①直接利用DFT计算:复乘次数为N2,复加次数为N(N-1)利用FT计算:复乘次数为og:n复加次数为log:解

1.为什么用DSP 2.DSP特点 3.DSP的种类 4.TI的DSP 5.DSP应用领域 6.DSP系统开发步骤 7.DSP知识平台 8.“DSP实验”课程内容 9.教学模式

5.1 非周期序列的傅里叶变换 5.2 非周期序列傅里叶变换的性质 5.3 非周期序列傅里叶变换与Z变换的关系 5.4 离散时间系统的频率特性 5.5 离散时间系统的频域分析 5.6 序列的希尔伯特变换 5.7 因果序列频谱的实部与虚部的约束关系 5.8 线性位不变全通系统和线性位不变因果稳定

一、多总线结构,三组16-bit数据总线和一组程序总线 二、40-bit算术逻辑单元(ALU),包括一个40- bit的桶形 三、移位器和两个独立的40-bit加器 四、17x17-bit并行乘法器,连接一个40-bit的专用加法器

提出一种快速傅里叶变换(FFT)的改进算法,该算法利用FFT的衰减特性,只需要对FFT算法做简单的变换,就可以有效地消除频谱泄漏分量,实现非整数次谐波的精确检测,克服了传统FFT的缺陷.该算法与加窗体FFT相比,具有相近的特性,在算法构造方面又比加窗体FFT算法更简单,因此该算法更加适合应用于存储资源有限的微处理器上.为证明该算法应用于微处理器的方便性,设计了一套基于数字信号处理(DSP)的谐波检测装置,并对该算法进行了验证

§2.1 Discrete-Time Signals: Time-Domain Representation §2.2 Operations on Sequences §2.3 Basic Sequences §2.4 The Sampling Process §2.5 Discrete-Time Systems §2.6 Time-Domain Characterization of LTI Discrete-Time System §2.7 Classification of LTI Discrete-Time Systems §2.8 Correlation of Signals

§4.1 LTI Discrete-Time Systems in the Transform Domain §4.2 The Frequency Response §4.3 Frequency Response Computation Using MATLAB §4.4 The Concept of Filtering §4.5 Phase and Group Delays §4.6 Frequency Response of the LTI Discrete-Time System §4.7 The Transfer Function §4.8 The Transfer Function §4.9 Frequency Response from Transfer Function §4.10 Types of Transfer Functions §4.11 Linear-Phase FIR Transfer Functions §4.12 Allpass Transfer Function §4.13 Minimum-Phase and Maximum-Phase Transfer Functions

§7.1 Digital Filter Specifications §7.2 Selection of Filter Type §7.3 Digital Filter Design: Basic Approaches §7.4 IIR Digital Filter Design: Bilinear Transformation Method §7.5 IIR Highpass, Bandpass, and Bandstop Digital Filter Design §7.6 Fixed Window Functions §7.7 FIR Filter Design Example

§5.1 Digital Processing of Continuous-Time Signals §5.2 Sampling of Continuous-time Signals §5.3 Effect of Sampling in the Frequency Domain §5.4 Recovery of the Analog Signal §5.5 Implication of the Sampling Process §5.6 Sampling of Bandpass Signals §5.7 Analog Lowpass Filter Specifications §5.8 Analog Lowpass Filter Design

 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