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| random signal analysis: Random Signal Analysis in Engineering Systems John Komo, 2012-12-02 Random Signal Analysis in Engineering Systems covers the concepts of probability, random variables, averages, simulation, and random signals. The book discusses set theory and probability; random variables and vectors; and the functions of random variables. The text also describes the statistical averages; simulation; statistical inference; and random processes. Undergraduate engineering students will find the book useful. |
| random signal analysis: Random Signal Analysis Jie Yang, Congfeng Liu, 2018-09-10 A fundamental introduction to the delopment of random signal processing with an emphasis on analysis. Linear transformation, nonlinear transformation, spectral analysis of stationary and narrow band random process are discussed in detail. With abundant exercises, this book is an essential reference for graduate students, scientists and practitioners in electronical engineering and signal processing. |
| random signal analysis: Random Signal Processing Dwight F. Mix, 1995 Providing detailed coverage of Wiener filtering and Kalman filtering, this book presents a coherent treatment of estimation theory and an in-depth look at detection theory for communication and pattern recognition. |
| random signal analysis: Random Signal Processing Shaila Dinkar Apte, 2017-08-15 This book covers random signals and random processes along with estimation of probability density function, estimation of energy spectral density and power spectral density. The properties of random processes and signal modelling are discussed with basic communication theory estimation and detection. MATLAB simulations are included for each concept with output of the program with case studies and project ideas. The chapters progressively introduce and explain the concepts of random signals and cover multiple applications for signal processing. The book is designed to cater to a wide audience starting from the undergraduates (electronics, electrical, instrumentation, computer, and telecommunication engineering) to the researchers working in the pertinent fields. Key Features: • Aimed at random signal processing with parametric signal processing-using appropriate segment size. • Covers speech, image, medical images, EEG and ECG signal processing. • Reviews optimal detection and estimation. • Discusses parametric modeling and signal processing in transform domain. • Includes MATLAB codes and relevant exercises, case studies and solved examples including multiple choice questions |
| random signal analysis: Principles of Random Signal Analysis and Low Noise Design Roy M. Howard, 2004-08-18 Describes the leading techniques for analyzing noise. Discusses methods that are applicable to periodic signals,aperiodic signals, or random processes over finite or infiniteintervals. Provides readers with a useful reference when designing ormodeling communications systems. |
| random signal analysis: Introduction to Random Signals and Noise Wim C. Van Etten, 2006-02-03 Random signals and noise are present in many engineering systems and networks. Signal processing techniques allow engineers to distinguish between useful signals in audio, video or communication equipment, and interference, which disturbs the desired signal. With a strong mathematical grounding, this text provides a clear introduction to the fundamentals of stochastic processes and their practical applications to random signals and noise. With worked examples, problems, and detailed appendices, Introduction to Random Signals and Noise gives the reader the knowledge to design optimum systems for effectively coping with unwanted signals. Key features: Considers a wide range of signals and noise, including analogue, discrete-time and bandpass signals in both time and frequency domains. Analyses the basics of digital signal detection using matched filtering, signal space representation and correlation receiver. Examines optimal filtering methods and their consequences. Presents a detailed discussion of the topic of Poisson processes and shot noise. An excellent resource for professional engineers developing communication systems, semiconductor devices, and audio and video equipment, this book is also ideal for senior undergraduate and graduate students in Electronic and Electrical Engineering. |
| random signal analysis: Probability and Random Processes Scott Miller, Donald Childers, 2012-01-11 Miller and Childers have focused on creating a clear presentation of foundational concepts with specific applications to signal processing and communications, clearly the two areas of most interest to students and instructors in this course. It is aimed at graduate students as well as practicing engineers, and includes unique chapters on narrowband random processes and simulation techniques. The appendices provide a refresher in such areas as linear algebra, set theory, random variables, and more. Probability and Random Processes also includes applications in digital communications, information theory, coding theory, image processing, speech analysis, synthesis and recognition, and other fields. * Exceptional exposition and numerous worked out problems make the book extremely readable and accessible * The authors connect the applications discussed in class to the textbook * The new edition contains more real world signal processing and communications applications * Includes an entire chapter devoted to simulation techniques. |
