pvcell_1

说明：  MATLAB编程用两种方法模拟光学实验.单缝衍射、杨氏双缝干涉、黑白光栅衍射的计算机模拟；以及用傅立叶变换方法对简单孔径衍射、黑白光栅及正弦光栅夫琅和费衍射的模拟(MATLAB program to simulate the optical in two experiments. Single slit diffraction, Young' s double slit, diffraction grating computer simulation of black and white and Fourier transform method with a simple aperture diffraction, black and white grating and sinusoidal grating Fraunhofer diffraction simulation)

分数时延估计文档对于宽带信号, 基于移相的窄带波束形成方法会导致方向图波束指向的偏移和主波束畸变, 并且无法有效地抑制宽带干扰。该文提出了基于数字时延加抽头延迟线 (tapped delay line , TDL)结构的宽带自适应波束形成方法。 该方法采用数字时延滤波单元补偿宽带信号在波束指向上的相位和包络偏移, 并用基于TDL 的空时二维滤波处理抑制宽带干扰。分析和实验结果表明, 该方法可以有效地合成空间宽带波束并抑制宽带干扰, 提高信干噪比(sig nal-to-interference-plus-noise r atio , SINR)增益。(frac delay estimation)

一种针对稀疏孔径的合成孔径雷达的区域加强成像算法(Region-Enhanced Imaging for Sparse-Aperture Passive Radar)

简单的RTMP Server，能接收发布端发过来的数据(simper RTMP Server)

基于matlab的超像素图像分割算法实现代码，将图像分割为小块表示(Matlab-based super-pixel image segmentation algorithm code, the image is divided into small representation)

无功补偿选址优化问题，实现成本最小，网损最少(Location reactive power compensation optimization problem to minimize costs, minimal losses)

code for object extraction to exatrct name plate

Algoritmo en matlab para leer 3 imagnees d un solo canal ,para formal una imagen a color RGB.

该程序源代码实现了matlab通信系统性能的仿真，调制方式QPSK仿真(The source code implements communication system simulation matlab, modulation QPSK simulation)

文章展示了一种新的方法用于功率系统中短期负载预测。提出的方案使用混沌时间序列分析基于确定性混沌去捕捉复杂的负载行为特征。确定性的混沌允许我们重构一个时间序列并决定输入的变量个数。这篇文章描述了混沌时间序列对日间功率系统峰值的分析。确定性混沌的非线性图形通过多层感知器的神经网络得到。提出的方案在一个例子中具体阐述。(This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behavior. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear mapping of deterministic chaos is identified by the multilayer perceptron of an artificial neural network. The proposed approach is demonstrated in an example.)