Monty-Hall

说明：  此粒子滤波程序采用重采样算法，用于二维平面目标跟踪(This procedure uses resampling particle filter algorithms for two-dimensional plane tracking)

matlab example.DSB-SC Modulation with AWGN transmission medium (channel)

自己做设计用的参数提取程序，主要是LPCC和MFCC。(Designs its own program with the parameter extraction, mainly LPCC and MFCC.)

网上下载的随机子空间程序修正版，保证可用(Download a random sub-space program revision to ensure that available)

基于MUSIC方法功率谱估计的matlab仿真(MUSIC-based method of power spectrum estimation matlab simulation)

Electric Vehicle Model

该程序主要基于对信号的处理，主要功能是实现振动合成，振动合成模块，该模块分为两个部分：频率相同的简谐振动的合成、同一直线上不同频率的两个简谐振动的合成。(The program is mainly based on the signal processing, the main function is to achieve the synthesis of vibration, vibration synthesis module, which is divided into two parts: the same harmonic vibration frequency synthesis, synthesis of two simple harmonic motion along the same line of different frequencies .)

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 )

一种新的复正弦信号二维频率估计算法一种新的复正弦信号二维频率估计算法(A new two-dimensional complex sinusoid frequency estimation algorithmA new two-dimensional complex sinusoid frequency estimation algorithm)

语音信号在其他变换域中的分析技术和特性,语音信号的同态处理和倒谱分析,离散余弦变换,小波和小波包变换(Speech signal analysis technologies and features in other transform domain, homomorphic processing voice signals and cepstrum analysis, discrete cosine transform, wavelet and wavelet packet transform)