FH

this program does de graphic convolution of two signals in real time, it gives de value of the convolution

实现16qam的程序，simulink模块(Procedures to achieve 16qam, simulink module)

信号处理与线性系统（英文版）( This text presents a comprehensive treatment of signal processing and linear systems suitable for juniors and seniors in electrical engineering. Based on B. P. Lathi s widely used book, Linear Systems and Signals, it features additional applications to communications, controls, and filtering。)

在matlab 中多种方式的边缘提取。edge(matlab edge)

A QPSK modulator and demodulator using initial assumptions

此程序为机器人位置正反解程序，可用此程序求出解析解(This procedure for robot location inverse solution procedures, use this procedure to derive analytical solutions)

人脸识别哦，用于人脸识别，很不错，能用，很给力(face recgonition)

非持续性CAMA是一种冲突避免的CSMA协议，如果终端检测到信道状态空闲，则立即进行数据传输，而信道状态忙，则停止载波监听，并随机在等待一段时间后再进行载波监听。(Non-durable CAMA avoid CSMA protocol is a kind of conflict, if the end channel state idle, detected immediately for data transmission, the channel state is busy, stop the carrier sense, and random carrier sense of the reentry after waiting for a period of time. )

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )

simulink 仿真教程，详细介绍了simulink的仿真环境及模型的创建(simulink simulation tutorial, detailing the simulink simulation environment and model creation)