main_program_time

matlab与c联合开发类 VC调用matlab(call matlab in VC++6.0)

智能天线自适应RLS算法matlab仿真(Smart Antenna Adaptive RLS algorithm simulation)

This program is for reducing the noise in ultrasonic images by using redundant wavelet transform with bilateral filtration approach.

改进的小波包特征熵，经本人试用，完全能够求解特征熵(Improved wavelet packet feature entropy, by my trial, fully able to solve the feature entropy)

该程序是硬核聚类和自适应硬核聚类的程序。(The program is hard-core hard-core clustering and adaptive clustering procedure.)

说明：  基于matlab的fsk调制的代码，可以实现仿真(it s fsk)

这是matlab的相关ppt，基本的一些操作和讲解，方便初学者更快的熟悉。(This is related to matlab ppt, and explain some of the basic operations, faster familiar easy for beginners.)

Image Compression A collection of simple routines for image compression using different techniques. BTCODE: Image compression Using Block Truncation Coding. PYRAMID: Image compression based on Gaussian Pyramids. DCTCOMPR: Image compression based on Discrete Cosine Transform. IMCOMPR: Image compression based on Singular Value Decomposition. The given codes can be also used in 2D noise suppression. Notes: The function conv2fft performs a 2D FFT-based convolution. Type help conv2fft on MATLAB command window for more informations.

信号包络检测分析的matlab程序.信号包络是信号的一个重要特征,对其进行正确检测可以提取出信号的一些重要的信息.(Matlab code of envelope detection for signal analysis.Envelope of signals is an important characteristic,and detecting it correctly can extract the important information of a signal.)

说明：  通过并行压缩进行二维信号压缩，提出了一种利用并行压缩传感技术对二维信号进行压缩的新方案。根据这个原理，译码器的重新构造可以并行进行。通过对二维信号进行某些排列，可以确保所有列具有大致相同的密度级别，并且可以使用相同的测量矩阵进行采样。这样可以避免二维信号的矢量化，从而大大减小测量矩阵的尺寸。我们证明，如果排列良好，重建均方误差的上限会更紧。为了说明这一方案，我们将其应用于视频压缩并使用不同帧的排列有两种：参考帧的基于Z字形扫描的排列和非参考帧的基于块测试的排列。仿真结果表明，在相同的压缩比下，峰值信噪比比比在无置换情况下可提高3-7db左右。(we propose a new scheme to compress 2D signals using parallel compressed sensing. According to this seheme, the reeonstruction at the decoder can be performed in parallel. By performing certain permutation on a 2D signal, all columns are insured to have approximately the same density level and can be sampled using the same measurement matrix. In this way, vectorization of a 2D signal can be avoided, and thus the size of the measurement matrix can be dramatically reduced. We prove that with a good permutation, we can have a tighter upper bound on reconstruction mean square error. To illustrate this scheme, we apply it to video compression and use two kinds of permutations for different frames: the zigzag-scanbased permutation for reference frames and the block-test-based permutation for non-reference frames. Simulation results show that under the same compression ratio, the peak signal-to-noise ratio can be improved by approximately 3-7 dB compared to the case without permutation.)