes and tractable approximation methods. Theoretical results are illustrated with practical Long-Term Evolution (LTE) applications and compared with real-world deployment results.

In many areas of human endeavor, the systems involved are not available for direct measurement. Instead, by combining mathematical models for a system's evolution with partial observations of its evolving state, we can make reasonable inferences about it. The increasing complexity of the modern world makes this analysis and synthesis of high-volume data an essential feature in many real-world problems.
The celebrated Kalman-Bucy filter, designed for linear dynamical systems with linearly structured measurements, is the most famous Bayesian filter. Its generalizations to nonlinear systems and/or observations are collectively referred to as nonlinear filtering (NLF), an extension of the Bayesian framework to the estimation, prediction, and interpolation of nonlinear stochastic dynamics. NLF uses a stochastic model to make inferences about an evolving system and is a theoretically optimal algorithm.
The breadth of its applications, firmly established and still emerging, is simply astounding. Early uses such as cryptography, tracking, and guidance were mostly of a military nature. Since then, the scope has exploded. It includes the study of global climate, estimating the state of the economy, identifying tumors using non-invasive methods, and much more.
The Oxford Handbook of Nonlinear Filtering is the first comprehensive written resource for the subject. It contains classical and recent results and applications, with contributions from 58 authors. Collated into 10 parts, it covers the foundations of nonlinear filtering, connections to stochastic partial differential equations, stability and asymptotic analysis, estimation and control, approximation theory and numerical methods for solving the nonlinear filtering problem (including particle methods). It also contains a part dedicated to the application of nonlinear filtering to several problems in mathematical finance.

This book provides design methods for Digital Signal Processors and Application Specific Instruction set Processors, based on the author's extensive, industrial design experience. Top-down and bottom-up design methodologies are presented, providing valuable guidance for both students and practicing design engineers.
Coverage includes design of internal-external data types, application specific instruction sets, micro architectures, including designs for datapath and control path, as well as memory sub systems. Integration and verification of a DSP-ASIP processor are discussed and reinforced with extensive examples.
FOR INSTRUCTORS: To obtain access to the solutions manual for this title simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials through the ";Instructor Manual"; link on this book's full web page.
* Instruction set design for application specific processors based on fast application profiling
* Micro architecture design methodology
* Micro architecture design details based on real examples
* Extendable architecture design protocols
* Design for efficient memory sub systems (minimizing on chip memory and cost)
* Real example designs based on extensive, industrial experiences

With the proliferation of wireless networks, there is a need for more compact, low-cost, power efficient transmitters that are capable of supporting the various communication standards, including Bluetooth, WLAN, GSM/EDGE, WCDMA and 4G of 3GPP cellular. This book describes a novel idea of RF digital-to-analog converters (RFDAC) and demonstrates how they can realize all-digital, fully-integrated RF transmitters that support all the current multi-mode and multi-band communication standards. With this book the reader will: Understand the challenges of realizing a universal CMOS RF transmitter Recognize the design issues and the advantages and disadvantages related to analog and digital transmitter architectures Master designing an RF transmitter from system level modeling techniques down to circuit designs and their related layout know-hows Grasp digital polar and I/Q calibration techniques as well as the digital predistortion approaches Learn how to generate appropriate digital I/Q baseband signals in order to apply them to the test chip and measure the RF-DAC performance.

Chipless RFID based on RF Encoding Particle: Realization, Coding and Reading System explores the field of chipless identification based on the RF Encoding Particle (REP). The book covers the possibility of collecting information remotely with RF waves (RFID) with totally passive tags without wire, batteries, and chips, and even printed on paper. Despite the many benefits of RFID, deployment is still hindered by several economic and technological factors. Among these barriers are the high cost of tags, lack of reliability and security in the information contained in the RFID chip, and how tags are 'recycled.' This book focuses on the development of chipless RFID tags, representing a new family of low cost tags. With this technology information is extracted from the electromagnetic response of the tag, which depends only on its geometry. Various solutions have been developed by the authors to increase the amount of information, reduce the surface of the tag, or improve the robustness of detection. Considerations such as realization using paper substrate, the development of a low cost detection system, and measurements in a real environment have been addressed for practical implementation.

An introduction to a new design for nonlinear control systems—backstepping—written by its own architects.

This innovative book breaks new ground in nonlinear and adaptive control design for systems with uncertainties. Introducing the recursive backstepping methodology, it shows—for the first time—how uncertain systems with severe nonlinearities can be successfully controlled with this new powerful design tool.

