plsm

This function flat the histogram of a specific window in image

音频水印程序，应用离散余弦变换，同步信号，分段处理。(Audio Watermarking procedures, using the discrete cosine transform, synchronous signal processing section.)

a bc converter alpha be to dq

完整的OFDM自适应资源分配仿真程序，OFDM.m为主程序，修改发送和接受天线数量可实现MIMO OFDM仿真。(Complete OFDM Adaptive resource allocation simulation program, OFDM.m the main program, modify the number of transmit and receive antennas to achieve MIMO OFDM simulation.)

说明：  Matlab2014a最新教程及中英文转换说明，详细讲述了2014a的最新功能。(Matlab2014a latest tutorials and instructions in English and converted, a detailed account of the latest features 2014a.)

Automatic license plate recognition

太阳能电池板VI曲线建模 用matlab的simulink工具 可视曲线(Simulink tool solar panels VI curve modeling using matlab visual curve)

图论中KM算法的源程序，仿真了5个用户5个信道的频谱分配问题(KM algorithm in graph theory of the source, simulation 5 of 5 users of spectrum allocation channel)

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

用c程序和matlab分别试验一种k-means改进算法，按照方法选取聚类中心点，事实证明，这种改进是有效的。(Matlab with c procedures and were experimenting with an improved k-means algorithm, in accordance with the method of selecting cluster center, the facts show that this improvement is effective.)