8.1 representation of periodic sequences:the discrete fourier series 8.2 the fourier transform of periodic signals 8.3 properties of the discrete fourier series 8.4 fourier representation of finite-duration sequences: Definition of the discrete fourier transform 8.5 sampling the fourier transform(point of sampling) 8.6 properties of the fourier transform 8.7 linear convolution using the discrete fourier transform 8.8 the discrete cosine transform(DCT)

一、了解MATLAB语言的背景知识 二、掌握MATLAB安装的方法过程 三、掌握MATLAB基本窗口的使用 四、掌握MATLAB语言的基础知识

实验4离散系统的频率响应分析和零、极点分布 实验目的:加深对离散系统的频率响应分析和零、极点分布的概念 理解。 实验原理:离散系统的时域方程为 (n-k)=(n-k) 其变换域分析方法如下:

6.1 Introduction The convolution sum description of an LTI discrete-time system can, in principle, be used to implement the system For an IR finite-dimensional system this approach is not practical as here the impulse response is of infinite length · However, direct implementation of the IIR finite-dimensional system is practica

Objective-Determination- of realizable transfer function G() approximating a given frequency response specification is an important step in the development of a digital filter If an IIR filter is desired, G() should be a stable real rational function Digital filter design is the process of deriving the transfer function G

Introduction Ideally, the system parameters along with the signal variables have infinite precision taking any value between -oo and · In practice, they can take only discrete values within a specified range since the registers of the digital machine where they are stored are of finite length

1.1 Introduction Any problems about signal analyses and processing may be thought of letting signals trough systems. f(t) y(t) h(t) From f(t) and h(t), find y(t), Signal processing From f(t) and y(t), find h(t), System design From(t)andh(t), find(t), Signal reconstruction

9.1 Typical Applications of Adaptive Filters 9.2 Principles of Adaptive Filters 9.3 Method of Steepest Descent 9.4 Least-Mean-Square Adaptive Filters 9.5 Recursive Least-Squares Adaptive Filters 9.6 Fast RLS Algorithms for FIR Filtering 9.7 Tracking Performance of Adaptive Algorithms 9.8 Summary

8.1 The Modeling Process: Theory and Practice 8.2 Estimation of All-Pole Models 8.3 Estimation Of Pole-Zero Models 8.4 Applications 8.4.1 Spectral Estimation 8.4.2 Speech Modeling 8.5 Harmonic Models and Frequency Estimation Techniques 8.6 Summary

2.1 Discrete-Time Signals: Time-Domain Representation Signals represented as sequences of numbers, called samples Sample value of a typical signal or sequence denoted as x[n] with n being an integer in the range-oo≤n≤∞ x[n] defined only for integer values of n and undefined for noninteger values of n Discrete-time signal represented by {x[n]}