biehang_v22

OFDM Signal generation and decoding using 16-QAM .64QAM as the modulation technique. This type of OFDM is particularly useful for WiMAX and other Wireless and Multimedia Standards. Just run the Model and with different SNRs in the AWGN Channel Block observe the performance of the OFDM. (OFDM Signal generation and decoding using 16-QAM .64 QAM as the modulation technique. This type of OFDM is particularly useful for WiMAX and other Wireless and Multimedia Standards. Just run the Model and with different SNRs in the AWGN Channel Block observe the performance of the OFDM.)

说明：  lu分解,可以应用该函数分解矩阵，lu分解,可以应用该函数分解矩阵，(lu fen jie)

实现TD SCDMA传输信道仿真，包括传输信道编码和译码，程序可以运行(Achieve TD SCDMA transmission channel simulation, including the transmission channel encoding and decoding, the program can run)

wavelet based design for matlab

matlabr2008a的下载和安装详细过程，非常好用，比一般网上找的下载安装要好的多了(matlabr2008a the download and installation process in detail, very easy to use, than the general Internet is far better to download and install more)

说明：  车牌识别是目前应用最广泛的识别技术之一大多数是用 Matlab 开发程序 (License plate identification Matlab development program)

说明：  利用FFT对原信号进行变换，然后在频域进行处理，再使用IFFT进行反变换，达到滤波的目的。(use FFT and IFFT to filter)

采用经典的数据关联方法算法实现的对单目标和多目标的跟踪(Classic data association algorithm was adopted to realize the track of single objective and multiple objectives)

做出一个矩形窗和布莱克曼窗，并分析它们的幅度和相频特性(the blackman window function amplitude and phase frequency characteristics)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)