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说明：  数据挖掘聚类算法 (Data Mining Clustering Algorithm)

使用MATLAB对MIMO系统进行辨识，其中有各种算法仿真算例，N4SID，针对多变量系统的一种识别算法(MOESP (Multivariable Output-Error State sPace) is a deterministic method to identify linear time invariant systems. The MOESP method developed by M. Verhaegen and P. Dewilde is based on the LQ decomposition of Hankel matrix formed from input-output data, where L is lower triangular and Q is orthogonal. A Singular Value Decomposition (SVD) can be performed on a block from the L (L22) matrix to obtain the system order and the extended observability matrix. From this matrix it is possible to estimate the matrices C and A of state-space model. The final step is to solve overdetermined linear equations using the least-squares method to calculate matrices B and D.)

matlab指令全集，对学习matlab编程的人很有帮助(The Complete Works of matlab commands for people to learn useful programming matlab)

说明：  一套由matlab编程的基于小波的水印应用图片例子(A set of matlab programming applications based on wavelet image watermark example)

digital to analog converter

多天线通信系统（MIMO）仿真，包括卷积码，循环冗余校验，高斯信道，最大似然译码。(Multi-antenna communication system (MIMO) emulation, comprising convolutional codes, cyclic redundancy check, a Gaussian channel, the maximum likelihood decoding.)

说明：  使用matlab代码来计算复杂网络的拓扑特征——聚类系数(Calculating complex network topological characteristics clustering coefficient)

蚁群聚类算法，这个算法在处理油色谱数据时很有用的(Ant colony clustering algorithm, this algorithm is useful when dealing with the oil used for chromatographic data)

文章主要介绍了利用MATCOM进行VC和MATLAB 混合编程的步骤与优点, 并将其用于SAR 图像处理与质量评估软件开发。该软件通过图形与菜单, 界面用户只需进行简 单的菜单操作就可以得到直观的处理和评估结果, 为开展SAR 图像处理、质量评估研究提供了一个有力的工具。(Article introduces the use of MATCOM for VC++ and MATLAB programming steps and advantages of mixed and used in SAR image processing and quality assessment of software development. The software graphics and menus, just a simple user interface, menu operations can be intuitive handling and evaluation of results, for carrying out SAR image processing, quality assessment study provides a powerful tool.)

the noise deteriorates the interpolation performance in digital images, it is effective to employ denoising prior to the interpolation. In this paper, we propose a novel interpolation framework in which denoising and image sharpening methods are embedded. In the proposed framework, the image is first decomposed using the bilateral filter into the detail and base layers which represent the small and large scale features, respectively. The detail layer is adaptively smoothed to suppress the noise before interpolation and an edge-preserving interpolation method is applied to both layers. Finally, the high resolution image is obtained by combining the base and detail layers. Experimental results show that the proposed algorithm outperforms the conventional methods