| random signal analysis: An Introduction to Statistical Signal Processing Robert M. Gray, Lee D. Davisson, 2004-12-02 This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications. |
| random signal analysis: Introduction to Random Signal Analysis and Kalman Filtering Robert Grover Brown, 1983 Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine. |
| random signal analysis: Discrete Random Signal Processing and Filtering Primer with MATLAB Alexander D. Poularikas, 2018-10-03 Engineers in all fields will appreciate a practical guide that combines several new effective MATLAB® problem-solving approaches and the very latest in discrete random signal processing and filtering. Numerous Useful Examples, Problems, and Solutions – An Extensive and Powerful Review Written for practicing engineers seeking to strengthen their practical grasp of random signal processing, Discrete Random Signal Processing and Filtering Primer with MATLAB provides the opportunity to doubly enhance their skills. The author, a leading expert in the field of electrical and computer engineering, offers a solid review of recent developments in discrete signal processing. The book also details the latest progress in the revolutionary MATLAB language. A Practical Self-Tutorial That Transcends TheoryThe author introduces an incremental discussion of signal processing and filtering, and presents several new methods that can be used for a more dynamic analysis of random digital signals with both linear and non-linear filtering. Ideal as a self-tutorial, this book includes numerous examples and functions, which can be used to select parameters, perform simulations, and analyze results. This concise guide encourages readers to use MATLAB functions – and those new ones introduced as Book MATLAB Functions – to substitute many different combinations of parameters, giving them a firm grasp of how much each parameter affects results. Much more than a simple review of theory, this book emphasizes problem solving and result analysis, enabling readers to take a hands-on approach to advance their own understanding of MATLAB and the way it is used within signal processing and filtering. |
| random signal analysis: Signal Analysis Ronald L. Allen, Duncan Mills, 2004-06-07 Offers a well-rounded, mathematical approach to problems in signal interpretation using the latest time, frequency, and mixed-domain methods Equally useful as a reference, an up-to-date review, a learning tool, and a resource for signal analysis techniques Provides a gradual introduction to the mathematics so that the less mathematically adept reader will not be overwhelmed with instant hard analysis Covers Hilbert spaces, complex analysis, distributions, random signals, analog Fourier transforms, and more |
| random signal analysis: Discrete Random Signals and Statistical Signal Processing Charles W. Therrien, 1992 |
| random signal analysis: A First Course in Statistics for Signal Analysis Wojbor A. Woyczyński, 2019-10-04 This self-contained and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, which are explained in a concise, yet rigorous presentation. With abundant practice exercises and thorough explanations, A First Course in Statistics for Signal Analysis is an excellent tool for both teaching students and training laboratory scientists and engineers. Improvements in the second edition include considerably expanded sections, enhanced precision, and more illustrative figures. |
| random signal analysis: Probability, Random Processes, and Statistical Analysis Hisashi Kobayashi, Brian L. Mark, William Turin, 2011-12-15 Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Itô process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum–Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals. |
| random signal analysis: Digital Processing of Random Signals Boaz Porat, 1994 |
| random signal analysis: Statistical Signal Processing Swagata Nandi, Debasis Kundu, 2020-08-21 This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics. |
| random signal analysis: Random Signals and Systems Bernard Picinbono, 1993 A presentation of random signals and systems focusing on applications often encountered in practice. It makes use of geometrical methods, contains a systematic presentation of covariance matrices, and includes a discussion of Gaussian complex random vectors. |
