report-coding

说明：  本文设计了一款人体行为异常监控系统，主要适用人群是老年人，在摄像头固定的情况下，自动检测人体运动轨迹，并与提前设定好的行为库进行匹配，分析判断是否具有异常行为。 在数字图像预处理部分采用了图像二值化，腐蚀与膨胀等几种方法为人体目标的跟踪和检测做准备。为了克服在实际操作中遇到的问题，采用了帧差法和ViBe算法，帧差法即利用帧间变化与当前帧、背景算法来判断它是否大于阈值，并分析视频中序列的运动特性，ViBe算法则是一种背景建模的方法，背景模型是由邻域像素来创建，并对比背景模型、当前输入像素值检测出前景，确定视频中的目标跟踪。在人体行为识别中，运动目标最小长宽比以及连续帧间的加速度来判断人体行为是否异常，如果检测到异常的行为比如说摔倒、快跑等行为，在识别的过程这种实时监测。不同环境可能需要调试，要求有一定编程基础，小白慎入。(In this paper, a monitoring system for abnormal human behavior is designed, which is mainly applicable to the elderly. When the camera is fixed, it can automatically detect the human motion track and match with the behavior database set in advance to analyze and judge whether there is abnormal behavior.)

卡尔曼滤波matlab程序，希望大家能够用的到。(Kalman filtering Matlab procedures in the hope that we can use that.)

通过“魔术公式”轮胎模型，确定轮胎侧偏力与侧偏角的关系。(Through the "magic formula" tire model, the relationship between tire cornering force and side deflection angle is determined.)

卫星导航单点定位、伪距差分、载波相位差分RTK定位、卫导测姿、PPP等功能。(GNSS point positioning， pseudorange/carrier phase differencinal positioning， attitude determination。)

解决一些电脑的，主板，显卡。等故障，可以帮助了解电脑 (good way)

基于帧差法的运动目标检测的matlab代码(Frame difference method based on moving object detection matlab code)

说明：  matlab语音采集 语音采集信号 时域 频域分析(matlab voice collecting voice collecting signals frequency domain analysis)

NUMERICAL OPTIMIZATION: This is steepest descent method with constant step length to find the minima of f(x, y) = xy exp(− 2x^2 − y^2 + 0.3y) Graphical represxentation in 5 ways of solution, simple and clear explained. (NUMERICAL OPTIMIZATION: This is steepest descent method with constant step length to find the minima of f(x, y) = xy exp(− 2x^2 − y^2+ 0.3y) Graphical represxentation in 5 ways of solution, simple and clear explained. )

用matlab仿真的BPSK_OFDM_Simulation的代码(Simulation using matlab code BPSK_OFDM_Simulation)

本程序是Matlab的Simulink模块程序。模拟虚拟输出队列。是通信建模的入门模块。欢迎大家学习交流(This program is Matlab' s Simulink module program. Analog virtual output queues. Getting Started modules in communications modeling. Welcome to the Learning Exchange)