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This an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain

天线接收信号时，突然出现一个干扰信号，并且干扰信号从０°开始，绕９０°，观察ＳＩＮＲ的变化规律(Antenna signal, suddenly appeared a jamming signal, and the interference signal from the 0 ° starting around 90 °, observed variation of SINR)

活动轮廓模型也称蛇形，定义为最小的能量样条曲线(Active contour model, also known as snake, is defined as the minimal energy splines)

This book includes nine examples that represent typical design tasks of an automotive engineer. It shows how The MathWorks modeling and simulation tools, Simulink® and Stateflow,TM facilitate the design of automotive control systems. Each example explains the principles of the physical situation, and presents the equations that represent the system. The examples show how to proceed from the physical equations to the Simulink block diagram. Once the Simulink model has been completed, we run the simulation, analyze the results, and draw conclusions from the study.

The file lib_crc.c contains source code for functions to calculate five commonly used CRC values: CRC-16, CRC-32, CRC-DNP, CRC-SICK, CRC-Kermit and CRC-CCITT. The functions can be freely used.

MATLAB模型：HMIPv6&MIPv6在HOP数增加情况下的额外开销(MATLAB model: HMIPv6 & MIPv6 HOP number increased in case of overhead)

通信原理课程原理课题：数字幅度调制的抗噪声性能。程序运行于MATLAB环境下(communication theory courses principle topics : digital modulation rate of the anti-noise performance. Procedures in the MATLAB environment)

system identification achieved for data sampled at 10 ms through m file

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)

说明：  随机共振的小程序，包含龙格库塔算法，非自适应，基本程序(Small program of stochastic resonance)