Communicative and accessible at a level not usually present in research texts, Nonlinear and Adaptive Control Design can be used as either a stand-alone or a supplemental text in courses on nonlinear or adaptive control, as well as in control research and applications. It eases the reader into the subject matter, assuming only standard undergraduate knowledge of control theory, and provides a pedagogical presentation of the material, most of which is completely new and not available in other textbooks. Written by the creators of backstepping, the book:

• Introduces the basic design tools and demonstrates their effectiveness through worked examples
• Provides detailed proofs and application examples (active suspension, jet engine, induction motor, and many others)
• Develops adaptive backstepping, tuning functions, and modular designs with full state feedback
• Generalizes the methodology to nonlinear systems with output feedback
• Describes the advantages of the new adaptive nonlinear techniques over traditional methods
• Offers a systematic methodology for performance improvement
• Provides new designs for linear systems which can be used independently from therest of the book
• Is self-contained with an extensive summary of stability and passivity prerequisites
• Includes sixty illustrations and tables with design algorithms

Nonlinear and Adaptive Control Design is an absolute must for researchers and graduate students with an interest in nonlinear systems, adaptive control, stability and differential equations and for anyone who would like to find out about the new and exciting advances in these areas.

Signal processing--the concept of frequency often referred to as spectral concepts--is the focal point of this collection of essays. Discussing parametric methods, with specific focus on time-series models, Capon's method, and notions of sub-spaces, as well as the popular and traditional analog methods, this text also addresses the quests for better frequency resolution in spectral concepts with advancements in digital tools.

Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes.

This text deals with signal processing as an important aspect of electronic communications in its role of transmitting information, and the language of its expression. It develops the required mathematics in an interesting and informative way, leading to confidence on the part of the reader. The first part of the book focuses on continuous-time models, and contains chapters on signals and linear systems, and on system responses. Fourier methods, so vital in the study of information theory, are developed prior to a discussion of methods for the design of analogue filters. The second part of the book is directed towards discrete-time signals and systems. There is full development of the z- and discrete Fourier transforms to support the chapter on digital filter design.
All preceding material in the book is drawn together in the final chapter on some important aspects of speech processing which provides an up-to-date example of the use of the theory. Topics considered include a speech production model, linear predictive filters, lattice filters and cepstral analysis, with application to recognition of non-nasal voiced speech and formant estimation.
In addition to course requirement for undergraduates studying electrical engineering, applied mathematics, and branches of computer science involving such signal processing as speak synthesis, computer vision and robotics, this book should provide a valuable reference source for post-graduate research work in industry and academia.
An elementary knowledge of algebra (e.g. partial fractions) is a prerequisite, and also calculus including differential equations. A knowledge of complex numbers and of the basic concept of a function of a complex variable is also needed.
Deals with signal processing as an important aspect of electronic communications in its role of transmitting information, and the language of its expressionTopics considered include a speech production model, linear predictive filters, lattice filters and cepstral analysis, with application to recognition of non-nasal voiced speech and formant estimation

Artificial Vision is a rapidly growing discipline, aiming to build computational models of the visual functionalities in humans, as well as machines that emulate them. Visual communication in itself involves a numberof challenging topics with a dramatic impact on contemporary culture where human-computer interaction and human dialogue play a more and more significant role.
This state-of-the-art book brings together carefully selected review articles from world renowned researchers at the forefront of this exciting area. The contributions cover topics including image processing, computational geometry, optics, pattern recognition, and computer science. The book is divided into three sections. Part I covers active vision; Part II deals with the integration of visual with cognitive capabilities; and Part III concerns visual communication.
Artificial Vision will be essential reading for students and researchers in image processing, vision, and computer science who want to grasp the current concepts and future directions of this challenging field.
Key Features
* This state-of-the-art book brings together selected review articles and accounts of current projects from world-renowned researchers at the forefront of this exciting area; The contributions cover topics such as:
* psychology of perception
* image processing
* computational geometry
* visual knowledge representation and languages
* It is this truly multi-disciplinary approach that has produced successful theories and applications for the subject.

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.

Dieses Buch gibt eine Einfuhrung in die Nachrichtentechnik bis hin zu modernen Verfahren der Datenubertragung und Datensicherheit. Zu Beginn wird ein Abriss der Geschichte sowie ausgewahlte Modelle der Nachrichtentechnik vorgestellt. Die von Claude E. Shannon begrundete Informationstheorie lost sich von der Bedeutung der Daten und benutzt, vereinfacht ausgedruckt, ausschliesslich deren statistische Eigenschaften. Auf Basis der Informationstheorie werden Einfuhrungen in die Gebiete Quellen- und Kanalcodierung, Ubertragungskanale, Entscheidungstheorie, Modulationsverfahren sowie elementare Kommunikationsprotokolle und Datensicherheit gegeben. Exemplarisch werden zu diesen Gebieten ausgewahlte praktische Verfahren, Methoden und Algorithmen beschrieben. Ein ausfuhrlicher Anhang stellt Grundlagen der Wahrscheinlichkeitsrechnung, der Fourier-Analyse und der Signal- und Systemtheorie bereit."

