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Description: Demonstrates 1-D FDTD initial state formation. Please edit the FLAGS for demonstration of different cases. BASED ON "1-D Digital Waveguide Modeling for Improved Sound Synthesis".-Demonstrates 1-D FDTD initial state forma tion. Please edit the FLAGS for demonstration o f different cases. BASED ON "1-D Digital Wavegu ide Modeling for Improved Sound Synthesis. "
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Size: 1024 |
Author: chunfenglay |
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Description: The code implements the optical flow algorithm described in Gautama, T. and Van Hulle, M.M. (2002). A Phase-based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering,IEEE Trans. Neural Networks, 13(5), 1127--1136.
The algo proceeds in 3 steps
1. spatial filtering
2. phase gradient estimation
3. IOC using recurrent networks -The code implements the optical flow algor ithm described in Gautama, and T. Van Hulle. M.M. (2002). A Phase-based Approach to the Esti mation of the Optical Flow Field Using Spatial F iltering, IEEE Trans. Neural Networks, 13 (5), 1127-- 1136. The algo proceeds in a three steps. spat ial filtering 2. 3 phase gradient estimation. I OC using recurrent networks
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Size: 658432 |
Author: Jallon |
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Description: 小波变换例子--f(t)=sin(2π×500t)+sin(2π×1000t)+1.5δ(t-165)+1.5δ(t-207)的小波变换-Wavelet Transform example- f (t) = sin (2π × 500t)+ Sin (2π × 1000t)+ 1.5δ (t-165)+ 1.5δ (t-207) Wavelet Transform
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Size: 1024 |
Author: hauyichu |
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Description: 24種解F矩陣的方法,可以用來處理匹配點和重建3D環境-24 F matrix solution methods can be used to handle match point and 3D reconstruction of the environment
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Size: 48128 |
Author: 許不了 |
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Description: 最小二乘曲面拟合程序（m文件），对一组三维数据z=f(x,y)拟合，成为关于x和y的多项式-least squares fitting procedure Surface (m), a group of three-dimensional data z = f (x, y) fitting, be on the x and y polynomial
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Size: 1024 |
Author: 谭彬 |
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Description: 自己手写的图像的FFT变换和DCT变换的MATLAB代码-own handwritten Image Transform FFT and DCT MATLAB code
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Size: 750592 |
Author: Alex |
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Description: This toolbox implements the algorithm in a fairly general way in a C file that can be called from Matlab. It allows to perform the traditional NL-means for denoising (for both B&W and color images) but also to use an arbitrary set of patches to perform the denoising.
-This toolbox implements the algorithm in a fairly general way in a C file that can be called f rom Matlab. It allows to perform the traditiona l NL-means for denoising (for both B
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Size: 1406976 |
Author: 张文国 |
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Description: 结合单片机和M a t l a b 两者的优点，基于事件驱动的中断通信机制，提出一种Matlab 环境下PC 机与
单片机实时串行通信及数据处理的方法；完成单片机数据采集系统与PC 机的RS-232/RS-485 串行通
信及其通信数据的分析处理、文件存储、F I R 滤波及图形显示；简化系统开发流程，提高开发效率。
该方法已成功应用于一个P I C 1 6 F 8 7 6 单片机应用系统实例之中-Combination of single-chip and M atlab strong points of both, based on event-driven interruption of communication mechanism, a Matlab environment PC and MCU in real-time serial communication and data processing methods complete single-chip data acquisition system with PC-RS-232/RS-485 serial communication and analysis of communication data processing, file storage, FIR filtering and graphical display simplify the system development flow, improve development efficiency. The method has been successfully applied to a PIC 1 6 F 8 7 6 examples of single-chip microcomputer application systems
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Size: 151552 |
Author: pengrong |
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Description: 用fibonacci变换方法对图象做置乱或逆置乱，效率极高，在信息隐藏中属常用的方法之一。调用函数为f=fibonacci_t(I,r,s)，其中参数分别为I=被置乱的图像，r=置乱密钥，s=0置乱，s=1逆置乱-Fibonacci transformation method used to do on the image or inverse Scrambling Scrambling, high efficiency in the information hidden in one of the methods are commonly used. Call the function f = fibonacci_t (I, r, s), which parameters were I = have been scrambling the image, r = scrambling key, s = 0 Scrambling, s = 1 inverse Scrambling
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Size: 1024 |
Author: gao |
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Description: 随机访问网的性能仿真，附有详细文档和matlab源码-Random access network performance simulation, with detailed documentation and source code matlab
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Size: 109568 |
Author: 余未 |
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Description: This MATLAB code is an example of how to train the GCRF model
described in "Learning Gaussian Conditional Random Fields for
Low-Level Vision" by M.F. Tappen, C. Liu, E.H. Adelson, and
W.T. Freeman in CVPR 2007. If you use this code in your research,
please cite this paper
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Size: 43008 |
Author: 代松 |
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Description: 盲源分离中的J.F.Cardoso的对角化matlab源程序
-Blind Source Separation of JFCardoso diagonalization matlab source
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Size: 4096 |
Author: 愚人自娱 |
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Description: 遗传算法，寻找f(x,y)=sinx*siny/(x*y)的最大适应值及其相应的位置。精度为0.0001种群数设定为50-Genetic algorithm, to find f (x, y) = sinx* siny/(x* y) the greatest value and its corresponding position. Accuracy of 0.0001 set to 50 the number of stocks
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Size: 1024 |
Author: 黄泉 |
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Description: 光学双稳特性曲线
调制作用:It=Ii*T(phi)
反馈作用:phi=phi_0+K*It
得透射率T(phi)与相移phi的反馈关系是
T(phi)=[phi-phi_0]/[K*Ii]
式中phi_0为初始相移
对于多干涉(F-P干涉)有:
T(phi)=1/[1+F*(sin(phi/2))^2]-Optical bistable characteristic curve modulation role: It = Ii* T (phi) feedback: phi = phi_0+ K* It may transmittance T (phi) and phase-shifting relationship between phi feedback T (phi) = [phi-phi_0 ]/[K* Ii] where phi_0 for the initial phase shift for the multi-interference (FP interference) are: T (phi) = 1/[1+ F* (sin (phi/2)) ^ 2]
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Size: 1024 |
Author: ryo |
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Description: 进行分段三次Hermite插值和分段三次Spline插值，比较F-C取导数方法所得到期收益率曲线逼近中债结算公司的到期收益率曲线的效果的程序-Hermite interpolation for sub-three and three sub-Spline interpolation, comparing FC take derivative curve approximation method, due in settlement of debts due the company s procedures for the effect of yield curve
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Size: 3072 |
Author: youyouhun |
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Description: The program md.f implements a simple molecular dynamics simulation in continuous real space. The velocity Verlet algorithm is used to implement the time stepping. The force and energy computations are performed in parallel, as is the time integration. (The program uses some Fortran90 features, so an F90 compiler may be needed.)
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Size: 2048 |
Author: danielwood |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 16384 |
Author: 徐剑 |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 220160 |
Author: 晨间 |
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Description: This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 348160 |
Author: 晨间 |
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Description: The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
-The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar-xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
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Size: 58368 |
Author: 晨间 |
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