PFdemo

Free Space Path Loss Model

建于眉山的同相供电装置相应的电能质量计算程序。(Built in Meishan calculated phase power supply device power quality program.)

Combination of Forecasting Using Modified GMDH and Genetic Algorithm

matlab算最小生成树经典算法prim s算法。(prim s argorism)

斯坦福大学公开课第三课中涉及的局部加权线性回归，用matlab实现了在不同带宽下的对比(Stanford open class third lesson involved locally weighted linear regression, using matlab to achieve a comparison of different bandwidths)

MIMO-OFDM发射机的labview编程实现(Labview programming implementation of MIMO-OFDM transmitter)

应用于非线性系统理论的研究，判定系统是否进入混沌状态(Research applied to nonlinear system theory, determines whether the system enters a chaotic state)

prob of detection vs prob of false alarm

因为MATLAB需要用到处理器的数学运算部分(MATLAB默认用的是INTEL的数学处理单元),故需要设置一下BLAS(Basic Linear Algebra Subroutines,就是"基础线性几何子程序"的意思)环境变量. 首先请确认你的MATLAB的文件夹中有如下文件:atlas_Athlon.dll(AMD系列的请用这个)， atlas_P4.dll(P4的用这个)，atlas_PIII.dll(P3的用这个)，atlas_PII.dll(P2的用这个)，这些是对应处理器的数值运算优化文件 } 先找到 $MATLAB7inwin32目录下的atlas_Athlon.dll（AMD系列CPU使用）和atlas_P4.dll（奔4用）文件，放到MCR目录中（随便），然后设置环境变量 变量名：BLAS_VERSION 变量值：D:MCRv70atlas_Athlon.dll(Because the MATLAB mathematical operations need to use some of the processor (MATLAB is used by default INTEL mathematical processing unit), so you need to set BLAS (Basic Linear Algebra Subroutines, is " based on linear geometry subroutine" means) environment variable. First make sure your MATLAB folder the following files: atlas_Athlon.dll (AMD series please use this), atlas_P4.dll (P4' s with this), atlas_PIII.dll (P3' s use this), atlas_PII.dll ( P2, use this), these are the values corresponding to processor operations to optimize file} first find $ MATLAB7 bin win32 directory atlas_Athlon.dll (AMD series CPU use) and atlas_P4.dll (Pentium 4 with) file, put MCR directory (random), and then set the environment variable variable name: BLAS_VERSION variable value: D: MCR v70 atlas_Athlon.dll)

相比DeconvNet写得比较简洁易懂。但是原来代码里面给的数据似乎没法跑。所幸同作者还有一些代码用到了TCNN，比如action recognition，可以一起下载下来参考。这个代码主要特色就是tiled结构，可以用来参考，然后把里面ICA的优化函数换成RICA，Sparse Coding等等。总之，在这个代码里也了解了不少(比如line search等)。(DeconvNet well written and easy to understand compared to relatively simple. But the original code to the data inside can not seem to run. Fortunately, there are some code with the author uses TCNN, such action recognition, can be downloaded along down the reference. This code is the main feature tiled structure that can be used for reference, and then put inside the ICA optimization functions into RICA, Sparse Coding and so on. In short, this code can also learn a lot (such as line search, etc.).)