fc_elsct

Demo of Level set project

 检查数据：如果某一组转速N下数据点少于5个点，给出提示：“数据点少不能绘制曲线”。  插值：每一条转速N线上的测量点个数可能不一样，按照测量点数最多的个数，将测量点少的数据插值成测量数据点最多一样的个数 ( check data: If a group speed N under the data point is less than 5 points, hints: " The few data points can not draw a curve. The  interpolation: each a speed N line, the number of measurement points may be different, in accordance with the maximum number of measurement points, the measurement point less data is interpolated to the measurement data points up the same number of)

对给定的信息序列进行Hamming编码，给出码字——C++汉明编码(Hamming coding sequence of the given information, code word- c++ Hamming coding are presented)

小波包分析提取振动信号中的特征频率，包括最后计算压缩图像的峰值信噪比和压缩效果的源码,包括回归分析和概率统计。( Wavelet packet analysis to extract vibration signal characteristic frequency, Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Including regression analysis and probability and statistics.)

压电陶瓷驱动器辞职模型的matlab编写 实用(Piezoelectric actuator prepared by the resignation of utility model matlab)

说明：  Solves oxy combustion of methane in matlab

利用matlab处理图像，分别用高斯滤波，均值滤波和中值滤波，对同一个噪声图像处理及得到结果(Using matlab image processing, respectively, with a Gaussian filter, mean filter and median filter, image processing on the same noise and get results)

说明：  小波包分解重构后，求节点能量，matlab中运行很好(After the wavelet packet decomposition and reconstruction, the node energy is calculated, which runs well in matlab.)

说明：  Adaboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器（强分类器）。(AdaBoost is an iterative algorithm. The core idea of AdaBoost is to train different classifiers (weak classifiers) for the same training set, and then combine these weak classifiers to form a stronger final classifier (strong classifier).)

格式 [sqnr,a_quan,code]=u_pcm(a,n) 输入样值序列a 、量化电平数目n, 程序计算量化间隔、进行均匀量化、进行编码、计算量化信噪比， 返回量化信噪比squn、编码前的量化序列a_quan、编码后的码序列code。 (Format [sqnr, a_quan, code] = u_pcm (a, n) input sample value sequence a, the number of quantization levels n, the calculation procedures to quantify interval, the uniform quantization, coding, computing quantization noise ratio, return quantization noise ratio squn, to quantify the pre-coding sequence a_quan, coding sequences after the code.)