ee477_fftlab_sp07

La transformée en ondelette discrète (en anglais Discrete Wavelet Transform, ou DWT) est une technique utilisée dans la compression de données numériques avec ou sans perte (JPEG2000). La compression est réalisée par approximations successives de l information initiale du plus grossier au plus fin. On réduit alors la taille de l information en choisissant un niveau de détail.

说明：  小波去噪，以Haar和db4小波基为例的去噪代码(wavelet，haar，db4)

WhitePaper_Matlab_Simulink_&_EB_Simtools_Ford Motor, whitePaper_Matlab_Simulink_&_EB_Simtools_Ford Motor, WhitePaper_Matlab_Simulink_&_EB_Simtools_Ford Motor.

matlab 一本有用的入门学习书籍 包括dc-ac ac-dc 的仿真 (matlab this is a very useful book for the new guy。)

ant colony for TSP problem

说明：  matlab代码遗传算法解决背包问题，0-1整数规划问题(Genetic algorithm to solve knapsack problem, 01 integer programming problem)

this is file for traffic networks benchmarks...

星敏感器，磁强计，惯导组合导航的EKF程序(Star sensor, magnetometer, EKF process of inertial integrated navigation)

1)football.m简单地以阵列形式显示一副图； 2）pre.m是心音信号过滤归一化预处理的过程； 3）wavlet.m是用db6小波进行信号的分解和重构，且求包络能量函数； 4）path.m是利用KKT条件的路径跟踪法，用于求解二次规划问题； 5）LRmmse是MIMO通信中常用检测方法，结合了最小均方误差准则和格基规约的知识。(1) football.m simply displays a graph in an array 2) pre.m is the process of normalized preprocessing of heart sound signal filtering 3) wavlet.m is a db6 wavelet for signal decomposition and reconstruction, and to find the envelope energy function 4) path.m is the path tracking method using KKT condition, which is used to solve the quadratic programming problem. 5) LRmmse is a commonly used detection method in MIMO communication, combining the knowledge of minimum mean square error criterion and lattice protocol.)

Start with the runlocalization track.m which is the entrance function to your lab. This function reads two les determined by simoutle and maple input arguments which contain information about sensor readings and the map of the environment respectively, runs a loop for all the sensor readings and calls the ekf localize.m to perform one iteration of EKF localization on the readings and plots the estimation(red)/ground truth(green) and odometry(blue) information.(This lab consists of two parts: 1. A preparatory case study with a standard Kalman lter where you learn more about the behavior of the Kalman lter. Very little extra code is needed. 2. The main lab 1 problem in which you need to complete an implementation of an Extended Kalman lter based robot localization.)