CIC

郑君里编著的信号与系统，书中各种原理的MATLAB仿真与MATLAB仿真的步骤，例程。(Junli edited signals and systems, various principles of the book MATLAB simulation and MATLAB simulation steps, routines.)

对应聚类分析与盲矿体的地球化学预测 为利用矿区地球化学数据进行找矿靶区预测，采用了对应的聚类分析方法(Cluster analysis and blind orebodies corresponding geochemical prediction for the use of data mining geochemical prospecting target prediction, using the corresponding cluster analysis method)

it is for designing filter

this matlab code presents a memory polynommial predistorter and a saleh model for the power amplifier

说明：  4进制PSK仿真子程序,子程序计算4进制PSK调制的误码率(four band PSK simulation subroutine, the four band calculation subroutine PSK modulation BER)

face to face through face-side down until the back face, the only

网上提供的IPIX雷达数据导入matlab中用作算法验证等用途，包含了导入程序和IPIX雷达数据下载地址(Provide online of radar data import IPIX of the matlab algorithm is used for verification purposes, including import procedures and IPIX radar data download address )

matlab 降维工具箱，最新版本。包含各类线性及非线性降维代码，lle,lpp,mvu,isomap,npe等皆在其中。(DRTOOL, by itself, creates a new DRTOOL or raises the existing singleton*. H = DRTOOL returns the handle to a new DRTOOL or the handle to the existing singleton*. DRTOOL( CALLBACK ,hObject,eventData,handles,...) calls the local function named CALLBACK in DRTOOL.M with the given input arguments. DRTOOL( Property , Value ,...) creates a new DRTOOL or raises the existing singleton*. Starting from the left, property value pairs are applied to the GUI before drtool_OpeningFunction gets called. An unrecognized property name or invalid value makes property application stop. All inputs are passed to drtool_OpeningFcn via varargin. *See GUI Options on GUIDE s Tools menu. Choose "GUI allows only one instance to run (singleton)".)

电磁计算中利用FDTD算法的mur吸收边界，数值计算。(FDTD method with Mur ABC)

传统的K-medoids聚类算法的聚类结果随初始中心点的 不同而波动，且计算复杂度较高不适宜处理大规模数据集； 快速K-medoids聚类算法通过选择合适的初始聚类中心改进 了传统K-medoids聚类算法，但是快速K-medoids聚类算法 的初始聚类中心有可能位于同一类簇。为了克服传统的K- medoids聚类算法和快速K-medoids聚类算法的缺陷，提出 一种基于粒计算的K-medoids聚类算法。(The traditional K-medoids clustering algorithm clustering results with different initial center points and volatility, and high computational complexity is not suitable for processing large data sets; K-medoids clustering algorithm by choosing proper initial cluster centers to improve the traditional K-medoids clustering algorithm, but the initial cluster center of K-medoids clustering algorithm can be located in the same cluster. In order to overcome the shortcomings of the traditional K- medoids clustering algorithm and the fast K-medoids clustering algorithm, a K-medoids clustering algorithm based on granular computing is proposed.)