Signal Processing and Data Analysis (eBook, PDF) - Qiu, Tianshuang; Guo, Ying
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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.
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Produktbeschreibung


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.


Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

  • Produktdetails
  • Verlag: de Gruyter Oldenbourg
  • Seitenzahl: 604
  • Erscheinungstermin: 9. Juli 2018
  • Englisch
  • ISBN-13: 9783110465082
  • Artikelnr.: 53675205
Autorenporträt


Tianshuang Qiu, Dalian University of Technology, Dalian, Ying Guo, Shenyang University of Technology, Shenyang, China

Inhaltsangabe
Table of Content: Chapter 1 Concepts and theoretical background in signals and systems 1.1 Introduction 1.2 Key concepts 1.3 Linear time invariant system and convolution 1.4 Characteristics of linear time invariant system Chapter 2 Fourier transform and frequency domain analysis 2.1 Introduction 2.2 Fourier series for continuous
time system 2.3 Fourier series for discrete
time system 2.4 Fourier transform for continuous
time system 2.5 Fourier transform for discrete
time system 2.6 Frequency domain analysis for signals and systems Chapter 3 Laplace transform and complex frequency domain analysis 3.1 Introduction 3.2 Laplace transform 3.3 Continuous
time signal and complex frequency domain analysis 3.4 Z transform 3.5 Discrete
time signal and complex frequency domain analysis Chapter 4 Discretization of continuous
time signal and serialization of discrete
time signal 4.1 Introduction 4.2 Sampling continuous
time signal 4.3 Interpolation and fitting for discrete
time signal Chapter 5 Discrete Fourier transform and fast Fourier transform 5.1 Introduction 5.2 Discrete Fourier transform 5.3 Problems in discrete Fourier transform 5.4 Two dimensional Fourier transform 5.5 Fast Fourier transform 5.6 Application of Fast Fourier transform Chapter 6 Digital filter and design 6.1 Introduction 6.2 Digital filter structure 6.3 Infinite impulse response filter 6.4 Finite impulse response filter 6.5 Lattice structure for digital filter 6.6 Infinite impulse response filter design 6.7 Finite impulse response filter design Chapter 7 Finite
length effect in digital signal processing 7.1 Introduction 7.2 Analog to digital converter 7.3 Quantification of digital filter 7.4 Finite
length effect in digital filter calculation 7.5 Finite
length effect in discrete Fourier transform Chapter 8 Error analysis and signal pre
treatment 8.1 Introduction 8.2 Concept and classification of error 8.3 uncertainty of measurement 8.4 Least square method in data analysis 8.5 Regression analysis 8.6 Trends and outliers 8.7 Case study on temperature measurement Chapter 9 Random signal processing 9.1 Introduction 9.2 Concepts and characteristics of random signal 9.3 Stochastic process and stochastic signal 9.4 Frequently0used stochastic signal and noise 9.5 Stochastic signal for linear system 9.6 Classical analysis for stochastic signal 9.7 Parameter analysis for stochastic signal Chapter 10 Correlation function estimation for stochastic signal and power spectral density estimation 10.1 Introduction 10.2 Correlation function and power spectral density 10.3 Self
correlation 10.4 Classical methods for spectral estimation 10.5 Methods for spectral estimation after 1960s 10.6 Cepstrum analysis 10.7 Application of spectral estimation in signal processing Chapter 11 Statistical optimal filter for stochastic signals 11.1 Introduction 11.2 Theoretical background of Wiener filter 11.3 Wiener predictor 11.4 Kalman filter Chapter 12 Adaptive filtering 12.1 Introduction 12.2 Adaptive transversal filter and stochastic gradient method 12.3 Least mean square algorithm for adaptive filter 12.4 Least square algorithm for adaptive filter 12.5 Application of adaptive filter Chapter 13 Statistic analysis of higher
order and Fractional lower
order signals 13.1 Higher
order cumulant 13.2 Higher
order spectrum and higher
order estimation 13.3 a
stable process and lower
order statistics 13.4 Application of lower
order signals Chapter 14 Modern signal processing 14.1 Time
frequency analysis 14.2 Wavelet analysis 14.3 Hibert
Huang transformation