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Fundamentals Of Statistical Processing Volume 2 Detection Theory

Fundamentals Of Statistical Processing  Volume 2  Detection Theory PDF

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Author: Steven M. Kay
Publisher: Pearson Education India
ISBN: 9788131729007
Size: 38.58 MB
Format: PDF
Category : Estimation theory
Languages : en
Pages : 672
View: 5660


"For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Authoer Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples."--Cover, volume 1.

Fundamentals Of Statistical Signal Processing Detection Theory

Fundamentals of Statistical Signal Processing  Detection theory PDF

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Author: Steven M. Kay
Publisher: Prentice Hall
ISBN:
Size: 17.38 MB
Format: PDF, ePub, Docs
Category : Technology & Engineering
Languages : en
Pages : 560
View: 4827


V.2 Detection theory -- V.1 Estimation theory.

Fundamentals Of Statistical Signal Processing Volume Iii

Fundamentals of Statistical Signal Processing  Volume III PDF

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Author: Steven M. Kay
Publisher: Prentice Hall
ISBN: 0132808064
Size: 12.26 MB
Format: PDF, ePub, Docs
Category : Technology & Engineering
Languages : en
Pages : 496
View: 5256


The Complete, Modern Guide to Developing Well-Performing Signal Processing Algorithms In Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, author Steven M. Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers. This final volume of Kay’s three-volume guide builds on the comprehensive theoretical coverage in the first two volumes. Here, Kay helps readers develop strong intuition and expertise in designing well-performing algorithms that solve real-world problems. Kay begins by reviewing methodologies for developing signal processing algorithms, including mathematical modeling, computer simulation, and performance evaluation. He links concepts to practice by presenting useful analytical results and implementations for design, evaluation, and testing. Next, he highlights specific algorithms that have “stood the test of time,” offers realistic examples from several key application areas, and introduces useful extensions. Finally, he guides readers through translating mathematical algorithms into MATLAB® code and verifying solutions. Topics covered include Step by step approach to the design of algorithms Comparing and choosing signal and noise models Performance evaluation, metrics, tradeoffs, testing, and documentation Optimal approaches using the “big theorems” Algorithms for estimation, detection, and spectral estimation Complete case studies: Radar Doppler center frequency estimation, magnetic signal detection, and heart rate monitoring Exercises are presented throughout, with full solutions. This new volume is invaluable to engineers, scientists, and advanced students in every discipline that relies on signal processing; researchers will especially appreciate its timely overview of the state of the practical art. Volume III complements Dr. Kay’s Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (Prentice Hall, 1993; ISBN-13: 978-0-13-345711-7), and Volume II: Detection Theory (Prentice Hall, 1998; ISBN-13: 978-0-13-504135-2).

Fundamentals Of Statistical Signal Processing

Fundamentals of Statistical Signal Processing PDF

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Author: Steven Kay
Publisher:
ISBN:
Size: 57.99 MB
Format: PDF, Kindle
Category : Engineering
Languages : en
Pages : 496
View: 7030


The Complete, Modern Guide to Developing Well-Performing Signal Processing Algorithms In Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, author Steven M. Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers. This final volume of Kay's three-volume guide builds on the comprehensive theoretical coverage in the first two volumes. Here, Kay helps readers develop strong intuition and expertise in designing well-performing algorithms that solve real-world problems. Kay begins by reviewing methodologies for developing signal processing algorithms, including mathematical modeling, computer simulation, and performance evaluation. He links concepts to practice by presenting useful analytical results and implementations for design, evaluation, and testing. Next, he highlights specific algorithms that have "stood the test of time," offers realistic examples from several key application areas, and introduces useful extensions. Finally, he guides readers through translating mathematical algorithms into MATLAB® code and verifying solutions. Topics covered include Step by step approach to the design of algorithms Comparing and choosing signal and noise models Performance evaluation, metrics, tradeoffs, testing, and documentation Optimal approaches using the "big theorems" Algorithms for estimation, detection, and spectral estimation Complete case studies: Radar Doppler center frequency estimation, magnetic signal detection, and heart rate monitoring Exercises are presented throughout, with full solutions. This new volume is invaluable to engineers, scientists, and advanced students in every discipline that relies on signal processing; researchers will especially appreciate its timely overview of the state of the practical art. Volume III complements Dr. Kay's Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (Prentice Hall, 1993; ISBN-13: 978-0-13-345711-7), and Volume II: Detection Theory (Prentice Hall, 1998; ISBN-13: 978-0-13-504135-2).

