guihua

Encryption algorithms are becoming more necessary to ensure data is securely transmitted over insecure communication channels. FOX is a recently developed algorithm and its structure is based on the already proven IDEA (International Data Encryption Algorithm) cipher. FOX is a symmetric (private key) block cipher. Its top-level structure uses the Lai-Massey scheme and the round functions used in the scheme are substitution permutation networks (SPN). Its flexibility lies in the fact that it can be efficiently implemented in hardware and software. We report some of the first results of implementing the cipher on an FPGA

some introduction of matlab

工程计算可视化与MATLAB实现:本书列举了大量的算例，并附有数值解和图形解，使学生通过上机学到数学和相关领域的知识。(Engineering Computing and Visualization in MATLAB: This book lists a number of examples, along with numerical and graphical solution, so that students learn mathematics through the machine and the related fields of knowledge.)

说明：  遗传算法，matlab实现。包含一整套源码(Genetic algorithm, matlab implementation)

MATLAB系统仿真验证与工程应用研讨会会议资料,具有较高的参考价值(MATLAB system simulation and data engineering workshop meeting, with a high reference value)

立体视觉方向 透视投影矩阵 基础矩阵等地求解函数(The stereovision direction perspective projection matrix fundamental matrix to solve the function)

qpsk调制解调simulink文件，亲测可用(qpsk modem simulink file)

MATLAB程序实现图像增强、图像去噪等图像处理。(Image Enhancement and Image Denoising)

This MATLAB M-file will produce the FDTD solution of a sectoral (2-D) Perfectly Electric Conducting (PEC) horn antenna excited by a sinusoidal voltage in a TEz computational domain.(This MATLAB M-file will produce the FDTD solution of a sectoral (2-D) Perfectly Electric Conducting (PEC) horn antenna excited by a sinusoidal voltage in a TEz computational domain.)

ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model see help icp for more information (ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model- matrix with model points, data- matrix with data points, OUTPUT: R- rotation matrix and T- translation vector accordingly so newdata = R*data+ T . newdata are transformed data points to fit model see help icp for more information )