dipole ,loop,horn,polar antenna

matlab编程必备手册，这本书是matlab的一个非常全面而且细致的教程，希望大家好好利用哦(matlab programming required manual matlab this book is a very comprehensive and detailed tutorial, I hope you make good use of O)

LSB Audio Algorithm.. Audio Stegnography code

该程序仿真CDMA接收机的功能，包括解扩，解交织，信道编码(卷积码)，解码，信源编码(CRC)解码5个功能模块，生成用于数字基带传输的信号序列。 仿真CDMA发射机的功能，包括随机序列的产生，信源编码尾比特添加, 信道编码(卷积编码)，分组交织和扩频调制6个功能模块，生成用于数字基带，传输的信号序列，输出还包括用于解调所需要的信源编码尾比特值add_bits，交织器的尾比特值i_add_bits(CDMA receiver functions, including despreading, Xie intertwined, channel coding (convolutional codes), decoded, the source code (CRC) decoder five functional modules, production figures for the base band signal transmission sequence. Simulation CDMA transmitter functions, including random sequence generation, the source coding bit late add, Channel Coding (coding), a spread-spectrum modulation intertwined and six functional modules, production figures for the base-band, The transmission signal sequence, and the output is also included for the modems needed source coding bit value add_bits tail, interleaver value of the last bit i_add_bits)

采用加权网络中节点强度和权重都是幂率分布的模型，旋转机械二维全息谱计算，cordic算法的matlab仿真，计算十字叉丝的在不同距离的衍射图像,混沌的判断指标Lyapunov指数计算,详细画出了时域和频域的相关图。

在确定约束条件时将灰度约束和光滑约束与可积性约束分开，使得计算公式和求解过程更加 简单，同时加快了迭代收敛速度，并能有效地防止迭代的发散。

背景建模和前景分割的方式把运动车辆提取出来。并进行最近临关联，输出目标轨迹。 MeanShift运动目标跟踪 matlab程序 1.截取跟踪目标矩阵rect 2.求取跟踪目标的加权直方图hist1 3.读取视频序列中的一帧， 先随机取一块与rect等大的矩形，计算加权直方图hist2。 4.计算两者比重函数，如果后者差距过大， 更新新的矩阵中心Y，进行迭代（MeanShift是一种变步长可以迅速接近概率密度峰值的方法），直至一定条件后停止。 这两种算法为处理图像跟踪的基本方法 (Background modeling and foreground segmentation the extracted moving vehicles. Pro associated output and the target locus. Target tracking matlab program MeanShift movement. Interception tracking the target matrix rect strike a tracking target weighted histogram hist1 3. Reads a video sequence, randomly take a with the rect large rectangular, calculate the weighted histogram hist2. 4. Calculate both the proportion of function, if the latter gap is too large to update the new center of the matrix Y conducted the iteration (MeanShift is a variable step size can be rapidly approaching the peak of the probability density) until certain conditions to stop. The basic method of these two algorithms for processing the image tracking)

This function completes what I have started with the functions variogram and variogramfit. It is not intended to be a highly optimized code for performing kriging but may have educational value. Note that for using kriging, you should download the latest version of variogramfit. kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform an ordinary kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the algor

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

simulink下茶油发电机模型 采用PID自适应控制，适合船舶电力推进发电机( Diesel generator)

说明：  LMD分解程序 能有效解决EMD的模态混叠现象，LMD结合样本熵，里面有个例子(The LMD decomposition program can effectively solve the modal aliasing phenomenon of EMD. LMD combines with sample entropy, and there is an example)