LCL1

fastslam 又称为rao-blackwillisd slam方法，源码提供fastslam1.0 和2.0的matlab源码。(fastslam also known as rao-blackwillisd slam way to provide source fastslam1.0 and 2.0 matlab source.)

D:matlab mmeigen.rar特征值(D : Matlab nmm eigen.rar eigenvalue)

大学数字信号处理实验课本例题4.9、7.16的matlab程序答案(University digital signal processing experiment Textbook Example 4.9,7.16 matlab program answers)

Wiley Wooldridge-An Introduction to Multi Agent Systems

Research paper on guided image filtring

混沌粒子群算法用于图像分割，程序已调试好，并用多种图像提供使用。分割效果显著。(Chaotic particle swarm optimization algorithm for image segmentation, the program has been debugged, and use a variety of images available. Segmentation effect is remarkable.)

Full search algorith

these codes read input image and generate the 2D gabor filters, with changeable scale, orientation, perform the convolution with input image.

说明：  matlab环境下，计算圆极化电磁波轴比的程序(calculated circularly polarized electromagnetic wave axis than the program Matlab environment)

CurveLab is a collection of Matlab and C++ programs for the Fast Discrete Curvelet Transform in two and three dimensions. For the 2d curvelet transform, the software package includes two distinct implementations: the wrapping-based transform and the transform using unequally-spaced fast Fourier trans- form (USFFT). Both variants are based on the Curvelet transform as described in ‘New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise C2Sin- gularities’, Comm. Pure Appl. Math. 57 (2004) 219-266. The implementation is also discussed in detail in ‘Fast Discrete Curvelet Transforms’, Multiscale Model. Simul. 5 (2006), no. 3, 861-899. We advise users to become familiar with these references. The two implementations diff er by the choice of spatial grid used to translate curvelets at each scale and angle.(CurveLab is a collection of Matlab and C++ programs for the Fast Discrete Curvelet Transform in two and three dimensions. For the 2d curvelet transform, the software package includes two distinct implementations: the wrapping-based transform and the transform using unequally-spaced fast Fourier trans- form (USFFT). Both variants are based on the Curvelet transform as described in ' New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise C2Sin-gularities' , Comm. Pure Appl. Math. 57 (2004) 219-266. The implementation is also discussed in detail in ' Fast Discrete Curvelet Transforms' , Multiscale Model. Simul. 5 (2006), no. 3, 861-899. We advise users to become familiar with these references. The two implementations differ by the choice of spatial grid used to translate curvelets at each scale and angle.)