binverter

用PIC24F128GA006 制作驱动HT1261显示功能 C语言(Production driver HT1261 with PIC24F128GA006 display C-)

波形产生器,产生方波，正弦波，双曲正割，三角波。高斯，超高斯，亚高斯等等(wave productor)

Pn code generation in simulink

小波包分析提取振动信号中的特征频率，二维声子晶体FDTD方法计算禁带宽度的例子,采用的是通用的平面波展开法。( Wavelet packet analysis to extract vibration signal characteristic frequency, Dimensional phononic crystals FDTD method calculation examples band gap, Using common plane wave expansion method.)

MIMO-OFDM不同信道估计的对比,基于MIMO-OFDM系统下做的信道估计技术的研究。(Contrast MIMO-OFDM channel estimation, channel estimation techniques based on MIMO-OFDM system do.)

振动信号的FFT分析，含有具体的振动算例(FFT analysis of vibration signals, vibration contain specific examples)

用matlab 实现了气缸的充气和放气的仿真，在等温情况和绝热两种情况下分别进行了仿真(Matlab cylinder with a deflated inflatable and Simulation, in isothermal and adiabatic, respectively, both cases were simulated)

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. )

Its a document which describes about the methods of feature extraction

matlab simulationfive least source