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Author:   Tianshuang Qiu ,  Ying Guo ,  Tsinghua University Press
Publisher:   De Gruyter
Imprint:   De Gruyter
Weight:   1.125kg
ISBN:  

9783110461589

ISBN 10:   3110461587
Pages:   604
Publication Date:   09 July 2018
Audience:   College/higher education ,  Tertiary & Higher Education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   In stock  
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Table of Contents

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 α-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

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

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