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adaptive MMSE equalizer

A multiplexing system with NT transmit and NR equals NT receive antennas is designed. The main challenge fo systems lies in the non-orthogonality of the transmission channel. This can be solved by turbo processing. Turbo channel encoder is used for converting the bits in its suitable form. Then different M-ary QAM is compared.

民用日传换到儒略日的matlab function。输入民用日的 年，月，日，小时，算得儒略日。(The civil day pass conversion to Julian day of Matlab FUNCTION. Enter the civil day of the year, month, day, hour, can be regarded as the Julian day.)

说明：  长期演进链路级仿真：采取更低复杂度的算法，而且它是基于长期演进系统级仿真的基础上发展起来。(LTE abstracts the Physical layer for lower complexity and is based on the experience built on the LTE System Level simulator.)

本例用五点差分法求解一个二阶偏微分方程，其间用SOR迭代求解(This example uses a five-point difference method for solving second order partial differential equations, with the SOR iterative solution during)

高阶谱分析工具箱，有常见的参数模模型高阶谱估计，DOA估计与时延估计 (Higher order spectral analysis toolbox, common parameter-mode model of higher order spectral estimation of DOA estimation and delay estimation)

This function computes the RMS feature of the signals in x,which are stored in columns. The signals in x are divided into multiple windows of size winsize and the windows are space wininc apart. Inputs x: columns of signals winsize: window size (length of x) wininc: spacing of the windows (winsize) datawin: window for data (e.g. Hamming, default rectangular) must have dimensions of (winsize,1) dispstatus:zero for no waitbar (default) Outputs feat: RMS value in a 2 dimensional matrix dim1 window dim2 feature (col i is the features for the signal in column i of x)

人脑图像边缘提取,比较好用的人脑图像边缘提取matlab仿真程序。(The human brain image edge extraction, relatively easy to use the human brain image edge extraction matlab simulation program.)

起伏地表下的地震波场数值模拟，有限差分法，一层介质，人工吸收边界条件，求取时间波场快照(Numerical simulation of seismic wave fields, finite difference method, a layer of dielectric undulating surface under artificial absorbing boundary conditions, the time to strike a snapshot of the wave field)

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks