matlabQ

几个地震勘探用的雷克子波小程序，有正常相位以及最小相位的子波。(Several seismic waves used Leike Zi applet, a normal phase and minimum phase wavelet.)

用matlab编写的深沟球滚动轴承在正常情况下的具体数据分析，分析了该信号的时域、频域、倒谱，循环谱等(Deep groove ball rolling bearings using matlab in normal circumstances, the specific data analysis, analysis of the signals in time domain, frequency domain, cepstrum, the cyclic spectrum)

在matlab上实现任意数据的倒频谱分析，支持各种采样频率(Cepstrum analysis, any data to support a variety of sampling frequency in matlab)

说明：  MATLAB/Asimulink实现T型三电平逆变电路的SVPWM控制，压缩包中含模型与参考论文。matlab版本2018a。(MATLAB/Asimulink to achieve the T - type three - level inverter SVPWM control, compression package with model and reference paper.Matlab 2018a.)

实现信号LMS处理，并输出波形，能够实现信号的处理，可用于声音的处理(It can realize signal LMS processing and output waveform. It can realize signal processing and can be used for sound processing.)

利用matlab软件获得彩色图像的特征值以及生成72维一维向量(Matlab software using color images, as well as the characteristic values to generate one-dimensional 72-dimensional vector)

我编的一个基于bnt的小程序，算bn的联合概率的，供大家参考(based on a series of small bnt procedures, the operator bn joint probability, for your reference)

MATLAB基础与应用书籍，学好基础，掌握未来。(Foundation and Application of matlab)

说明：  程序清单A中收录了产生PPM-TH和PAM-DS这两个信号源的所有函数；程序清单B中收录了加性高斯白噪声（AWGN）信道建模，正交和非正交单脉冲PPM-TH接收机结构，以及反极性PAM-DS单脉冲接收机结构仿真所需的所有函数。(A list of procedures resulting in a collection of PPM-TH and PAM-DS these two signal sources all function procedure includes a list of B in the additive white Gaussian noise (AWGN) channel modeling, orthogonal and non-orthogonal single pulse PPM-TH receiver structure, as well as the anti-polarity PAM-DS monopulse receiver structure simulation all the necessary functions.)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.