random-process

matlab学习课件，内容基本，易懂，方便自学！(matlab learning courseware, content, basic, easy to understand, convenient self-study!)

very uesful program about ofdm

Figure creates a magnification box when under the mouse position when a button is pressed. Press + / - while button pressed to increase/decrease magnification. Press > / < while button pressed to increase/decrease box size. Hold Ctrl while clicking to leave magnification on figure

prefix word detection

说明：  fft算法，fft与dft算法的比较，并有详细的文档说明。(fft algorithm, fft compared with the dft algorithm and a detailed description of the document.)

震动信号的预警和过零率计算，需要有自己的原始震动信号(seismic signal processing zero_-pass rate)

可以有效方便地对原始数据进行自适应分解，广泛用于故障诊断和模式识别等方面的特征提取。(Can be effectively and easily decompose the raw data adaptively, widely used in troubleshooting and other aspects of the pattern recognition feature extraction.)

傅里叶变换对加速度信号的处理，输入加速度信号，采样率，下限上线频率，积分次数(Fourier transform of the acceleration signal processing, input acceleration signal, the sampling rate, upper and lower line frequency, the number of integrations)

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 )

snake主动轮廓模型，基于区域主动轮廓模型(snake active contour models, region-based active contour model)