IQGA

说明：  数字通信系统中采用基带传输和频带传输[ 1, 2 ] ,在抽样判决器之前,信号叠加了信道噪声. 本文利用 Matlab软件仿真[ 3, 4 ]分析了在判决器前叠加了高斯噪声后,信噪比与系统的误码率之间的关系.(Digital communication system using baseband transmission and band transmission [1, 2], in a sample before the decision device, the signal superimposed on the channel noise. In this paper, Matlab software simulation [3, 4] of the device in the sentence before the superposition of the Gaussian noise After the SNR and BER relationship.)

IMAGE SOURCE CODING & DECODING In This code The Coding Technique Used Is Huffman Coding Data Input Is A Gray Image The Symbols Are The Intensity Levels Of The Pixels (256 Intensity)

四种不同的算法在实现多项式相乘时的时间复杂度比较(Four different algorithms, when multiplied in the realization of polynomial time complexity comparison)

利用kalman实现目标跟踪的MATLAB源代码，能够实现。(Target tracking using kalman the MATLAB source code, can be achieved.)

大学数值分析算法，重要参数的提取，采用的是脉冲对消法，基于kaiser窗的双谱线插值FFT谐波分析，可以动态调节运行环境的参数，三相光伏逆变并网的仿真，相关分析过程的matlab方法，结合PCA的尺度不变特征变换(SIFT)算法。( University of numerical analysis algorithms, Extract important parameters, It uses a pulse of consumer law, Dual-line interpolation FFT harmonic analysis kaiser windows, Can dynamically adjust the parameters of the operating environment, Three-phase photovoltaic inverter and network simulation, Correlation analysis process matlab method, Combined with PCA scale invariant feature transform (SIFT) algorithm.)

Matlab小波滤波程序及演示程序，很好的学习资料，有实例和仿真(Matlab wavelet filtering procedure and demonstration program, good learning materials, there are examples and simulation)

VITERBI AND (4,3,2)SOFT-DECODIING

在仿真中，我们规定每个移动节点的发射功率是常数，PTx = 1W；接收节点接收机的灵敏度Smin = -80 dBm；信号质量 min = 2 dB；系统的背景噪声门限INThre = 1.2e-10(In the simulation, we require that each mobile node s transmission power is constant, PTx = 1W receiver node receiver sensitivity Smin =-80 dBm signal quality )

，对于一个使用相关器的二进制通信系统，用monte carlo仿真估计Pe并且画出Pe与SNR的对比图~(for a binary communication systerm，use monte carlo simulation draw the Pe and the compared of Pe and SNR~)

将K均值算法用于图像分割,输入的是彩色图像,转换为灰度图像进行分割,输出结果为灰度图像.利用灰度做为特征对每个像素进行聚类,由于光照等原因,有时应该属于一个物体的像素,其灰度值也会有很大的差别,可能导致对该像素的聚类发生错误.在分割结果中,该物体表面会出现一些不同于其它像素的噪声点,因此,算法的最后,对结果进行一次中值滤波,以消除噪声,达到平滑图像的作用(The K means algorithm for image segmentation, the input is a color image, convert to grayscale image segmentation, the output of grayscale images. The use of gray as the characteristics of each pixel clustering, due to light and other reasons, and sometimes should belong to an object pixel, its gray value will also be very different, may lead to clustering of the pixel error has occurred. in the segmentation results, the surface, there would be different from other pixel noise points, so , the algorithm Finally, the results of a median filter to eliminate noise, to the role of smoothing the image)