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Bayesian Signal Processing

Author: James V. Candy
Publisher: John Wiley & Sons
ISBN: 1119125472
Size: 68.98 MB
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Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to “fill-in-the gaps” of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical “sanity testing” lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: “Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving MATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available Problem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.

Radio Resource Management In Multi Tier Cellular Wireless Networks

Author: Ekram Hossain
Publisher: John Wiley & Sons
ISBN: 1118502671
Size: 14.31 MB
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Providing an extensive overview of the radio resource management problem in femtocell networks, this invaluable book considers both code division multiple access femtocells and orthogonal frequency-division multiple access femtocells. In addition to incorporating current research on this topic, the book also covers technical challenges in femtocell deployment, provides readers with a variety of approaches to resource allocation and a comparison of their effectiveness, explains how to model various networks using Stochastic geometry and shot noise theory, and much more.

Robust Systems Theory And Applications

Author: Ricardo S. Sánchez-Peña
Publisher: Wiley-Interscience
Size: 76.97 MB
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A complete, up-to-date textbook on an increasingly important subject Robust Systems Theory and Applications covers both the techniques used in linear robust control analysis/synthesis and in robust (control-oriented) identification. The main analysis and design methods are complemented by elaborated examples and a group of worked-out applications that stress specific practical issues: nonlinearities, robustness against changes in operating conditions, uncertain infinite dimensional plants, and actuator and sensor limitations. Designed expressly as a textbook for master's and first-year PhD students, this volume: * Introduces basic robustness concepts in the context of SISO systems described by Laplace transforms, establishing connections with well-known classical control techniques * Presents the internal stabilization problem from two different points of view: algebraic and state --space * Introduces the four basic problems in robust control and the Loop shaping design method Presents the optimal *2 control problem from a different viewpoint, including an analysis of the robustness properties of *2 controllers and a treatment of the generalized *2 problem * Presents the *2 control problem using both the state-space approach developed in the late 1980s and a Linear Matrix Inequality approach (developed in the mid 1990s) that encompasses more general problems * Discusses more general types of uncertainties (parametric and mixed type) and ??-synthesis as a design tool * Presents an overview of optimal ,1 control theory and covers the fundamentals of its star-norm approximation * Presents the basic tools of model order reduction * Provides a tutorial on robust identification * Offers numerous end-of-chapter problems and worked-out examples of robust control

Adaptive Approximation Based Control

Author: Jay A. Farrell
Publisher: John Wiley & Sons
ISBN: 0471781800
Size: 16.60 MB
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A highly accessible and unified approach to the design and analysis of intelligent control systems Adaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox. Mixing approximation theory, parameter estimation, and feedback control, this book presents a unified approach designed to enable readers to apply adaptive approximation based control to existing systems, and, more importantly, to gain enough intuition and understanding to manipulate and combine it with other control tools for applications that have not been encountered before. The authors provide readers with a thought-provoking framework for rigorously considering such questions as: * What properties should the function approximator have? * Are certain families of approximators superior to others? * Can the stability and the convergence of the approximator parameters be guaranteed? * Can control systems be designed to be robust in the face of noise, disturbances, and unmodeled effects? * Can this approach handle significant changes in the dynamics due to such disruptions as system failure? * What types of nonlinear dynamic systems are amenable to this approach? * What are the limitations of adaptive approximation based control? Combining theoretical formulation and design techniques with extensive use of simulation examples, this book is a stimulating text for researchers and graduate students and a valuable resource for practicing engineers.

Data Variant Kernel Analysis

Author: Yuichi Motai
Publisher: John Wiley & Sons
ISBN: 1119019346
Size: 72.43 MB
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Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.

Control Oriented System Identification

Author: Jie Chen
Publisher: Wiley-Interscience
ISBN: 9780471320487
Size: 76.30 MB
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A comprehensive, one-stop reference for new system modeling and identification tools The field of control-oriented identification has grown immensely over the past decade, spawning numerous results and modeling techniques and promising the potential to influence science and engineering for years to come. In this new work, Jie Chen and Guoxiang Gu, two leading authorities on worst-case identification, share their vision and walk readers through carefully selected topics from the vast literature, offering a much-needed, timely comprehensive introduction to the theory of H identification and model validation. Chen and Gu clearly demonstrate the pros and cons of the worst-case approach in comparison to traditional techniques and provide researchers in systems and control theory with ready access to many new and complementary identification tools. Through a rigorous yet logical and easy-to-follow treatment, supported by many deep insights, intuitions, and philosophical thinking, they: * Survey and assess the current state of control and system identification research * Develop both two-stage and interpolatory algorithms for system identification * Show readers how to analyze the properties of linear algorithms * Offer a unique emphasis on model uncertainty estimation and complexity, two of the central issues * Develop both time-domain and frequency-domain identification algorithms * Explain in detail uncertainty model validation concepts and techniques * Devote a chapter to a review of the requisite mathematics Provide a concise yet self-contained appendix on several key relevant notions

Cognitive Dynamic Systems

Author: Simon Haykin
Publisher: Cambridge University Press
ISBN: 0521114365
Size: 25.87 MB
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A groundbreaking book from Simon Haykin, setting out the fundamental ideas and highlighting a range of future research directions.

Analog Vlsi Neural Networks

Author: Yoshiyasu Takefuji
Publisher: Springer Science & Business Media
ISBN: 1461535824
Size: 19.35 MB
Format: PDF, ePub
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This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.

Robust Adaptive Control

Author: Petros Ioannou
Publisher: Courier Corporation
ISBN: 0486320723
Size: 60.72 MB
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Presented in a tutorial style, this comprehensive treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Numerous examples clarify procedures and methods. 1995 edition.

Handbook On Array Processing And Sensor Networks

Author: Simon Haykin
Publisher: John Wiley & Sons
ISBN: 9780470487051
Size: 77.75 MB
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A handbook on recent advancements and the state of the art in array processing and sensor Networks Handbook on Array Processing and Sensor Networks provides readers with a collection of tutorial articles contributed by world-renowned experts on recent advancements and the state of the art in array processing and sensor networks. Focusing on fundamental principles as well as applications, the handbook provides exhaustive coverage of: wavelets; spatial spectrum estimation; MIMO radio propagation; robustness issues in sensor array processing; wireless communications and sensing in multi-path environments using multi-antenna transceivers; implicit training and array processing for digital communications systems; unitary design of radar waveform diversity sets; acoustic array processing for speech enhancement; acoustic beamforming for hearing aid applications; undetermined blind source separation using acoustic arrays; array processing in astronomy; digital 3D/4D ultrasound imaging technology; self-localization of sensor networks; multi-target tracking and classification in collaborative sensor networks via sequential Monte Carlo; energy-efficient decentralized estimation; sensor data fusion with application to multi-target tracking; distributed algorithms in sensor networks; cooperative communications; distributed source coding; network coding for sensor networks; information-theoretic studies of wireless networks; distributed adaptive learning mechanisms; routing for statistical inference in sensor networks; spectrum estimation in cognitive radios; nonparametric techniques for pedestrian tracking in wireless local area networks; signal processing and networking via the theory of global games; biochemical transport modeling, estimation, and detection in realistic environments; and security and privacy for sensor networks. Handbook on Array Processing and Sensor Networks is the first book of its kind and will appeal to researchers, professors, and graduate students in array processing, sensor networks, advanced signal processing, and networking.