| random signal analysis: Pseudo Random Signal Processing Hans-Jurgen Zepernick, Adolf Finger, 2013-07-17 In recent years, pseudo random signal processing has proven to be a critical enabler of modern communication, information, security and measurement systems. The signal’s pseudo random, noise-like properties make it vitally important as a tool for protecting against interference, alleviating multipath propagation and allowing the potential of sharing bandwidth with other users. Taking a practical approach to the topic, this text provides a comprehensive and systematic guide to understanding and using pseudo random signals. Covering theoretical principles, design methodologies and applications, Pseudo Random Signal Processing: Theory and Application: sets out the mathematical foundations needed to implement powerful pseudo random signal processing techniques; presents information about binary and nonbinary pseudo random sequence generation and design objectives; examines the creation of system architectures, including those with microprocessors, digital signal processors, memory circuits and software suits; gives a detailed discussion of sophisticated applications such as spread spectrum communications, ranging and satellite navigation systems, scrambling, system verification, and sensor and optical fibre systems. Pseudo Random Signal Processing: Theory and Applicationis an essential introduction to the subject for practising Electronics Engineers and researchers in the fields of mobile communications, satellite navigation, signal analysis, circuit testing, cryptology, watermarking, and measurement. It is also a useful reference for graduate students taking courses in Electronics, Communications and Computer Engineering. |
| random signal analysis: Random Processes for Engineers Bruce Hajek, 2015-03-12 This engaging introduction to random processes provides students with the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. A brief review of probability theory and real analysis of deterministic functions sets the stage for understanding random processes, whilst the underlying measure theoretic notions are explained in an intuitive, straightforward style. Students will learn to manage the complexity of randomness through the use of simple classes of random processes, statistical means and correlations, asymptotic analysis, sampling, and effective algorithms. Key topics covered include: • Calculus of random processes in linear systems • Kalman and Wiener filtering • Hidden Markov models for statistical inference • The estimation maximization (EM) algorithm • An introduction to martingales and concentration inequalities. Understanding of the key concepts is reinforced through over 100 worked examples and 300 thoroughly tested homework problems (half of which are solved in detail at the end of the book). |
| random signal analysis: Random Signal Analysis Ali Abedi, 2012-12-16 This book introduces the fundamental concepts of random signal analysis based on probability theory and random processes. It presents the mathematical and engineering tools to analyze and interpret random events occurring in natural phenomena, games, sciences, and engineering. Specifically, practical examples from electrical and computer systems are studied in depth. This book is designed to motivate and inspire students to learn concepts of random signal analysis and pursue follow up courses in communications and signal processing areas. |
| random signal analysis: Statistical Signal Processing in Engineering Umberto Spagnolini, 2017-12-13 A problem-solving approach to statistical signal processing for practicing engineers, technicians, and graduate students This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals. In writing it, the author drew on his vast theoretical and practical experience in the field to provide a quick-solution manual for technicians and engineers, offering field-tested solutions to most problems engineers can encounter. At the same time, the book delineates the basic concepts and applied mathematics underlying each solution so that readers can go deeper into the theory to gain a better idea of the solution’s limitations and potential pitfalls, and thus tailor the best solution for the specific engineering application. Uniquely, Statistical Signal Processing in Engineering can also function as a textbook for engineering graduates and post-graduates. Dr. Spagnolini, who has had a quarter of a century of experience teaching graduate-level courses in digital and statistical signal processing methods, provides a detailed axiomatic presentation of the conceptual and mathematical foundations of statistical signal processing that will challenge students’ analytical skills and motivate them to develop new applications on their own, or better understand the motivation underlining the existing solutions. Throughout the book, some real-world examples demonstrate how powerful a tool statistical signal processing is in practice across a wide range of applications. Takes an interdisciplinary approach, integrating basic concepts and tools for statistical signal processing Informed by its author’s vast experience as both a practitioner and teacher Offers a hands-on approach to solving problems in statistical signal processing Covers a broad range of applications, including communication systems, machine learning, wavefield and array processing, remote sensing, image filtering and distributed computations Features numerous real-world examples from a wide range of applications showing the mathematical concepts involved in practice Includes MATLAB code of many of the experiments in the book Statistical Signal Processing in Engineering is an indispensable working resource for electrical engineers, especially those working in the information and communication technology (ICT) industry. It is also an ideal text for engineering students at large, applied mathematics post-graduates and advanced undergraduates in electrical engineering, applied statistics, and pure mathematics, studying statistical signal processing. |
