Matlab-engineering-application

有关MATLAB的插值，拟合和线性回归的学习教程。(The MATLAB interpolation, fitting, and linear regression learning tutorial.)

random integer selaction by secc metod

论文使用一种经典的特征提取方法—主成分分析法（PCA）进行特征提取，其基本思想是降维。降维后的数据除了计算工作量减少之外不会减少原始数据所包含的有效信息量。(This paper use a classical method for feature extraction—Principal Component Analysis(PCA)with the basic idea of dimensionality reduction(it still contains all valid information).)

ANSYS FE procedures and 3D models for thermal and mechanical simulation of both Laser and TIGwelding processes are presented

(2). 采用汉明窗，分段长度L=32，用修正的周期图法进行谱估计，并分析数据长度N，分段长度M对谱估计结果的影响。((2) using Hamming window, segment length L = 32, the modified periodogram method of spectral estimation and analysis of data length N, the length M of the sub-spectrum estimation results.)

波数域算法又称为距离徙动算法（Range Migration Algorithm，RMA）或-算法，由Cafforio等提出。该算法分析距离处理后信号的二维频谱，通过二维频域匹配滤波进行相位补偿，在完成方位聚焦的同时完全校正距离徙动。由于其中的Stolt变换能克服SAR信号中距离徙动和SRC对斜距的依赖，因此波数域算法是一种理想的成像算法，尤其在处理大斜视角和长合成孔径的SAR数据时。但Stolt变换需要插值，降低了处理效率并引入额外的误差。(The wavenumber domain Algorithm is also called Range Migration Algorithm (RMA) or -algorithm, which is proposed by Cafforio et al.The algorithm analyzes the 2-d spectrum of the signal after the distance processing, carries out phase compensation through the 2-d frequency domain matching filter, and completely corrects the distance migration while completing the azimuth focusing.Because the Stolt transform can overcome the SAR signal range cell migration and SRC dependence on oblique distance, thus wavenumber domain algorithm is an ideal imaging algorithm, especially when dealing with large squint Angle and long synthetic aperture of SAR data.But Stolt transformation requires interpolation, reduces processing efficiency and introduces additional errors.)

说明：  用Matlab实现主成分分析（PCA)算法.(Principal component analysis using Matlab implementation (PCA) algorithm.)

this is the code for repetition code and ostbc code

This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values.

performance improvement with different codes