Sixth in the book series, Advances in Image Communication, which documents the rapid advancements of recent years in image communication technologies, this volume provides a comprehensive exploration of subband coding.

Originally, subband coding and transform coding were developed separately. The former, however, benefitted considerably from the earlier evolution of transform coding theory and practice. Retaining their own terminology and views, the two methods are closely related and this book indeed aims to unify the approaches. Specifically, the volume contributes effectively to the understanding of frequency domain coding techniques. Many images from coding experiments are presented, enabling the reader to consider the properties of different coders.

Chapter 1 introduces the problem of image compression in general terms. Sampling of images and other fundamental concepts, such as entropy and the rate distortion function, are briefly reviewed. The idea of viewing coding techniques as series expansions is also introduced. The second chapter presents signal decomposition and the conditions for perfect reconstruction from minimum representations. Chapter 3 deals with filter bank structures, primarily those displaying the perfect reconstruction property. Quantization techniques and the efficient exploitation of the bit resources are discussed from a theoretical perspective in Chapter 4 and this issue is further examined in Chapter 6, from a more practical point of view. Chapter 5 provides a development of gain formulas, i.e. quantitative measures of the performance of filter banks in a subband coding context, and these are then employed in a search for optimal filter banks. A number of examples of coded images using different subband coders are presented in Chapter 7, these indicating that subband coders give rise to some characteristic types of image degradations. Accordingly, Chapter 8 presents several techniques for minimizing these artifacts. The theory and practice of subband coding of video, at several target bit rates, is discussed in the last chapter.

Spectacular advances during the last decade have altered the related disciplines of computing and telecommunications beyond all recognition. The developments in the"enabling technologies,"which have made these advances possible, have been less obvious to the casual observer. The subject of this book is one of these technologies--the coding of still images and picture sequences (video).
Digital Compression of Still Images and Video is general in approach and covers all the recognized coding algorithms; explaining their basic theory in enough detail that the reader will understand the principles involved. Results which have been achieved with coding algorithms by researchers in all parts of the world are also discussed. A brief historical review of the area is included to orient those new to the field, and the work is supported by references spanning the period from the earliest work on time/frequency analysis, to the latest topic of research interest--image sequence transmission using asynchronous transfer mode (ATM) techniques.
This book will make an excellent reference for graduates and researchers working on image coding and transmission, and will also be of use to those in the related areas of computer vision and multimedia developments. It is essential reading for the rapidly increasing number of technical and professional staff in the industrial environment who, maybe for the first time, are coming to grips with image coding.
Key Features
* Provides complete and up-to-date coverage of all digital image and video coding (compression) techniques
* Includes more than 850 references covering relevant work from the 1940s to the present
* Presents information on new directions in coding
* Contains details on current coding standards
* Describes and explains all algorithms in use for image coding

The rapid advancements in image communication technologies are documented in the book series, Advances in Image Communication. Each publication provides an in-depth exploration of an intrinsic element of the multi-disciplinary field and together they form a comprehensive overview of the whole.

This volume, the fifth in the series, examines the definition, study and use of the wavelet transform in communications for two-dimensional (2-D) digital signals. The transform is used for signal reorganization before compression and the trade-off between these two steps and the whole compression process is discussed.

The five chapters specifically present the theory of wavelets applied to images, then applications of compression of still images and sequences. Chapter 1 introduces biorthogonal bases of compactly supported wavelets: this generalization of orthonormal wavelet theory allows the use of linear phase filters. A non rectangular wavelet representation of 2-D signals is developed in the second chapter: the properties usually used with wavelets, phase, linearity, and regularity are discussed. Chapter 3 is composed of three parts: a description of commonly used biorthogonal wavelets; a presentation of vector quantization algorithms; a consideration of lattice vector quantization followed by a discussion of the bit allocation procedure (with experimental results given). The fourth chapter deals with a region-based discrete wavelet transform for image coding. Chapter 5 investigates the transmission of image sequences: wavelet transforms and motion estimation are detailed in a multiconstraint approach of image sequence coding.