jiaquan

追赶法 西安交大 数值分析 梅立泉 编程(solve the equation matlab)

一本MATLAB入门书籍，比中国人自己写的容易懂多了，中国人的书就喜欢丢公式，人家都是用例子的(A MATLAB entry books, written by more than the Chinese themselves easier to understand more, Chinese people like to throw the book on the formula, others are using the example)

说明：  AVS帧内预测算法的MATLAB代码，具体做了水平预测、垂直预测、DC和左上预测、右下预测。(AVS intra-prediction algorithm MATLAB code, the specific level of the forecast done, vertical prediction, DC prediction and the upper left, lower right forecast.)

learn matlab in easy way

本书详细介绍了AC/DC、AC/AC、DC/DC电路的仿真(This book details the AC/DC, AC/AC, DC/DC circuit simulation)

块对角化算法实现子函数，输入信道信息，输出各用户的预编码向量(BD for MU-MIMO)

基于灰色理论的粒子群优化算法，在matlab7上进行了试用(Based on Grey Theory PSO, a trial on the matlab7)

Chapter 3 Matlab codes from AEMM book

改进后的游程编码，可以继续量化编码转化为二进制数据流，针对一些较为接近的数，通过合适的阈值设定可以近似，从而大大压缩数据流(The improved run-length coding, can continue to quantify the code into binary data stream, for some of the more close to the number set by appropriate threshold can be approximated, thereby greatly compressed data stream)

利用pcnn，每当有一批像素对应的神经元点火，对像素值进行一次修正。第n次点火的所有神经元用矩阵B（n）表示， 已经点火的像素位置标记为‘1’，未点火的标记为‘0’。通过一个3*3的模板滑过B（n），判断若模板内的值全为‘1’或全为‘0’， 则这些像素值不进行处理，否则若模板中心的值为‘1’，则增加该位置的像素值的大小，中心值为‘0’，则减小像素值。该功能由xiugai(B,K)函数实现 Beta取负值来抑制周围的神经元点火，因为输入pcnn（X）的是模糊图像，抑制之后使处理的像素值更加接近原图像的像素值（抑制参数通过试验确定，并不精确） 总之运行结果只是对像素值变化较大的边缘区域进行了像素值的修正。 通过不断调整参数使峰值信噪比有所升高，但是升高得不是太多(Use pcnn, whenever a group of pixels corresponding to the firing of neurons, a correction value for the pixel. Ignition of all the n-th neuron matrix B (n) represents, Already ignition pixel position labeled 1 , unfired labeled 0 . By a 3* 3 templates over the B (n), determines if the value within the template are all 1 or all 0 , These pixel values are not processed, otherwise the value if the template center 1 , then the increase in the size of the pixel values of the position, the center value of 0 , the pixel value is reduced. This function is implemented by xiugai (B, K) function Beta is a negative value around the element to inhibit neuronal firing, because the input pcnn (X) is a blurred image, the pixel values of inhibition after treatment of the pixel value closer to an original image (suppression parameter determined by experiment, is not exact) Results only in short run large changes in the edge region of the pixel values of the corrected pixel values. )