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Description: to understand the Algorithm go to matlab help in page dct2 and idct2 to get the mathematical expression
for M = N = 8, we can calculate the most of hard values and save it as LUTs to speed up the execution
now compare our special 8X8 functions with the internal general functions
use this code:
A = int32(100*rand(8,8))
tic for i = 1 : 1000 IDCT_8X8(DCT_8X8(A)) end toc
tic for i = 1 : 1000 idct2(dct2(A)) end toc
i had built the general functions too, but with low speed
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Size: 1180 |
Author: hujik |
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Description: 使用gabor变换作语音识别，下载自剑桥的实验室的网站，有兴趣的可以去网站上找资料-used for voice recognition, Cambridge downloaded from the website of the laboratory, interested parties can go to find information on the web site
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Size: 25600 |
Author: 江上 |
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Description: to understand the Algorithm go to matlab help in page dct2 and idct2 to get the mathematical expression
for M = N = 8, we can calculate the most of hard values and save it as LUTs to speed up the execution
now compare our special 8X8 functions with the internal general functions
use this code:
A = int32(100*rand(8,8))
tic for i = 1 : 1000 IDCT_8X8(DCT_8X8(A)) end toc
tic for i = 1 : 1000 idct2(dct2(A)) end toc
i had built the general functions too, but with low speed -to understand the Algorithm go to matlab help in page dct2 and idct2 to get the mathematical expressionfor M = N = 8, we can calculate the most of hard values and save it as LUTs to speed up the executionnow compare our special 8X8 functions with the internal general functionsuse this code: A = int32 (100* rand (8,8)) tic for i = 1: 1000 IDCT_8X8 (DCT_8X8 (A)) end toc tic for i = 1: 1000 idct2 (dct2 (A)) end toc i had built the general functions too, but with low speed
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Size: 1024 |
Author: hujik |
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Description: n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.-n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar-xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
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Size: 13312 |
Author: 徐剑 |
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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: The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
-The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar-xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
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Size: 202752 |
Author: 晨间 |
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Description: In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
-In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar-xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
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Size: 129024 |
Author: 晨间 |
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Description: In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
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Size: 197632 |
Author: 晨间 |
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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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Description: 对于GPS导航的一个很好的MATLAB代码,用一次你就知道这个好用了,你去试下-GPS navigation for a good MATLAB code, used once you know this easy to use, you go to trial under the
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Size: 5120 |
Author: 李涛 |
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Description: 函数再现机构设计
试设计一曲柄摇杆机构，再现函数
要求：
输入构件的转角范围180°，输出构件摆角范围30°，即：
当输入构件从a转至a+90时，输出构件从b转至b+30
当输入构件从a+90转至a+180时，输出构件从b+30转至b
-Function reproduce the design of mechanism design test one crank-rocker mechanism, reproduction function requirements: the corner of the scope of the importation of component 180 °, the output component swinging angle range 30 °, namely: When the input from a component to a+ 90, the output components from the b Go to b+ 30, when the importation of components from a+ 90 to a+ 180, the output component from b+ 30 to b
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Size: 4096 |
Author: 王神仙 |
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Description: 地震模拟资料数字化的过程中，会存在数据打折、台阶等现象，该程序用于把数字化后的数据去打折、去台阶-Earthquake simulation data digitization process, there will be discounts of data, step of such a phenomenon, the program for the digital data to a discount, go to step
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Size: 14336 |
Author: liuzemin |
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Description: 这是MATLAB小型的。下下去看看嘛，活血对你以后的学习有帮助-This is a small MATLAB. To see them go under, Huoxue after learning of your help
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Size: 10038272 |
Author: 雨林 |
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Description: 很好的OFDM的基于MATLAB的仿真程序包，且包含了最终结果图.-montecarlo
type montecarlo in the command window and wait for a long time..
_simulation of the complete OFDM system.
_use of a very large file in order to get probabilities.
_loop over different value of the noise.
_compute the SNR for each value of the noise.
_provide the SNR/BER plot.
if you are in a rush : simulation_system !!
go into the right folder and type simulation_system in the command window.
_then type the value of the noise power (range = [-20,10])
_the function provides the channel estimation, the bit allocation,
and a plot illustrating the errors.
_this is a fast function (less pilot, no synchronization, small file).
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Size: 96256 |
Author: 田静 |
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Description: 在matlab7.0下沒問題，有性趣的人可以拿回去參考參考。-Matlab7.0 no problem in the next, there are interested people can go back and refer to the reference holding.
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Size: 2048 |
Author: allen |
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Description: 图像特征识别通过神经网络训练方法实现，是学习参考的好资料-you will need first to run the file that name "charGUI4.fig" and on the right side there is a load training set where you have to train the system first, run any data that is should be from 1 to 9 and 0 like ( 1 2 3 4 5 5 6 7 8 9 0) as many line as you want so if you have a traning data that is 10 columns by 5 lines then you have to assign that to the small boxes that name column and line. right now keep the#words as is because I plan to use it but I change my mind so that it is automatically count the word for you.
after you train the system then load any image test, from the "Load Image" once you upload the image then choose the "select" and then go to the uploaded image and select whatever you want to recognize, after that select "crop" to crop the exact part that you want to recognize after that selcet "Features Extraction" to extract the features after that choose the "recognize" to see the results up on the result box.
P.S. keep in mind that the program has been tested on the
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Size: 709632 |
Author: 反对撒 |
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Description: PEER TO PEER P2P 点到点 多线程断点续传的实现。 珍贵资料啊~~
如果不能下载 请到http://www.aibao28.com 免费下载-PEER TO PEER P2P point-to-multi-threaded HTTP implementation. Valuable information ah ~ ~ If you can not download the free download go to http://www.aibao28.com
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Size: 62464 |
Author: 张红静 |
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Description: 轨迹跟踪
用PID控制器或者模糊控制器来控制小车的行走。
编写了小车能绕过障碍并且延者给定的轨迹行走，其中小车的轨迹要事先给定。
-Trajectory tracking fuzzy controller with PID controller to control the car or walking. Prepared a car can go around the obstacle and the extension of those who walk a given trajectory, which tracks car to be given in advance.
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Size: 428032 |
Author: 徐晴 |
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