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Introduction To Applied Statistical Signal Analysis

Author: Richard Shiavi
Publisher: Academic Pr
ISBN:
Size: 73.28 MB
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This book provides a balanced perspective of the concept, mathematical bases, requirements for estimation, and detailed quantitative examples of the implementation of the techniques for classical signal analysis. The presentation integrates theory and implementation, practical examples, homework exercises which range from pencil and paper format to computer-based format problems, to instructional notebooks. The notebooks provide a mode of learning that is interactive and suited for self-pacing and independent learning. * "real-world" applications * real data available for exercises and projects * notebooks for interactive learning * graphical explanation of concepts * exercises emphasizing concepts * CD-ROM with MATLAB implementation

An Introduction To Statistical Signal Processing

Author: Robert M. Gray
Publisher: Cambridge University Press
ISBN: 9781139456289
Size: 25.22 MB
Format: PDF, ePub, Docs
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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.

Biosignal And Medical Image Processing

Author: John L. Semmlow
Publisher: CRC Press
ISBN: 0824750683
Size: 24.49 MB
Format: PDF, Kindle
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Relying heavily on MATLAB® problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications, providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts. The first and only textbook to supply a hands-on tutorial in biomedical signal and image processing, it offers a unique and proven approach to signal processing instruction, unlike any other competing source on the topic. The text is accompanied by a CD with support data files and software including all MATLAB examples and figures found in the text.

Discrete Random Signal Processing And Filtering Primer With Matlab

Author: Alexander D. Poularikas
Publisher: CRC Press
ISBN: 1351834452
Size: 23.89 MB
Format: PDF, ePub
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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 Theory The 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.

Digital And Statistical Signal Processing

Author: Anastasia Veloni
Publisher: CRC Press
ISBN: 042901757X
Size: 57.78 MB
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Nowadays, many aspects of electrical and electronic engineering are essentially applications of DSP. This is due to the focus on processing information in the form of digital signals, using certain DSP hardware designed to execute software. Fundamental topics in digital signal processing are introduced with theory, analytical tables, and applications with simulation tools. The book provides a collection of solved problems on digital signal processing and statistical signal processing. The solutions are based directly on the math-formulas given in extensive tables throughout the book, so the reader can solve practical problems on signal processing quickly and efficiently. FEATURES Explains how applications of DSP can be implemented in certain programming environments designed for real time systems, ex. biomedical signal analysis and medical image processing. Pairs theory with basic concepts and supporting analytical tables. Includes an extensive collection of solved problems throughout the text. Fosters the ability to solve practical problems on signal processing without focusing on extended theory. Covers the modeling process and addresses broader fundamental issues.

Statistical Signal Processing

Author: Debasis Kundu
Publisher: Springer Science & Business Media
ISBN: 8132206282
Size: 77.56 MB
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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.

Equine Locomotion

Author: Willem Back
Publisher: Elsevier Health Sciences
ISBN: 0702052930
Size: 61.58 MB
Format: PDF
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The first edition of Equine Locomotion has established itself as the book in the equine literature that discusses all aspects of equine locomotion and gait analysis, written by an international team of editors and contributors. The new edition continues this trend and gives the reader a complete picture of the horse in motion, at the same time including many recent findings in this area. The book begins with a history of man’s association with the horse and then continues to discuss with comprehensive descriptions of the present state of knowledge beginning with the initiation of gait and ending with the more scientific area of computer modeling. In the new edition, the list of contributors continues to comprise of authors who are acknowledged experts in their subject areas and includes many new illustrations. • international team of editors and contributors, with leading experts from the USA, the Netherlands, Sweden and France (all centres of excellence for the study of equine locomotion) • editors are from two of the worlds leading locomotion centres – Utrecht and Michigan • highly illustrated with nearly 500 detailed line drawings and illustrations • covers all you will ever need to know about equine locomotion, gait analysis and much more • international team of editors and contributors, with leading experts from the USA, the Netherlands, Sweden and France (all centres of excellence for the study of equine locomotion) • editors are from two of the worlds leading locomotion centres – Utrecht and Michigan • highly illustrated with nearly 500 detailed line drawings and illustrations • covers all you will ever need to know about equine locomotion, gait analysis and much more

Statistical Signal Processing For Neuroscience And Neurotechnology

Author: Karim G. Oweiss
Publisher: Academic Press
ISBN: 9780080962962
Size: 63.86 MB
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This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems. Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems

Statistical Signal Processing Of Complex Valued Data

Author: Peter J. Schreier
Publisher: Cambridge University Press
ISBN: 1139487620
Size: 46.27 MB
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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.

Nonlinear Dynamics And Statistics

Author: A. I. Mees
Publisher: Springer Science & Business Media
ISBN: 9780817641634
Size: 14.65 MB
Format: PDF, Mobi
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All models are lies. "The Earth orbits the sun in an ellipse with the sun at one focus" is false, but accurate enough for almost all purposes. This book describes the current state of the art of telling useful lies about time-varying systems in the real world. Specifically, it is about trying to "understand" (that is, tell useful lies about) dynamical systems directly from observa tions, either because they are too complex to model in the conventional way or because they are simply ill-understood. B(:cause it overlaps with conventional time-series analysis, building mod els of nonlinear dynamical systems directly from data has been seen by some observers as a somewhat ill-informed attempt to reinvent time-series analysis. The truth is distinctly less trivial. It is surely impossible, except in a few special cases, to re-create Newton's astonishing feat of writing a short equation that is an excellent description of real-world phenomena. Real systems are connected to the rest of the world; they are noisy, non stationary, and have high-dimensional dynamics; even when the dynamics contains lower-dimensional attractors there is almost never a coordinate system available in which these at tractors have a conventionally simple description.