corner

第一步是计算输人信号单边功率谱密度（ESD）。使用快 ％速傅里叶（FFr）算法将信号从时域转换到频域。因为FFr算法的输出是离散谱，而这 ％里我们需要的是连续谱，因此需要引人不同的比例因子来实现从离散谱到连续谱的转换。 ％在第二步中，我们利用迭代算法计算出相对于特定阂值的ESD的最高和最低频率，从而 ％估算出被检测信号所占用的带宽。这种算法同时适用于基带信号和已调制信号。最后， ％在第三步中，我们给出了输出图形的原代码。(The first step is calculating the input signal unilateral power spectral density (ESD). The use of fast Fourier speed (FFr) algorithm for the signal conversion from time domain to frequency domain. FFR algorithm because the output is a discrete spectrum, which we need is a continuous spectrum, so the need for food of different scale factor to achieve from the discrete spectrum to continuous spectrum conversion. In the second step, we use iterative algorithm to calculate a specific threshold in relation to the ESD of the highest and lowest frequency, thus estimated to be occupied by the signal detection bandwidth. This algorithm applies to both baseband signal and modulation signals. Finally, in the third step, we give the graphical output of the original code.)

说明：  计算给定矩阵的互信息，这时的相关性是利用互信息来判断，矩阵的形式为邻接矩阵(compute the mutual information of the given matrix such as the matrix the elements in it are 0 or 1.)

分块RK法求解一介偏微分方程的matlab程序，参数自己设计即可，包括自己设定的函数。(Block RK method for solving partial differential equations ordinary matlab program, the parameters can be of their own design, including its own set of functions.)

雷达ofdm信号 采用多脉冲发射 利用通信原理中ofdm多载频技术 绝对好用 毕设代码(radar ofdm signal)

一种压缩感知重构算法ROMP 需要已知稀疏度(A compressed sensing reconstruction algorithm ROMP needs known sparsity)

Creates wireless sensor network in matlab

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

说明：  很详尽的傅里叶变换课件，可以学习下，适合初学者。(Fourier transform is very detailed courseware, you can study, one for beginners.)

dynamic voltage restorer by dq0 control system

生成Gabor滤波器的函数，并用gabor滤波器进行滤波，简单易懂(Generating function Gabor filters and filtered using gabor filters, easy to understand)