ofdm_system

最大化频谱接入机会的能量检测方法的研究-----认知无线电(Access to opportunities to maximize the energy spectrum detection of cognitive radio-----)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

实现二维的MUSIC算法，并精确计算出波达方向，并作出可视图形。(The two dimensional MUSIC algorithm, and calculate the direction of arrival, and make visual graphics)

一段matlab下的LPP算法实现的完整程序，与算法同仁一起分享(LPP for matlab)

attached file is matlab code

英文版的反馈控制理论书籍，因为看着英文比较费劲，所以也没看过，不过这本书应该也是不错的()

Wavelet denoising For using this code need to use signal toolbox and general toolbox in your matlab In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size 512 by 512 and then generate noisy image by adding a Gaussian noise but under the condition of having SNR=20dB by select the suitable value of variance for Gaussian noise formula. Second step is performing wavelet denoising using the hard thresholding (Use the db 6 for four levels) in the condition of finding the optimal thresholding value of T in terms of the SNR obtained. It means that, we should find the highest SNR value by finding the suitable value for threshold. Then we asked to do the same process but this time using soft thresholding. Finally for the last part of question one, we should compare the results of the obtained SNR with the recommendations of 3*sigma for the hard thresholding and 3/2*sigma for the soft thresholding.

Face Detection System Face Detection using gabor feature extraction and neural networks (Face Detection System Face Detection using gabor feature extraction and neural networks)

Wavelet Scale Spectra

利用元包数组来存放计算过程的结果，利用matlab实现多元统计学中的聚类算法（计算距离矩阵）。(million packets using arrays to store the calculation results Matlab achieve multiple use of statistical clustering algorithm (distance matrix calculation).)