Stochastische Signale

Stochastische Signale PDF

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Author: Johann Frederic Böhme
Publisher: Springer-Verlag
ISBN: 3663079597
Size: 46.68 MB
Format: PDF, ePub
Category : Technology & Engineering
Languages : de
Pages : 285
View: 7585


Das Buch wendet sich an Leser, die sich gründlich in die Theorie stochastischer Signale einarbeiten wollen. Im ersten Teil werden in einem Grundkurs die notwendigen Werkzeuge der Stochastik erarbeitet, im zweiten Teil stochastische Signale behandelt. Zahlreiche Beispiele, Übungsaufgaben und Lösungsskizzen, Anhänge, Tabellen für Standardverteilungen und ein Praktikum in MATLAB mit Simulationsaufgaben erleichtern das Selbststudium und die Anwendungen in der Praxis.

Zeitdiskrete Signalverarbeitung

Zeitdiskrete Signalverarbeitung PDF

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Author: Alan V. Oppenheim
Publisher:
ISBN: 9783827370778
Size: 41.27 MB
Format: PDF
Category : Signal processing
Languages : en
Pages : 1031
View: 3286


Neuübersetzung der aktuelle US-Ausgabe.

Digital Signal Processing Fundamentals

Digital Signal Processing Fundamentals PDF

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Author: Vijay Madisetti
Publisher: CRC Press
ISBN: 1351835726
Size: 21.54 MB
Format: PDF, Mobi
Category : Technology & Engineering
Languages : en
Pages : 904
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Now available in a three-volume set, this updated and expanded edition of the bestselling The Digital Signal Processing Handbook continues to provide the engineering community with authoritative coverage of the fundamental and specialized aspects of information-bearing signals in digital form. Encompassing essential background material, technical details, standards, and software, the second edition reflects cutting-edge information on signal processing algorithms and protocols related to speech, audio, multimedia, and video processing technology associated with standards ranging from WiMax to MP3 audio, low-power/high-performance DSPs, color image processing, and chips on video. Drawing on the experience of leading engineers, researchers, and scholars, the three-volume set contains 29 new chapters that address multimedia and Internet technologies, tomography, radar systems, architecture, standards, and future applications in speech, acoustics, video, radar, and telecommunications. Emphasizing theoretical concepts, Digital Signal Processing Fundamentals provides comprehensive coverage of the basic foundations of DSP and includes the following parts: Signals and Systems; Signal Representation and Quantization; Fourier Transforms; Digital Filtering; Statistical Signal Processing; Adaptive Filtering; Inverse Problems and Signal Reconstruction; and Time–Frequency and Multirate Signal Processing.

Detection And Estimation For Communication And Radar Systems

Detection and Estimation for Communication and Radar Systems PDF

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Author: Kung Yao
Publisher: Cambridge University Press
ISBN: 0521766397
Size: 19.33 MB
Format: PDF, Mobi
Category : Technology & Engineering
Languages : en
Pages : 322
View: 4920


A systematic guide to detection and estimation theory and their applications in the design, implementation and optimization of real-world systems.

Signal Processing For Cognitive Radios

Signal Processing for Cognitive Radios PDF

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Author: Sudharman K. Jayaweera
Publisher: John Wiley & Sons
ISBN: 1118824849
Size: 36.90 MB
Format: PDF, Docs
Category : Technology & Engineering
Languages : en
Pages : 768
View: 7763


This book examines signal processing techniques for cognitive radios. The book is divided into three parts: Part I, is an introduction to cognitive radios and presents a history of the cognitive radio (CR), and introduce their architecture, functionalities, ideal aspects, hardware platforms, and state-of-the-art developments. Dr. Jayaweera also introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). Part II of the book, Theoretical Foundations, guides the reader from classical to modern theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation, classification, adaptive algorithms (machine learning), and inference and decision processes. Applications to the signal processing, inference and learning problems encountered in cognitive radios are interspersed throughout with concrete and accessible examples. Part III of the book, Signal Processing in Radios, identifies the key signal processing, inference, and learning tasks to be performed by wideband autonomous cognitive radios. The author provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios.

Ultra Wideband Communications

Ultra Wideband Communications PDF

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Author: Mohammad Abdul Matin
Publisher: BoD – Books on Demand
ISBN: 9533074612
Size: 20.59 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 362
View: 3185


This book has addressed few challenges to ensure the success of UWB technologies and covers several research areas including UWB low cost transceiver, low noise amplifier (LNA), ADC architectures, UWB filter, and high power UWB amplifiers. It is believed that this book serves as a comprehensive reference for graduate students in UWB technologies.

Robust And Distributed Hypothesis Testing

Robust and Distributed Hypothesis Testing PDF

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Author: Gökhan Gül
Publisher: Springer
ISBN: 3319492861
Size: 13.18 MB
Format: PDF, ePub
Category : Technology & Engineering
Languages : en
Pages : 141
View: 5814


This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the book describes the derivation of theoretical bounds in minimax decentralized hypothesis testing, which have not yet been known. As a timely report on the state-of-the-art in robust hypothesis testing, this book is mainly intended for postgraduates and researchers in the field of electrical and electronic engineering, statistics and applied probability. Moreover, it may be of interest for students and researchers working in the field of classification, pattern recognition and cognitive radio.

