详细介绍基于OFDM的各种信道估计算法及其复杂度和适用性。

讲述了OFDM的一些基本知识，包含框图，及OFDM的优缺点等。对OFDM有个全面认识。(OFDM about some of the basics, including block diagram, and the advantages and disadvantages of OFDM and so on. Have a comprehensive understanding of OFDM.)

说明：  在MATLAB环境中，对航海雷达中频信号建模，并完成ＣＡ恒虚警处理。(In the MATLAB environment, on marine radar IF signal modeling, and complete the CA CFAR.)

为作业最终的仿真结果与分析 PL_free.m：free space path loss model PL_logdist.m: log-distance path loss model PL_lognorm.m: log-normal shadowing path loss model plot_PL_general.m: plot the figures itum.m: ITU M.1225 vehicular-B(120km/hr) channel model AWGN.m: create AWGN channel object(The final for the job simulation results and analysis PL_free.m: free space path loss model PL_logdist.m: log-distance path loss model PL_lognorm.m: log-normal shadowing path loss model plot_PL_general.m: plot the figures itum.m: ITU M.1225 vehicular-B (120km/hr) channel model AWGN.m: create AWGN channel object)

Matrix Converter Commutation Strategies With and Without Explicit Input Voltage Sign Measurement

人脸识别，使用深度学习中的DBN算法进行人脸识别算法，数据集是ORL人脸数据集。(FACE Recognition, using the depth learning DBN algorithm recognition algorithm, the data set is a ORL face dataset.)

蚁群算法解决公交车路径规划问题的MATLAB代码实现(Ant Colony Algorithm to Solve the Problem of Bus Route Planning with MATLAB Code)

顾名思义，NSCT变换核心是Contourlet，即关于边缘的变换。 所谓非下采样是基于频域的，根据个人理解，这就相当于，对某一图像而言，首先设定一个频率阈值，然后通过滤波器筛选出大于等于这个阈值频率的图像（当然这不是一次性得筛选过程，而是迭代使用非下采样二通道带通滤波器的过程）

M/M/1 排队模型，用MATLAB简单写的(M/M/1 queuing model, built up in matlab)

gram-schmidt与hosehouder算法，stability程序为通过hosehouder算法检验gram-schmidt算法的正确性，适合工程复算(gram-schmidt and hosehouder algorithms, stability test procedures for the adoption of gram-schmidt algorithm hosehouder correctness of the algorithm is suitable for engineering complex operator)

说明：  稳健自适应波束形成，Wiley出版社，是一本有关MVDR波束形成稳健性分析的好书。(The latest research and developments in robust adaptive beamforming Recent work has made great strides toward devising robust adaptive beamformers that vastly improve signal strength against background noise and directional interference. This dynamic technology has diverse applications, including radar, sonar, acoustics, astronomy, seismology, communications, and medical imaging. There are also exciting emerging applications such as smart antennas for wireless communications, handheld ultrasound imaging systems, and directional hearing aids. Robust Adaptive Beamforming compiles the theories and work of leading researchers investigating various approaches in one comprehensive volume. Unlike previous efforts, these pioneering studies are based on theories that use an uncertainty set of the array steering vector. The researchers define their theories, explain their methodologies, and present their conclusions. Methods presented include: Coupling the standard Capon beamformers with a spher)