Modern Spectrum Analysis
Author: Donald G. Childers
Publisher: IEEE Computer Society Press
Published: 1978
Total Pages: 348
ISBN-13:
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Introduction to Spectral Analysis
Author: Petre Stoica
Publisher: Pearson Education
Published: 1997
Total Pages: 358
ISBN-13:
DOWNLOAD EBOOKThis book presents an introduction to spectral analysis that is designed for either course use or self-study. Clear and concise in approach, it develops a firm understanding of tools and techniques as well as a solid background for performing research. Topics covered include nonparametric spectrum analysis (both periodogram-based approaches and filter- bank approaches), parametric spectral analysis using rational spectral models (AR, MA, and ARMA models), parametric method for line spectra, and spatial (array) signal processing. Analytical and Matlab-based computer exercises are included to develop both analytical skills and hands-on experience.
Modern Spectrum Analyzer Theory and Applications
Author: Morris Engelson
Publisher:
Published: 1984
Total Pages: 284
ISBN-13:
DOWNLOAD EBOOKModern Spectrum Analysis of Time Series
Author: Prabhakar S. Naidu
Publisher: CRC Press
Published: 1995-10-25
Total Pages: 424
ISBN-13: 9780849324642
DOWNLOAD EBOOKSpectrum analysis can be considered as a topic in statistics as well as a topic in digital signal processing (DSP). This book takes a middle course by emphasizing the time series models and their impact on spectrum analysis. The text begins with elements of probability theory and goes on to introduce the theory of stationary stochastic processes. The depth of coverage is extensive. Many topics of concern to spectral characterization of Gaussian and non-Gaussian time series, scalar and vector time series are covered. A section is devoted to the emerging areas of non-stationary and cyclostationary time series. The book is organized more as a textbook than a reference book. Each chapter includes many examples to illustrate the concepts described. Several exercises are included at the end of each chapter. The level is appropriate for graduate and research students.
Spectral Analysis of Signals
Author: Yanwei Wang
Publisher: Morgan & Claypool Publishers
Published: 2005
Total Pages: 108
ISBN-13: 1598290002
DOWNLOAD EBOOKSpectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Spectral Analysis for Physical Applications
Author: Donald B. Percival
Publisher: Cambridge University Press
Published: 1993-06-03
Total Pages: 616
ISBN-13: 9780521435413
DOWNLOAD EBOOKThis 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.
Fundamentals of Spectrum Analysis
Author: Christoph Rauscher
Publisher:
Published: 2007
Total Pages: 219
ISBN-13: 9783939837015
DOWNLOAD EBOOKNMR-Spectroscopy: Modern Spectral Analysis
Author: Ursula Weber
Publisher: John Wiley & Sons
Published: 2008-11-21
Total Pages: 409
ISBN-13: 3527612327
DOWNLOAD EBOOKThe state-of-the-art in NMR spectral analysis. This interactive tutorial provides readers with a comprehensive range of software tools and techniques, as well as the necessary theoretical knowledge required to analyze their spectra and obtain the correct NMR parameters. Modern Spectral Analysis provides expert guidance, by presenting efficient strategies to extract NMR parameters from measured spectra. A database of selected spectra and modern, powerful WIN-NMR software designed by Bruker are provided on the enclosed CD-ROM. The programs provided are 1 D WIN-NMR, WIN-DAISY, WIN-DR and WIN-DYNAMICS, and direct data exchange between all these programs is possible. Readers are shown how they can obtain maximum structural information from their 1 D NMR spectra with time-saving computer assistance. Practical problems that can occur and their solutions are discussed at length using clear, easy-to-follow examples. Both homo- and heteronuclear and first- and second-order spin systems are demonstrated. Moreover, relaxation analysis, nuclear Overhauser effects and magnetic site exchange are all covered in this hands-on guide to NMR spectral analysis.
Digital Spectral Analysis
Author: S. Lawrence Marple, Jr.
Publisher: Courier Dover Publications
Published: 2019-03-20
Total Pages: 435
ISBN-13: 048678052X
DOWNLOAD EBOOKDigital Spectral Analysis offers a broad perspective of spectral estimation techniques and their implementation. Coverage includes spectral estimation of discrete-time or discrete-space sequences derived by sampling continuous-time or continuous-space signals. The treatment emphasizes the behavior of each spectral estimator for short data records and provides over 40 techniques described and available as implemented MATLAB functions. In addition to summarizing classical spectral estimation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. Topics include Prony's method, parametric methods, the minimum variance method, eigenanalysis-based estimators, multichannel methods, and two-dimensional methods. Suitable for advanced undergraduates and graduate students of electrical engineering — and for scientific use in the signal processing application community outside of universities — the treatment's prerequisites include some knowledge of discrete-time linear system and transform theory, introductory probability and statistics, and linear algebra. 1987 edition.
Singular Spectrum Analysis
Author: J.B. Elsner
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 167
ISBN-13: 1475725140
DOWNLOAD EBOOKThe term singular spectrum comes from the spectral (eigenvalue) decomposition of a matrix A into its set (spectrum) of eigenvalues. These eigenvalues, A, are the numbers that make the matrix A -AI singular. The term singular spectrum analysis· is unfortunate since the traditional eigenvalue decomposition involving multivariate data is also an analysis of the singular spectrum. More properly, singular spectrum analysis (SSA) should be called the analysis of time series using the singular spectrum. Spectral decomposition of matrices is fundamental to much the ory of linear algebra and it has many applications to problems in the natural and related sciences. Its widespread use as a tool for time series analysis is fairly recent, however, emerging to a large extent from applications of dynamical systems theory (sometimes called chaos theory). SSA was introduced into chaos theory by Fraedrich (1986) and Broomhead and King (l986a). Prior to this, SSA was used in biological oceanography by Colebrook (1978). In the digi tal signal processing community, the approach is also known as the Karhunen-Loeve (K-L) expansion (Pike et aI., 1984). Like other techniques based on spectral decomposition, SSA is attractive in that it holds a promise for a reduction in the dimen- • Singular spectrum analysis is sometimes called singular systems analysis or singular spectrum approach. vii viii Preface sionality. This reduction in dimensionality is often accompanied by a simpler explanation of the underlying physics.
