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Tuesday, April 28, 2020 | History

1 edition of **Spectral analysis: methods and techniques** found in the catalog.

Spectral analysis: methods and techniques

- 216 Want to read
- 33 Currently reading

Published
**1970** by M. Dekker in New York .

Written in English

- Spectrum analysis.

**Edition Notes**

Includes bibliographies.

Statement | edited by James A. Blackburn. |

Contributions | Blackburn, James A. |

Classifications | |
---|---|

LC Classifications | QC451 .S617 |

The Physical Object | |

Pagination | xi, 289 p. |

Number of Pages | 289 |

ID Numbers | |

Open Library | OL17752959M |

It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. Spectral Analysis for Economic Time Series suitable when dealing with very long time series, like those found in geophysics, astrophysics, neurosciences or ﬁnance. But their application to short series — the norm in macroeconomics — is diﬃcult and may give unstable parameter-dependent results.

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Spectral methods are useful techniques for solving integral and partial differential equations, many of which appear in fluid mechanics and engineering problems. Based on a graduate course, this book presents these popular and efficient techniques with both rigorous analysis and extensive coverage of their wide range of by: J.N.

Rayner, in International Encyclopedia of the Social & Behavioral Sciences, Spectral analysis is one of several statistical techniques necessary for characterizing and analyzing sequenced data.

Sequenced data are observations that have been taken in one, two, or three dimensional space, and/or time. Examples might be observations of population density along a road, or of rainfall.

This book presents the basic algorithms, the main theoretical results, and some applications of spectral methods. Particular attention is paid to the applications of spectral methods to nonlinear problems arising in fluid dynamics, quantum mechanics, weather prediction, heat conduction and other book consists of three parts.

Chapter 5 on time series analysis is a book in itself, spanning a wide diversity of topics from stochastic processes and stationarity, coherence functions, Fourier analysis, tidal harmonic analysis, spectral and cross-spectral analysis, wavelet and other related methods for processing nonstationary data series, digital filters, and fractals.

Every chapter ends with a set of problems for practice. This excellent and very well-written book could be used as s graduate textbook in mathematics and other engineering disciplines. It would also be a good reference book for active practitioners and researchers of spectral methods.” (Srinivasan Natesan, ACM Computing Reviews, January Cited by: Spectral methods are a class of techniques used in applied mathematics and scientific computing to numerically solve certain differential equations, potentially involving the use of the fast Fourier idea is to write the solution of the differential equation as a sum of certain "basis functions" (for example, as a Fourier series which is a sum of sinusoids) and then to choose the.

A broad discussion is presented of spectral estimation techniques and their implementation. The topics addressed include: reviews of linear systems, transform theory, matrix algebra, and random process theory; classical spectral estimation; parametric models of random processes; autoregressive process and spectrum properties; block data algorithms and sequential data algorithms in Cited by: Additional Physical Format: Online version: Spectral analysis: methods and techniques.

New York, M. Dekker, (OCoLC) Document Type: Book. Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County.

It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for /5(2). Digital 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.4/5(2). 'The book constitutes a lot more than an update of the authors' book Spectral Analysis for Physical Applications.

The stand-out features are still the examples and exercises, but all data analysis has been done in R and considerable effort has gone into explanation, and how the methods fit in with alternatives.

This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book. Spectral Techniques in Proteomics begins with an overview of structural proteomics and chemical proteomics, followed by an in-depth presentation of widely used methods like ESI, MALDI, SELDI, ICAT, tandem MS, and shotgun proteomics.

The book also presents. Spectral methods are well-suited to solve problems modeled by time-dependent partial differential equations: they are fast, efficient and accurate and widely used by mathematicians and practitioners.

This class-tested introduction, the first on the subject, is ideal for graduate courses, or by: This book presents applications of spectral methods to problems of uncertainty propagation and quantification in model-based computations, focusing on the computational and algorithmic features of these methods most useful in dealing with models based on partial differential equations, in particular models arising in simulations of fluid flows.

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Spectral Analysis of Signals/Petre Stoica and Randolph Moses No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher.

Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 2 Nonparametric Methods Chapter 4 presents results relating the smoothness of a function to the accuracy of the approximation used in these spectral methods. Chapters 5, 6, and 7 develop similar methods and results using nonuniform Chebyshev grids, and give examples of the use of these Chebyshev methods to solve 2-D boundary value problems.

