电子科技大学：《生物医学信号处理 Biomedical Signal Processing》课程教学资源（课件讲稿）Lecture 4.2 biomedical Applications of AR Model

演示演讲PPT成功案例：小波变换在信号处理中的应用

10.1 滤波电路的基本概念与分类 10.2 一阶有源滤波电路 10.3 高阶有源滤波电路 10.3.1 有源低通滤波电路 10.3.2 有源高通滤波电路 10.3.3 有源带通滤波电路 10.3.4 二阶有源带阻滤波电路 *10.4 开关电容滤波器 10.5 正弦波振荡电路的振荡条件 10.6 RC正弦波振荡电路 10.7 LC正弦波振荡电路 10.7.1 LC选频放大电路 10.7.2 变压器反馈式LC振荡电路 10.7.3 三点式LC振荡电路 10.7.4 石英晶体振荡电路 10.8 非正弦信号产生电路 10.8.1 电压比较器 10.8.2 方波产生电路 10.8.3 锯齿波产生电路

一、时间序列自回归模型 二、时间序列向量自回归模型 三、格兰杰因果关系检验 四、格兰杰因果在脑电中的应用 五、补充知识

◆5.0 Introduction ◆5.1 Frequency Response of LTI Systems ◆5.2 System Functions For Systems Characterized by Linear Constant-coefficient Difference equation ◆5.3 Frequency Response for Rational System Functions ◆5.4 Relationship Between Magnitude and Phase ◆5.5 All-Pass System ◆5.6 Minimum-Phase Systems ◆5.7 Linear Systems with Generalized Linear Phase

本文从生物视觉采样模型、神经形态视觉传感器的采样模型及类型、视觉信号处理与特征表达、视觉任务应用等视角进行了系统性的回顾与综述,展望了该领域未来研究的技术挑战与可能发展方向,同时探讨了其对未来机器视觉和人工智能领域的潜在影响

◆8.0 Introduction ◆8.1 Representation of Periodic Sequence: the Discrete Fourier Series ◆8.2 Properties of the Discrete Fourier Series ◆8.3 The Fourier Transform of Periodic Signal ◆8.4 Sampling the Fourier Transform ◆8.5 Fourier Representation of Finite-Duration Sequence: the Discrete Fourier Transform ◆8.6 Properties of the Discrete Fourier Transform ◆8.7 Linear Convolution using the Discrete Fourier Transform ◆8.8 the discrete cosine transform (DCT)

◆7.0 Introduction ◆7.1 Design of Discrete-Time IIR Filters From Continuous-Time Filters ◆7.2 Design of FIR Filters by Windowing ◆7.3 Examples of FIR Filters Design by the Kaiser Window Method ◆7.4 Optimum Approximations of FIR Filters ◆7.5 Examples of FIR Equiripple Approximation ◆7.6 Comments on IIR and FIR Discrete-Time Filters

3.1 测试系统的静态特性 3.2 测试系统的静态特性指标 3.3 测试系统静态特性的标定 3.4 测试系统动态特性 3.5 测试系统无失真测试条件 3.6 一阶、二阶系统的动态特性 3.7 测试系统对瞬态激励的响应 3.8 测试系统动态特性的标定 3.9 测试系统动态特性标定示例 3.10 测量仪器的特性指标

◆2.1 Discrete-Time Signals: Sequences ◆High and Low Frequencies in Discrete-time signal ◆2.2 Discrete-Time Systems ◆Memoryless (memory); Linear; Time-Invariant; Causality; Stability(BIBO) ◆2.3 Linear Time-Invariant (LTI) Systems ◆LTI Systems:Convolution( 系统适用吗?) ◆2.4 Properties of LTI Systems ◆Stability and Causality of LTI systems;FIR and IIR systems ◆2.5 Linear Constant-Coefficient Difference Equations ◆The output for a given input is not uniquely specified. Auxiliary conditions are required; initial-rest conditions ◆2.6 Frequency-Domain Representation of Discrete￾Time Signals and systems ◆ Eigenfunction and Eigenvalue for LTI systems ◆2.7 Representation of Sequences by Fourier Transforms ◆2.8 Symmetry Properties of the Fourier Transform ◆2.9 Fourier Transform Theorems ◆2.10 Discrete-Time Random Signals