motor

毫米波图像存在分辨率低的缺点，该程序在WRLA算法的基础上，进一步采用投影算法，能有效改善毫米波图像的分辨率(Images acquired from Passive Millimeter-Wave (PMMW) radiometer have poor resolution due to limited aperture dimension. Therefore, Super-Resolution Algorithm (SRA) is a key component for PMMW imager. SRA realizes spectrum extrapolation, however, spectrum in-band is deteriorated too. Wiener-Richardson-Lucy Algorithm (WRLA) recovers spectrum in-band by Wiener filter, and extrapolates spectrum out-of-band by Richardson-Lucy Algorithm (RLA). WRLA can avoid spectrum deterioration in passband, but its ability for spectrum extrapolation is limited also. We propose an improved algorithm to solve this problem. This algorithm, which has built constraints according to characteristics of PMMW imaging, uses Projection-Onto-Convex-Sets (POCS) technique to enhance the WRLA s performance, so it is termed as POCS-WRLA.)

该文件为支持向量机模型参数的matlab代码 支持向量机模型为专门用于处理不平衡数据的成本控制型支持向量机模型(Matlab code for support vector machine model parameters in the file support vector machine model specifically for the cost of dealing with unbalanced data control support vector machine model)

说明：  数学实验matlab的好程序，给matlab的初学者(Good mathematical experiment matlab program to matlab for beginners)

实现白噪声、正弦信号和带有白噪声的正弦信号的自相关函数的分析。(Achieve white noise, white noise with a sinusoidal signal and a sinusoidal signal of the autocorrelation function.)

设计和实现了一个截止频率在10~50Mhz的FIR带通滤波器，并给定了一个混合信号，画出经过带通滤波器滤波后的输出波形(Design & implement an FIR band-pass digital filter with cut-off frequencies 10 MHz & 50 MHz Apply it to a signal, and display related filter and signal waveforms.)

Approaching the Capacity of Wireless Networks through Distributed Interference Alignment中的分布式干扰对齐算法的代码，结果与论文中相同 (Approaching the Capacity of Wireless Networks through Distributed Interference Alignment)

MIMO ofdm完整程序，已仿真过，比较误码率(MIMO ofdm)

5阶delta-sigma Matlab仿真(Fifth-order delta-sigma Matlab simulation)

simulation for one-D FDTD(Basic standard FDTD code for learner)

This file implement PCA algorithm for dimension reduction and clustering techniques. Principle Component Analysis, Dimension reduction, Clustering.