SEPIC

摘要:为提高密码的抗攻击性,利用超混沌系统随机性好!可确定再生,且密钥空间较大的特性,提出了一种基于四维连续超混沌系统和扩展映射的实时视频流加密算法"(Abstract: In order to improve the password anti-aggressive use of system randomness hyperchaos good! Regeneration can be identified, and key characteristics of the larger space, a row based on four-dimensional hyperchaotic system and expansion of mapping real-time video stream encryption algorithm )

说明：  IIR低通数字滤波器在MATLAB中的设计方法(Low-pass IIR digital filter design in MATLAB method)

很好的Kalman滤波器生成算法。能够用来生成信号估计、信道估计。对于信号处理以及通信信道估计很有帮助。推荐下载。(Kalman Filter Algorithm for good. Can be used to generate signal estimation, channel estimation. For signal processing and communications channel estimation helpful. Recommended download.)

样本熵是时间复杂度的一个，度量是衡量时间序列复杂度的一个参数(The sample entropy time complexity metric is a parameter of the measure of time series complexity)

说明：  SPGL1: A solver for large-scale sparse reconstruction(% SPGL1: A solver for large-scale sparse reconstruction % Version 1.7 (May 20, 2009) % % Files % spg_bp - Solve the basis pursuit (BP) problem % spg_bpdn - Solve the basis pursuit denoise (BPDN) problem % spg_lasso - Solve the LASSO problem % spg_mmv - Solve the multiple measurement vector (MMV) problem % spg_group - Solve the group-sparse BPDN problem % spgdemo - Demonstrates the use of the SPGL1 solver % spgl1 - Expert driver for BP, BPDN, and LASSO problems % spgSetParms - Set options for SPGL1 % spgsetup - Compile the SPGL1 MEX interfaces % % $Id: Contents.m 1384 2009-05-20 19:09:37Z ewout78 $)

ROOT-MUSIC的matlab仿真源码，基于求根方式的music算法，避免了谱峰搜索，优化了计算复杂度(ROOT-MUSIC for matlab simulation source code, algorithm-based Roots music the way to avoid the peak search, optimization of the computational complexity)

HSUPA channel transmitter and receiver block diagram

用MATLAC做的非线性时间序列预测和平滑,可以用来做非线性模型的预测和估计(MATLAC done using nonlinear time series prediction and smooth, non-linear model can be used to make the projections and estimates)

data set for clustering

支持向量机的matlab实例，对学习支持向量机有很大的帮助。(The matlab example of the support vector machine is of great help to the learning support vector machine.)