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本资料为基于MATLAB和SINMULINK的16点QAM的数据传输研究。内含MATLAB源代码和用分立模块搭建的SINMULINK仿真图。(This information is based on MATLAB and SINMULINK of 16:00 QAM data transmission research. Contains MATLAB source code and the use of discrete simulation module built SINMULINK map.)

报童问题(负指数分布) 几个参数： 卖出报纸挣a元，退回报纸赔b元 卖报量y,订报量z，订报量上限z_up 观察期的总天数t,用于积累天数的ti 总的平均损失量ave_loss，供过于求损失loss1,供小于求损失loss2,ave_loss=loss1+loss2 最佳订报量superz,最小损失量ave_loss_min (newsvender)

摄像机的标定、角点的提取、三维空间的重建、最后生成矫正后无畸变的图形(Camera Calibration，This section takes you through a complete calibration example based on a total of 20 (and 25) images of a planar checkerboard. This example lets you learn how to use all the features of the toolbox: loading calibration images, extracting image corners, running the main calibration engine, displaying the results, controlling accuracies, adding and suppressing images, undistorting images, exporting calibration data to different formats... This example is highly recommended for someone who is just starting using the toolbox. )

Computes the vector gradient of an RGB image

The radially averaged power spectrum (RAPS) is the direction-independent mean spectrum, i.e. the average of all possible directional power spectra. This function computes and plots the RAPS of an input image. The function was written to produce the RAPS for square weather radar reflectivity images so you will have to relabel the plot axes and title to suit your needs. The radially averaged power spectrum provides a convenient means to view and compare information contained in 2D spectra in 1D

语音处理的短时时域处理技术中的短时中值滤波(voice when dealing with the short-time domain processing technology of short-term median filtering)

Contrast Stretching with matlab

说明：  我做的用于非接触电能传输的自适应谐振技术方案，用到了MATLAB仿真，对研究非接触供电，以及学习锁相环的MATLAB仿真有很好的帮助。(Principles of Adaptive Resonance Technology for Contactless Power Transmission System，contactless, adaptive, series resonance, phase-locked loop)

Blocking an image based on the specified window size.

We present in this paper a new method for blind source separation which is adapted to the case where the sources have different morphologies. We show that the morphology diversity concept leads to a new and very efficient method, even in the presence of noise.