This is also good. Third, the window reduces the resolution in the spectrum by making the peaks wider. This is bad. In DSP jargon, windows provide a trade-off between resolution (the width of the peak) and spectral leakage (the amplitude of the tails). To explore the theoretical aspects of this in more detail, imagine an infinitely long discrete sine wave at a frequency of the sampling rate.

A one-stop reference to the major techniques for analyzing microwave planar transmission line structures The last two decades have seen important progress in the development of methods for the analysis of microwave and millimeter-wave passive structures, which contributed greatly to microwave integrated circuit design while also stimulating the development of new planar transmission lines.

Spectral methods have long been popular in direct and large eddy simulation of turbulent flows, but their use in areas with complex-geometry computational domains has historically been much more limited.

More recently, the need to find accurate solutions to the viscous flow equations around complex configurations has led to the development of high-order discretization procedures on. Request PDF | Spectral Methods | The preceding two chapters studied the subspace clustering problem using algebraic-geometric and statistical techniques, respectively.

Under the | Find, read. Summary This chapter contains sections titled: Introduction Group Frequencies Identification Hydrogen Bonding Spectrum Manipulation Concentration Simple Quantitative Analysis Multi‐Component Analys.

This book discusses the latest advances in singular spectrum-based algorithms for seismic data processing, providing an update on recent developments. It also includes comprehensive methodological and parametric descriptions and testing on appropriately generated synthetic data sets.

Additionally, the sections covering spectral, wavelet, and harmonic analysis techniques are completely revised since these techniques have attracted significant attention over the past decade as more accurate and efficient data gathering and analysis methods.

This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time series from oceanography, metrology, atmospheric science and other areas are used in running examples throughout, to allow clear comparison of how the various methods address questions.

Spectral Methods in Surface Superconductivity is intended for students and researchers with a graduate level understanding of functional analysis, spectral theory, and PDE analysis. Anything which is not standard is recalled as well as important semiclassical techniques in spectral theory that are involved in the nonlinear study of.

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.

Prerequisites include some knowledge of discrete-time linear system and transform theory, introductory probability and statistics, and linear. Introduction to Spectral Analysis DonPercival,AppliedPhysicsLab,UniversityofWashington Examples of Spectral Analysis.

A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis.

This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for. Spectral analysis offers an interesting addition to other methods of time series analysis. For those who wish to find more out about it, we strongly recommend the book by C.

Chatfield (). It offers a thorough, readable treatment of a difficult, but useful, subject. Data Structure The data are entered in a single variable. Missing ValuesFile Size: KB. The book considers the influence of the sample geometry, of the type (metal, insulator, semiconductor, organic), and of the atomic number can have on surface analysis.

The text describes in detail low energy ion scattering spectroscopy, X-ray photoelectron spectroscopy, Auger electron spectroscopy, secondary ion mass spectroscopy, and infrared Book Edition: 1.

In the book, Iwaniec treats the spectral theory of automorphic forms as the study of the space \(L^2 (H\Gamma)\), where \(H\) is the upper half-plane and \(\Gamma\) is a discrete subgroup of volume-preserving transformations of \(H\).

He combines various techniques from analytic number theory. The text develops the principal aspects of applied Fourier analysis and methodology with the main goal to inculcate a different way of perceiving global and regional geodetic and geophysical data, namely from the perspective of the frequency, or spectral, domain rather than the spatial : Christopher Jekeli.

Summary Facilitating the innovation, development, and application of new spectroscopic methods in proteomics, Spectral Techniques in Proteomics provides a broad overview of the spectroscopic toolbox that can be used, either with proteome or sub-proteome mixtures or with individual/purified proteins studied in gives a modest overview of existing and proven techniques as well as a.

The Analytical Methods section describes methodologies using a wide variety of potentially hazardous chemicals (acids, bases, organic solvents, etc.) Acid digestions, either at atmospheric pressure or atFile Size: 1MB.

Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County.

Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. the performance and limitations of spectral methods, contains an exhaustive bibliography for spectral methods at the level of year A more strange feature of spectral methods is the fact that, in some sit-uations, they transform self-adjoint diﬀerential problems into non symmetric, i.e., non normal, discrete algebraic problems.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.

(source: Nielsen Book Data) Supplemental links Table of contents Publisher description.“This is a largely self-contained book on major parts of the application of spectral methods to the numerical solution of partial differential equations.

The material is accessible to advanced students of mathematics and also to researchers in neighbouring fields wishing to acquire a sound knowledge of methods they might intend to Price: $