New-Microsoft-Word-Document

除趋势波动分析，非常好的分析工具。除趋势波动分析，非常好的分析工具(distrend fluctuation analysis, very good analysis tool)

BPSK modulation with BER with and without OFDM Here are the files for a OFDM modulation. Where in I have used a BPSK modulation and showed the decrease in BER with OFDM. This project was done as a course project under the Guidance of Dr. Dayalan Kasilingam, Umass Dartmouth. (BER with BPSK modulation with and without O FDM Here are the files for a OFDM modulation. Whe 're in I have used a BPSK modulation and showed the decrease in BER with OFDM. This project was done as a course project under the Guidance of Dr.. Day alan Kasilingam, UMassOnline Dartmouth.)

Bi-dimensional Gabor filter with DC component compensation This version of the 2D Gabor filter is basically a bi-dimensional Gaussian function centered at origin (0,0) with variance S modulated by a complex sinusoid with polar frequency (F,W) and phase P described by the following equation: G(x,y,S,F,W,P)=k*Gaussian(x,y,S)*(Sinusoid(x,y,F,W,P)-DC(F,S,P)), where: Gaussian(x,y,S)=exp(-pi*S^2*(x^2+y^2)) Sinusoid(x,y,F,W,P)=exp(j*(2*pi*F*(x*cos(W)+y*sin(W))+P))) DC(F,S,P)=exp(-pi*(F/S)^2+j*P) File Id: 13776 Average rating: 0.0 Size: 1 KB # of reviews: 0 Submitted: 2007-01-26 Downloads: 274 Subscribers: 0 Keywords: gabor filter Stiven Schwanz Dias (Bi-dimensional Gabor filter with DC compo .. compensation This version of the 2D Gabor f ilter is basically a bi-dimensional Gaussian f unction centered at origin (0, 0) with variance S modulated by a complex sinuso id with polar frequency (F, W) and phase P described by the following equati on : G (x, y, S, F, W, P) = k* Gaussian (x, y, S)* (Sinusoid (x, y, F, W, P)- DC (F, S, P)), where : Gaussian (x, y, S) = exp (-pi* S* 2 ^ (x ^ 2 y ^ 2)) Sinusoid (x, y, F, W, P) = exp (j* (2* pi* F* (x* cos (W) y* sin (W)) P))) D C (F, S, P) = exp (-pi* (F/S) ^ 2* P j) File Id : 13776 Average rating : 0.0 Size :# 1 KB of reviews : 0 Submitted : 2007-01-26 Downloads : 274 Subscribers : 0 Keywords : gabor filter Stiven Schwanz Dias)

MATLAB计算机视觉与深度学习实战-运行视频(As a biological technology, face is difficult to be stolen and infection-free. In addition to that, it enjoys the authentication concealment and is never forgotten. Therefore, authentication based on face recognition will become the mainstream of future development. It has important and extensive application in various fields, and is also a hot topic at present biological characteristics identification technology research. Under the non-restrictive condition, the face recognition rate is not high, and its accuracy needs to be further improved. In this paper, the following aspects is analyzed)

quinto esempi vari per c++ (schemi base)

说明：  一些基于CDMA的编码程序，希望对初学者有一定的帮助。(some CDMA-based coding process, and I hope to beginners helpful to a certain extent.)

Iterative Instrumental variable

PIC程序，电子科技大学粒子模拟课程设计(PIC program, University of Electronic Science and Technology particle simulation curriculum design)

说明：  MATLAB 代码 第1章 BP神经网络的数据分类——语音特征信号分类 第2章 BP神经网络的非线性系统建模——非线性函数拟合 第3章 遗传算法优化BP神经网络——非线性函数拟合 第4章 神经网络遗传算法函数极值寻优——非线性函数极值寻优 第5章 基于BP_Adaboost的强分类器设计——公司财务预警建模 第6章 PID神经元网络解耦控制算法——多变量系统控制 第7章 RBF网络的回归--非线性函数回归的实现 ....等58章(MATLAB code The first chapter is BP neural network data classification -- speech characteristic signal classification The second chapter is the nonlinear system modeling of BP neural network nonlinear function fitting The third chapter, genetic algorithm optimization BP neural network - nonlinear function fitting The fourth chapter, neural network, genetic algorithm, function extreme value optimization nonlinear function extremum seeking The fifth chapter is based on BP_Adaboost's strong classifier design -- the company financial early-warning model The sixth chapter is PID neural network decoupling control algorithm multivariable system control The seventh chapter is the regression of RBF network the realization of nonlinear function regression .........the last is 58 chapters)

可用于解决整数规划方面的问题。用matlab编写的源代码。(integer programming)