MATLAB_jixieyuanli_fangzhenshiyan

考虑两辆车在道路上同向行驶，在O-16s时，两车均保持匀速直线运动，由安装在后车上的车载毫米波雷达检测出与前车的距离为150m，相对速度为-3m/s，方位角 。在16-20s时，前车向右偏转，与后车的相对角加速度为 。后车加速，与前车的纵向相对加速度为 。雷达的扫描周期为T=0.1s，系统噪声为 ， 。量测误差为 。(Consider the two cars traveling the same direction on the road, in the O-16s, the two vehicles are kept uniform linear motion, installed in the vehicle after the car millimeter wave radar to detect the distance of the vehicle in front 150m, the relative speed- 3m/s, Azimuth. In the 16-20s, the first car to the right deflection, and the relative angular acceleration after the car is. After the vehicle acceleration, relative to the vehicle in front of the vertical acceleration. Radar scan period T = 0.1s, the system noise. Measurement error.)

在matlab与SystemGenerator的平台下实现零阶时延数字正切锁相环。此模块最终能达到与输入信号同频率。(In the matlab, and SystemGenerator the platform, zero-order delay tangent digital phase-locked loop. This module is ultimately to reach input signal with the same frequency.)

signal transient crosstalk noise

经验模态分解(Empirical Mode Decomposition,EMD)法是黄锷(N. E. Huang)在美国国家宇航局与其他人于1998年创造性地提出的一种新型自适应信号时频处理方法，特别适用于非线性非平稳信号的分析处理。(Empirical Mode Decomposition)

复小波分解的程序，对信号进行去噪，只有一个程序，实现复小波的分解(Complex wavelet decomposition process, the signal denoising, only one procedure to achieve the decomposition of complex wavelet)

G.729的全名Code Excited Linear Prediction Model (CELP) and Conjugate-Structure Algebraic CELP (CS-ACELP). 共轭结构-代数码激励线性预测编码. (Code Excited Linear Prediction Model (CELP) and Conjugate-Structure Algebraic CELP (CS-ACELP)

IEC Flickermeter Simulation

基于彩色分割的汽车牌照识别 基于彩色分割的汽车牌照识别基于彩色分割的汽车牌照识别(segmentation based on the color of the vehicle license identification based on color segmentation in vehicle license identification based on color segmentation of the vehicle license as identification)

利用角度的不同进行识别，两图像的模式识别范畴(Using the perspective of the different recognition, two images of the field of pattern recognition.)

visual c++ matlab图像处理与识别实用案例精选，帮助我们学习(visual c++ matlab image processing and recognition of practical case selected to help us learn)