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Digital Spectral Analysis

Digital Spectral Analysis
Author: S. Lawrence Marple, Jr.
Publisher: Courier Dover Publications
Total Pages: 435
Release: 2019-03-20
Genre: Technology & Engineering
ISBN: 0486838862
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Designed to offer a broad perspective on spectral estimations techniques and their implementation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. 1987 edition.

Digital Signal Processing and Spectral Analysis for Scientists

Digital Signal Processing and Spectral Analysis for Scientists
Author: Silvia Maria Alessio
Publisher: Springer
Total Pages: 909
Release: 2015-12-09
Genre: Technology & Engineering
ISBN: 3319254685
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This book covers the basics of processing and spectral analysis of monovariate discrete-time signals. The approach is practical, the aim being to acquaint the reader with the indications for and drawbacks of the various methods and to highlight possible misuses. The book is rich in original ideas, visualized in new and illuminating ways, and is structured so that parts can be skipped without loss of continuity. Many examples are included, based on synthetic data and real measurements from the fields of physics, biology, medicine, macroeconomics etc., and a complete set of MATLAB exercises requiring no previous experience of programming is provided. Prior advanced mathematical skills are not needed in order to understand the contents: a good command of basic mathematical analysis is sufficient. Where more advanced mathematical tools are necessary, they are included in an Appendix and presented in an easy-to-follow way. With this book, digital signal processing leaves the domain of engineering to address the needs of scientists and scholars in traditionally less quantitative disciplines, now facing increasing amounts of data.

The Spectral Analysis of Time Series

The Spectral Analysis of Time Series
Author: L. H. Koopmans
Publisher: Academic Press
Total Pages: 383
Release: 2014-05-12
Genre: Mathematics
ISBN: 1483218546
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The Spectral Analysis of Time Series describes the techniques and theory of the frequency domain analysis of time series. The book discusses the physical processes and the basic features of models of time series. The central feature of all models is the existence of a spectrum by which the time series is decomposed into a linear combination of sines and cosines. The investigator can used Fourier decompositions or other kinds of spectrals in time series analysis. The text explains the Wiener theory of spectral analysis, the spectral representation for weakly stationary stochastic processes, and the real spectral representation. The book also discusses sampling, aliasing, discrete-time models, linear filters that have general properties with applications to continuous-time processes, and the applications of multivariate spectral models. The text describes finite parameter models, the distribution theory of spectral estimates with applications to statistical inference, as well as sampling properties of spectral estimates, experimental design, and spectral computations. The book is intended either as a textbook or for individual reading for one-semester or two-quarter course for students of time series analysis users. It is also suitable for mathematicians or professors of calculus, statistics, and advanced mathematics.

Digital Spectral Analysis

Digital Spectral Analysis
Author: Francis Castanié
Publisher: John Wiley & Sons
Total Pages: 297
Release: 2013-02-04
Genre: Mathematics
ISBN: 1118601831
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Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution methods are detailed in a further four chapters: spectral analysis by stationary time series modeling, minimum variance, and subspace-based estimators. Finally, advanced concepts are the core of the last four chapters: spectral analysis of non-stationary random signals, space time adaptive processing: irregularly sampled data processing, particle filtering and tracking of varying sinusoids. Suitable for students, engineers working in industry, and academics at any level, this book provides a rare complete overview of the spectral analysis domain.

Digital Spectral Analysis MATLAB® Software User Guide

Digital Spectral Analysis MATLAB® Software User Guide
Author: S. Lawrence Marple, Jr.
Publisher: Courier Dover Publications
Total Pages: 130
Release: 2019-05-15
Genre: Technology & Engineering
ISBN: 0486837386
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This user guide serves as a companion to Digital Spectral Analysis, Second Edition (Dover Publications, 2019), illustrating all the text's techniques and algorithms, plus time versus frequency analysis. The spectral demonstrations use MATLAB software that encompasses the full experience from inputting signal sources, interactively setting technique parameters and processing with those parameters, and choosing from a variety of plotting techniques to display the results. The processing functions and scripts have been coded to automatically handle sample data that is either real-valued or complex-valued, permitting the user to simply modify the demonstration scripts to input their own data for analysis. Four integrated software categories support the demonstrations. These are the main MATLAB spectral demonstration scripts, supporting MATLAB plotting scripts, MATLAB processing functions listed in this guide, and signal sample data sources. Scripts and demonstration data files can be found on the Dover website for free downloading; see the Introduction for details.