| random signal analysis: Digital Signal Processing and Statistical Classification George J. Miao, Mark A. Clements, 2002 This is the first book to introduce and integrate advanced digital signal processing (DSP) and classification together, and the only volume to introduce state-of-the-art transforms including DFT, FFT, DCT, DHT, PCT, CDT, and ODT together for DSP and communication applications. You get step-by-step guidance in discrete-time domain signal processing and frequency domain signal analysis; digital filter design and adaptive filtering; multirate digital processing; and statistical signal classification. It also helps you overcome problems associated with multirate A/D and D/A converters. |
| random signal analysis: Signal Analysis Athanasios Papoulis, 2018 |
| random signal analysis: Stochastic Signal Processing Johann Frederic Böhme, Pei-Jung Chung, 2012 This book intends to provide graduate students in electrical and information science a solid background in stochastic signal processing. Chapter one introduces random signals through measurement noise. Chapter two develops fundamental concepts in probability theory and statistical methods. Chapter three is devoted to stochastic processes, stochastic system theory, and statistical signal processing. The examples are carefully selected. Some of them are aimed at motivating students interested in advanced topics such as signal detection, estimation, spectral analysis and system identification. Problems with solutions and MATLAB exercises are included to encourage self study by researchers or engineers in related areas. The most important concepts in statistics are presented so that linear systems and nonlinear ones as rectifiers with random input and output signals have proper mathematical description and allow statistical inference. Such systems are fundamental to many engineering areas, for example, electronics, measurements, communications and control. |
| random signal analysis: Random Signal Analysis Jie Yang, Congfeng Liu, 2018-09-10 A fundamental introduction to the delopment of random signal processing with an emphasis on analysis. Linear transformation, nonlinear transformation, spectral analysis of stationary and narrow band random process are discussed in detail. With abundant exercises, this book is an essential reference for graduate students, scientists and practitioners in electronical engineering and signal processing. |
| random signal analysis: Signal Processing and Data Analysis Tianshuang Qiu, Ying Guo, 2018-07-09 This book presents digital signal processing theories and methods and their applications in data analysis, error analysis and statistical signal processing. Algorithms and Matlab programming are included to guide readers step by step in dealing with practical difficulties. Designed in a self-contained way, the book is suitable for graduate students in electrical engineering, information science and engineering in general. |
| random signal analysis: Biomedical Signal Analysis Rangaraj M. Rangayyan, 2015-04-24 The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications 800 mathematical expressions and equations Practical questions, problems and laboratory exercises Includes fractals and chaos theory with biomedical applications |
| random signal analysis: RANDOM PROCESSES: FILTERING, ESTIMATION AND DETECTION Lonnie C. Ludeman, 2010-07-01 Market_Desc: Graduate students of electrical and computer engineering. Practicing engineers in communications and signal processing. Special Features: Covers modern detection and estimation theory as well as the basics of random processes Emphasizes the use of discrete-time Weiner and Kalman filters and covers nonlinear systems in detail Includes over 380 class-tested homework exercises About The Book: An understanding of random processes is crucial in the study of many engineering systems, for example analyzing noise in a wireless communications channel. This book covers the basics of probability and random processes for an engineering audience. Importantly, though, the book also presents the details of modern detection and estimation theory, giving it a real edge over existing textbooks. The author has a proven track record. His book Fundamentals of Digital Signal Processing has sold 15,000 copies and won Choice magazine's Outstanding Engineering Book of the Year award. |
| random signal analysis: Random Vibrations Paul H. Wirsching, Thomas L. Paez, Keith Ortiz, 2006-01-01 The most comprehensive text and reference available on the study of random vibrations, this book was designed for graduate students and mechanical, structural, and aerospace engineers. In addition to coverage of background topics in probability, statistics, and random processes, it develops methods for analyzing and controlling random vibrations. 1995 edition. |
