FFT

根据文件中mat的雅克42实测数据，带有距离对准，相位补偿，ISAR成像(According to the file of the mat Jacques 42 measured data, with range alignment, phase compensation, ISAR imaging)

APF model matlab simulink model

The method mainly consists in considering a reference normal ECG segment and input ECG (unknown behavior) segment. Each segment of the input ECG T is compared to the reference segment using SEA. The SEA method outputs a measure in the range [0..1] in terms of the correlation factor that reflects the degree of similarity between the reference and the segment under analysis. Based on this measure, the whole segment is classified as normal or abnormal. A user specified threshold value on the correlation factor c is used for decision

基于K-L展开的人脸检测matlab程序，采用K-L展开对图片主成分分析、多图训练，从而达到在复杂图片中检测人脸的功能。(KL expansion based on face detection matlab program, using KL expansion of the picture principal component analysis, multi-map training, so as to achieve a complex picture face detection function.)

说明：  自动控制原理中的波的图的画法以及相关的程序语言(bode matlab)

基于matlab的波的传播shuzhi模拟(Wave propagation simulation based on matlab)

粒子群算法求解机器人路径规划的MATLAB算法(the pathline of robot)

Space time coding transmitter receiver matlab codes

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)

This function calculates the airfoil coordinates using Sobieski s relationships parameters. The coordinates are stored in a .dat file