Spectral Analysis for Physical Applications

Spectral Analysis for Physical Applications
Author: Donald B. Percival
Publisher: Cambridge University Press
Total Pages: 616
Release: 1993-06-03
Genre: Mathematics
ISBN: 9780521435413
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This book is an up-to-date introduction to univariate spectral analysis at the graduate level, which reflects a new scientific awareness of spectral complexity, as well as the widespread use of spectral analysis on digital computers with considerable computational power. The text provides theoretical and computational guidance on the available techniques, emphasizing those that work in practice. Spectral analysis finds extensive application in the analysis of data arising in many of the physical sciences, ranging from electrical engineering and physics to geophysics and oceanography. A valuable feature of the text is that many examples are given showing the application of spectral analysis to real data sets. Special emphasis is placed on the multitaper technique, because of its practical success in handling spectra with intricate structure, and its power to handle data with or without spectral lines. The text contains a large number of exercises, together with an extensive bibliography.

Digital Spectral Analysis

Digital Spectral Analysis
Author: Anthony J. Villasenor
Publisher:
Total Pages: 60
Release: 1968
Genre: Power spectra
ISBN:
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Spectral Analysis and Its Applications

Spectral Analysis and Its Applications
Author: Gwilym M. Jenkins
Publisher: Emerson Adams PressInc
Total Pages: 525
Release: 1968
Genre: Science
ISBN: 9781892803030
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Spectral Analysis

Spectral Analysis
Author: Francis Castanié
Publisher: John Wiley & Sons
Total Pages: 186
Release: 2013-03-01
Genre: Technology & Engineering
ISBN: 1118614275
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This book deals with these parametric methods, first discussing those based on time series models, Capon’s method and its variants, and then estimators based on the notions of sub-spaces. However, the book also deals with the traditional “analog” methods, now called non-parametric methods, which are still the most widely used in practical spectral analysis.

Spectral analysis methods for noisy sampled-data systems

Spectral analysis methods for noisy sampled-data systems
Author: Steve F. Russell
Publisher: Steve F. Russell
Total Pages: 500
Release: 1978-08-15
Genre: Technology & Engineering
ISBN:
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This dissertation covers both the theory and practice of estimating the spectrum of signals in noise using digital data. The theory of describing some of the signal processing concepts for digital data are given and various spectral estimation methods are given. The theory of MEM is described in detail using approaches from estimation theory, communication theory, and statistics. The work was intended to give researchers the theory and practice of practical means of spectral estimation using communications or scientific data. The Maximum Entropy Method by John Parker Burg is explained from what was known in 1974-75. KEY WORDS: Calculus-of-Variations, Data Systems, Noise , Spectrum Analysis, Time Series Analysis, Autocorrelation, Computer Programs, Data Windowing, Ergodic Process, Maximum Entropy Method (MEM, Fourier Transformation, Optimum Order of Estimation, Sampling, Spectral Resolution, Statistical Significance Test, Systems Analysis, Wiener-Khinchine Theorem. From The Smithsonian/NASA Astrophysics Data System -- The practical aspects of spectral analysis are contrasted with the mathematical theory. Treatment is limited to ergodic processes and emphasizes data window and noise effects. The Discrete Fourier Transform (DFT) and Maximum Entropy Method (MEM) are covered extensively both in theory and application with FORTRAN programs and many examples being provided. Several of the chapters are tutorial and discuss the important topics of sampling theory and system analysis. Topics on MEM include a complete calculus-of-variations solution, relationship between MEM and the Wiener-Khinchine relations, spectral resolution, and choosing the optimum order of the estimation. DFT leakage effects are modeled. A statistical significance test was developed to determine the realness of a spectral component. Keywords: Data Systems, Noise (Sound), Spectrum Analysis, Time Series Analysis, Autocorrelation, Computer Programs, Ergodic Process, Fourier Transformation, Sampling, Systems Analysis [less]