| random signal analysis: Geophysical Signal Analysis Enders A. Robinson, Sven Treitel, 2000 Addresses the construction, analysis, and interpretation of mathematical and statistical models. The practical use of the concepts and techniques developed is illustrated by numerous applications. The chosen examples will interest many readers, including those engaged in digital signal analysis in disciplines other than geophysics. |
| random signal analysis: Statistical Signal Processing Debasis Kundu, Swagata Nandi, 2012-05-24 Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research. |
| random signal analysis: Nonlinear Signal Processing Gonzalo R. Arce, 2005-01-03 Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed that encompass a large portion of the nonlinear signal processing tools proposed in the literature over the past several decades. Key features include: * Numerous problems at the end of each chapter to aid development and understanding * Examples and case studies provided throughout the book in a wide range of applications bring the text to life and place the theory into context * A set of 60+ MATLAB software m-files allowing the reader to quickly design and apply any of the nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site. |
| random signal analysis: Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions Robert Grover Brown, Patrick Y. C. Hwang, 1997 In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work. |
| random signal analysis: Statistical Signal Processing of Complex-Valued Data Peter J. Schreier, Louis L. Scharf, 2010-02-04 Complex-valued random signals are embedded in the very fabric of science and engineering, yet the usual assumptions made about their statistical behavior are often a poor representation of the underlying physics. This book deals with improper and noncircular complex signals, which do not conform to classical assumptions, and it demonstrates how correct treatment of these signals can have significant payoffs. The book begins with detailed coverage of the fundamental theory and presents a variety of tools and algorithms for dealing with improper and noncircular signals. It provides a comprehensive account of the main applications, covering detection, estimation, and signal analysis of stationary, nonstationary, and cyclostationary processes. Providing a systematic development from the origin of complex signals to their probabilistic description makes the theory accessible to newcomers. This book is ideal for graduate students and researchers working with complex data in a range of research areas from communications to oceanography. |
| random signal analysis: Probabilistic Methods of Signal and System Analysis George R. Cooper, Clare D. McGillem, 1999 Probabilistic Methods of Signal and System Analysis, 3/e stresses the engineering applications of probability theory, presenting the material at a level and in a manner ideally suited to engineering students at the junior or senior level. It is also useful as a review for graduate students and practicing engineers. Thoroughly revised and updated, this third edition incorporates increased use of the computer in both text examples and selected problems. It utilizes MATLAB as a computational tool and includes new sections relating to Bernoulli trials, correlation of data sets, smoothing of data, computer computation of correlation functions and spectral densities, and computer simulation of systems. All computer examples can be run using the Student Version of MATLAB. Almost all of the examples and many of the problems have been modified or changed entirely, and a number of new problems have been added. A separate appendix discusses and illustrates the application of computers to signal and system analysis. |
| random signal analysis: Signal Theory and Random Processes Harry Urkowitz, 1983 |
| random signal analysis: Applied Signal Processing Sadasivan Puthusserypady, 2021-03-31 Being an inter-disciplinary subject, Signal Processing has application in almost all scientific fields. Applied Signal Processing tries to link between the analog and digital signal processing domains. Since the digital signal processing techniques have evolved from its analog counterpart, this book begins by explaining the fundamental concepts in analog signal processing and then progresses towards the digital signal processing. This will help the reader to gain a general overview of the whole subject and establish links between the various fundamental concepts. While the focus of this book is on the fundamentals of signal processing, the understanding of these topics greatly enhances the confident use as well as further development of the design and analysis of digital systems for various engineering and medical applications. Applied Signal Processing also prepares readers to further their knowledge in advanced topics within the field of signal processing. |
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