FUZZY--LOGIC-EDGE-DETECTION

PLATE Plate mesh in the xy plane

说明：  介绍了一种用于ip电话的自适应回声消除器，采用LMS自适应滤波器实现。首先通过MATLAB进行LMS算法的步长选择,并用生成的符合G.168要求的带限CCS测试信号对回声消除器的性能进行了测试(Introduced a kind of adaptive echo cancellation ip phone device, using LMS adaptive filter. Carried out by MATLAB first step LMS algorithm selection, and use the resulting band-limited meet G.168 requirements of CCS test signal on the performance of echo cancellation devices were tested)

详细介绍了多种图像处理的案例，包括原代码！用于进行图像处理、分析、可视化和算法开发。可用其对有噪图像或退化图像进行去噪或还原、增强图像以获得更高清晰度、提取特征、分析形状和纹理以及对两个图像进行匹配。(Introduced a variety of image processing of cases, including the original code! Used for image processing, analysis, visualization, and algorithm development. Its noisy image can be used or degraded image denoising or restore, and enhance in order to obtain higher resolution images, extract features, analyze shapes and textures as well as the two images to match.)

8085 instruction set

仿真poisson distribution(Simulation poisson distribution)

Implementation of Digital predistortion in simulink using Saleh Memoryless Non-linearity and Least Squares method. I modified inbuilt OFDM block with Raised cosine block

matlab中实现QPSK的调制识别，加入高斯白噪声，绘制出星座图，眼图，计算出信噪比(Modulation Recognition of QPSK in matlab)

一个师兄的毕设，一些自适应信号处理的算法，遗传算法无功优化。( A complete set of brothers, Some adaptive signal processing algorithms, Genetic algorithm based reactive power optimization.)

boost电路仿真，元件采用的是igbt，用触发脉冲触发的(boost simuliation The components are IGBT and triggered by trigger pulse.)

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.