Fault Detection

Fault Detection PDF

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Author: Wei Zhang
Publisher: BoD – Books on Demand
ISBN: 9533070374
Size: 63.66 MB
Format: PDF, Kindle
Category : Technology & Engineering
Languages : en
Pages : 512
View: 2617


In this book, a number of innovative fault diagnosis algorithms in recently years are introduced. These methods can detect failures of various types of system effectively, and with a relatively high significance.

Wireless Sensor Networks

Wireless Sensor Networks PDF

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Author: Ananthram Swami
Publisher: John Wiley & Sons
ISBN: 9780470061817
Size: 68.75 MB
Format: PDF, ePub
Category : Technology & Engineering
Languages : en
Pages : 416
View: 3863


A wireless sensor network (WSN) uses a number of autonomous devices to cooperatively monitor physical or environmental conditions via a wireless network. Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection. Technological advancements, coupled with lowering costs, suggest that wireless sensor networks will have a significant impact on 21st century life. The design of wireless sensor networks requires consideration for several disciplines such as distributed signal processing, communications and cross-layer design. Wireless Sensor Networks: Signal Processing and Communications focuses on the theoretical aspects of wireless sensor networks and offers readers signal processing and communication perspectives on the design of large-scale networks. It explains state-of-the-art design theories and techniques to readers and places emphasis on the fundamental properties of large-scale sensor networks. Wireless Sensor Networks: Signal Processing and Communications : Approaches WSNs from a new angle – distributed signal processing, communication algorithms and novel cross-layer design paradigms. Applies ideas and illustrations from classical theory to an emerging field of WSN applications. Presents important analytical tools for use in the design of application-specific WSNs. Wireless Sensor Networks will be of use to signal processing and communications researchers and practitioners in applying classical theory to network design. It identifies research directions for senior undergraduate and graduate students and offers a rich bibliography for further reading and investigation.

Multi Factor Models And Signal Processing Techniques

Multi factor Models and Signal Processing Techniques PDF

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Author: Serges Darolles
Publisher: John Wiley & Sons
ISBN: 1118577493
Size: 41.61 MB
Format: PDF, ePub
Category : Technology & Engineering
Languages : en
Pages : 320
View: 2177


With recent outbreaks of multiple large-scale financial crises,amplified by interconnected risk sources, a new paradigm of fundmanagement has emerged. This new paradigm leverages“embedded” quantitative processes and methods toprovide more transparent, adaptive, reliable and easily implemented“risk assessment-based” practices. This book surveys the most widely used factor models employedwithin the field of financial asset pricing. Through the concreteapplication of evaluating risks in the hedge fund industry, theauthors demonstrate that signal processing techniques are aninteresting alternative to the selection of factors (bothfundamentals and statistical factors) and can provide moreefficient estimation procedures, based on lq regularized Kalmanfiltering for instance. With numerous illustrative examples from stock markets, this bookmeets the needs of both finance practitioners and graduate studentsin science, econometrics and finance. Contents Foreword, Rama Cont. 1. Factor Models and General Definition. 2. Factor Selection. 3. Least Squares Estimation (LSE) and Kalman Filtering (KF) forFactor Modeling: A Geometrical Perspective. 4. A Regularized Kalman Filter (rgKF) for Spiky Data. Appendix: Some Probability Densities. About the Authors Serge Darolles is Professor of Finance at Paris-DauphineUniversity, Vice-President of QuantValley, co-founder of QAMLabSAS, and member of the Quantitative Management Initiative (QMI)scientific committee. His research interests include financialeconometrics, liquidity and hedge fund analysis. He has writtennumerous articles, which have been published in academicjournals. Patrick Duvaut is currently the Research Director of TelecomParisTech, France. He is co-founder of QAMLab SAS, and member ofthe Quantitative Management Initiative (QMI) scientific committee.His fields of expertise encompass statistical signal processing,digital communications, embedded systems and QUANT finance. Emmanuelle Jay is co-founder and President of QAMLab SAS. She hasworked at Aequam Capital as co-head of R&D since April 2011 andis member of the Quantitative Management Initiative (QMI)scientific committee. Her research interests include SP forfinance, quantitative and statistical finance, and hedge fundanalysis.

Swarm Intelligence Methods For Statistical Regression

Swarm Intelligence Methods for Statistical Regression PDF

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Author: Soumya Mohanty
Publisher: CRC Press
ISBN: 1351365029
Size: 70.44 MB
Format: PDF, ePub, Mobi
Category : Business & Economics
Languages : en
Pages : 120
View: 5284


A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges

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