nodules-coverage

说明：  高斯噪声下的8psk误码率检测与理论值比较(Gaussian noise of 8PSK BER detection compared with the theoretical value)

基于matlab实现高斯函数，以及图像模糊恢复，调用matlab自带函数。(Implementation of Matlab based on Gauss function, and the blurred image restoration, call the MATLAB built-in function.)

模拟了一个6cm直径的金属圆柱散射体，在高斯源激励下的模场分布图，采用了PML边界(a 6-cm-diameter metal cylindrical scatterer in free space is modeled. The source excitation is a Gaussian pulse with a carrier frequency of 5 GHz.)

说明：  应用双种群蚁群算法解决TSP问题，计算速度快，准确率高(Application of ant colony algorithm to solve TSP double population problem, computational speed, high accuracy)

基于matlab和LMS/NLMS/SMI方法的自适应均衡(Based on matlab and LMS/NLMS/SMI adaptive equalization method)

基于matlab的同步发电机模型，得到了清晰的同步发电机运行情况下的电压波形图。(Matlab-based synchronous generator model, a clear case of synchronous generators running voltage waveform graph.)

Maximum Likelihood Decoding

ofdm系统仿真 含16qam调制 fft 加窗 加cp等模块 北京邮电大学通信系课设题目(ofdm system simulation)

通过一个强非线性模型比较ukf、ekf、pf、pfekf、pfukf等的性能。(By a strong non-linear model is ukf, ekf, pf, pfekf, pfukf such performance.)

经典的香农采样定理认为,为了不失真地恢复模拟信号,采样频率应该不小于奈奎斯特频率(即模拟信号 频谱中的最高频率)的两倍.但是其中除了利用到信号是有限带宽的假设外,没利用任何的其它先验信息.采集到 的数据存在很大程度的冗余.Donoho等人提出的压缩感知方法(Compressed Sensing或Compressive Sampling, CS)充分运用了大部分信号在预知的一组基上可以稀疏表示这一先验信息,利用随机投影实现了在远低于奈奎斯 特频率的采样频率下对压缩数据的直接采集.该方法不仅为降低采样频率提供了一种新思路,也为其它科学领域 的研究提供了新的契机.该文综述性地阐述了压缩感知方法的基本原理,给出了其中的一些约束问题和估计方法, 并介绍压缩感知理论的相关问题———矩阵填充,最后讨论了其未来可能的应用前景. (According to the conventional Shannon s sampling theorem,in order to represent the analog signal,the sampling rate should not be less than twice the Nyquist sampling rate.Howev- er,this theorem only makes use of the bandwidth information.As a result,the collected data contain many redundant information.The recently proposed sampling method,compressed sens- ing or compressive sampling(CS),can collect compressed data at the sampling rate much lower than that needed in Shannon s sampling theorem by exploring the compressibility of the signal. This paper presents a review on the basic theory of CS.Some of the restrictions and recovery methods in CS are also discussed.Finally,some potential applications based on CS are presented. )