实验2离散系统的差分方程、冲激响应和卷积分析 实验目的:加深对离散系统的差分方程冲激响应和卷积分析方法 的理解。 实验原理:离散系统

第三章离散 Fourier变换(DFT) 主要内容: 一、离散傅立叶变换的定义 二、离散傅立叶变换的基本性质 三、频率域采样 四、DFT的应用

了解周期序列的傅里叶级数及性质，掌握周期卷积过程； 理解离散傅里叶变换及性质，掌握圆周移位、共轭对称性，掌握圆周卷积、线性卷积及两者之间的关系； 掌握FFT算法的计算量分析，按时间抽选的基-2FFT算法； 2.1 离散傅里叶变换（DFT） 2.2 快速傅里叶变换(FFT) 2.3 FFT应用

 S1. Filters: devices of signal processing  Classic Filter  Optimal Filter  Adaptive Filter  S2. Adaptive filtering applications  S3. Stochastic processes  Partial characteristic: Autocorrelation Matrix (ACM)  PSD  Linear parametric model  S4. Mean Square Information Space

电子科技大学：《计算电磁学 Computational Electronmagentics》课程教学资源（课件讲稿，有限差分法）第6章 时域有限差分法 III 6.1 激励源技术 6.2 集总参数电路元件的模拟 6.3 数字信号处理技术 6.4 应用举例（I）

 Introduction  Periodogram and correlogram  Nonparametric methods  Parametric methods for Rational spectra  Parametric methods for line spectra  Filter-bank approach  Spatial methods

 S1. Introduction  S2. Conventional Kalman filter  S3. Example  S4. RLS and Kalman filtering  S5. Square-root information filter  S6. Square-root covariance filter  Better numerical properties than the conventional one  The square-root RLS algorithms are special cases

 S.1 Prototype adaptive filter  Filter structure  Quadratic cost functions  S2. Wiener filter theory  MMSE criterion  Wiener-Hopf equations  Orthogonality principle  S3. Unrealizable Wiener filter

第三章离散傅立叶变换FT 3.0引言 3.1离散傅立叶变换的定义 3.2频率抽样理论 3.3离散傅立叶变换(DFT)的定理和性质 3.4DFt应用举例

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)