susancode

双线性变换巴特沃斯发设计数字带通滤波器。matlab环境下(Bilinear transform hair design digital Butterworth bandpass filter)

说明：  信号的双边带am调制，相干解调仿真。通信原理matlab仿真设计。(Double sideband signal am modulation, coherent demodulation simulation. Communication theory matlab simulation design.)

《MATLAB 7.0编程基础基础》源程序 第4章(Chapter 4 of the " MATLAB 7.0 programming foundation foundation source)

说明：  包含蚁群，模拟退火，遗传等算法解决TSP问题的matlab代码。(Include ant colonies, simulated annealing and genetic algorithm to solve TSP problem matlab code.)

x=GaussianElimination(A,b) GaussianElimination solves system of linear equations of the form Ax = b using Gaussian Elimination method. A = n x n square matrix with the coefficients of the system of equations as the elements of matrix A b = column matrix with length = n

BP神经网络识别手写字符验证码，包括10721张字母、数字样本(BP neural network handwritten character recognition codes, including 10,721 letters, numbers, samples)

说明：  都是一些matlab图形用户界面的设计实例，包括simulink仿真。(are Matlab graphical user interface design examples, including Simulink simulation.)

用MATLAB实现的各种图像压缩方法，有BTCODE，PYRAMID，DCTCOMPR，IMCOMPR等。(MATLAB various image compression method, BTCODE, PYRAMID. DCTCOMPR, IMCOMPR so.)

matlab数字图像处理书籍的源代码 包括各章的书籍上的代码(digital image processing matlab source code books, including books, chapters of the code on the)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)