•拉氏变换的定义——从傅立叶变换到拉氏变换 •拉氏变换与傅氏变换的关系 •拉氏变换的性质，收敛域 •卷积定理(S域) •系统函数和单位冲激响应

一、本课程要研究的问题是什么? 二、本课程的任务和地位。 三、为什么要学习该课程? 四、怎样才能学好该课程?

一、Z变换的基本概念和基本性质 二、变换的域分析 三、离散系统的系统函数 四、离散系统的频率响应

4.1 Chapter highlights • Hypothesis testing and the MAP criterion • Extension to the general Bayes criterion • Minimax criterion • Neyman-Person criterion 4.2 Hypothesis testing & MAP criterion 4.3 Bayes criterion 4.4 Minimax criterion 4.5 Neyman-Pearson criterion

5.5 The optimum digital detector in additive Gaussian noise 5.6 Filtering alternatives 5.7 Continuous signals - White Gaussian noise 5.8 Continuous signals - Colored Gaussian noise 5.9 Performance of binary receivers in AWGN 5.11 Sequential detection & performance

6.1 Chapter highlights • Composite hypothesis testing. • Unknown phase. • Unknown amplitude. • Unknown frequency. • Unknown time of arrival. 6.2 Composite hypothesis testing 6.3 Unknown phase 6.4 Unknown amplitude 6.5 Unknown frequency 6.6 Unknown time of arrival

11.1 Chapter highlights • Parameter estimation in WGN • Amplitude estimation in the coherent case • Amplitude estimation in the noncoherent case • Phase estimation in WGN • Time-delay estimation in WGN • Frequency estimation in WGN 11.2 Parameter estimation in WGN 11.4 Amplitude estimation in the coherent case with WGN 11.5 Amplitude estimation in the noncoherent case with WGN 11.6 Phase estimation in WGN 11.7 Time-delay estimation in WGN 11.8 Frequency estimation in WGN

周期序列的DFS及性质 离散傅里叶变换（DFT）及性质 DFT的快速算法（FFT） DFT/FFT的应用

 1 线性相位FIR数字滤波器的特点  2 窗函数设计法  3 IIR与FIR数字滤波器的比较

10.1 Chapter highlights • Parameter estimation problem formulation • Properties of estimators • Bayes estimation • Minimax estimation • Maximum-likelihood estimation • Comparison of estimators of parameters 10.2 Formulation of the general parameter estimation problem 10.3 Types of estimation problems 10.5 Properties of estimators 10.6 Bayes estimation 10.7 Minimax estimation 10.8 Maximum-likelihood estimation