dsp_hu

最小均方误差的信道估计程序,对学习LTE很有帮助(LTE_channelestimator_MSE Minimum mean square error of channel estimation procedures )

用matlab编程模拟光滑表面的气体流动效应(Analog programming using matlab-smooth surface of the gas flow effect)

希尔伯特黄变换的代码测试程序，其中主要执行test主程序(Hilbert-Huang Transform code testing procedures, which mainly perform test main program)

LDPC编码的C语言实现。包括校验矩阵，生产矩阵的生成，编码，译码等。(LDPC coding in C language. Including the parity matrix, matrix production generation, encoding, decoding and the like.)

基于量子遗传算法的函数寻优算法的Matlab程序实现(Based on quantum genetic algorithm function optimization algorithm Matlab procedures)

RS编解码过程,并包括进制转换和域的变换,可用于RS码仿真(RS codec process, and includes hexadecimal conversion and transform domain can be used for RS code simulation)

系统后件参数识别，智能控制理论课后作业题解(System parameter identification, intelligent control theory homework problem solutions)

粒子群算法在matlab中应用，很实用，很好用(Particle swarm optimization applications, very useful in matlab)

ES is a symmetric block cipher. The same key is used to encrypt and decrypt the message. The plain text and the cipher text are the same size. The Aes algorithm works as follows: Convert to state array: This step consists of conversion on the matrix onto a state array operating with the sbox. Transformations (and their inverses) AddRoundKey: Computing the round key for each round. SubBytes: Substituting by bytes from Sbox. ShiftRows: Shifting of rows. MixColumns: Exor operation on columns. Key Expansion: Expanding the key to be used on next round.

：介绍了扩展卡尔曼滤波算法和无迹变换(unscented transformation，UT)算法，并对扩展卡尔曼滤波算法(EKF)和无 迹卡尔曼滤波算法(UKF)进行比较，阐明了UKF优于EKF。在此基础上，提出了一种基于Unscented变换(uT)的高斯和滤 波算法，该算法首先通过合并准则得到适当个数的混合高斯模型，逼近系统中非高斯噪声的概率密度(: Introduction of the extended Kalman filter algorithm and unscented transform (unscented transformation, UT) algorithm, the extended Kalman filter algorithm (EKF) and unscented Kalman filter (UKF) for comparison to clarify the UKF is superior to EKF. On this basis, we propose an approach based on Unscented Transform (uT) and the Gaussian filtering algorithm, which first of all, by merging the appropriate number of criteria to be a mixture of Gaussian model, which was close to the system of the Central African Gaussian noise probability density)