* acousticfeatures.m: Matlab script to generate training and testing files from event timeseries. * afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries. * extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information. * extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes. * sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries. * ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module. ?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module. ?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module.
標(biāo)簽： acousticfeatures timeseries generate training
上傳時(shí)間： 2013-12-26
上傳用戶：牛布牛
實(shí)現(xiàn)PET/SPECT 幻影圖像regression的matlab源代碼 algorithms for Poisson emission tomography PET/SPECT/ Poisson regression eml_ emission maximum likelihood eql_ emission quadratically penalized likelihood epl_ emission penalized likelihood
標(biāo)簽： Poisson SPECT regression algorithms
上傳時(shí)間： 2014-01-07
上傳用戶：cuiyashuo
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
標(biāo)簽： 傳感器網(wǎng)絡(luò)
上傳時(shí)間： 2016-11-27
上傳用戶：xxmluo
％ [BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation) ％ Finds a maximum of a function of several variables. ％ fmaxga solves problems of the form: ％ max F(X) subject to: LB <= X <= UB ％ BestPop--------最優(yōu)的群體即為最優(yōu)的染色體群 ％ Trace----------最佳染色體所對(duì)應(yīng)的目標(biāo)函數(shù)值 ％ FUN------------目標(biāo)函數(shù) ％ LB-------------自變量下限 ％ UB-------------自變量上限 ％ eranum---------種群的代數(shù),取100--1000(默認(rèn)1000) ％ popsize--------每一代種群的規(guī)模；此可取50--100(默認(rèn)50) ％ pcross---------交叉的概率,此概率一般取0.5--0.85之間較好(默認(rèn)0.8) ％ pmutation------變異的概率,該概率一般取0.05-0.2左右較好(默認(rèn)0.1) ％ options--------1×2矩陣,options(1)=0二進(jìn)制編碼(默認(rèn)0),option(1)~=0十進(jìn)制編碼,option(2)設(shè)定求解精度(默認(rèn)1e-4)
標(biāo)簽： pmutation BestPop popsize maximum
上傳時(shí)間： 2015-07-16
上傳用戶：Altman
Maximum Security (First Edition) 網(wǎng)絡(luò)安全 英文版
標(biāo)簽： Security Maximum Edition First
上傳時(shí)間： 2015-07-23
上傳用戶：c12228
Classify using the maximum-likelyhood algorithm
標(biāo)簽： maximum-likelyhood algorithm Classify using
上傳時(shí)間： 2015-08-28
上傳用戶：日光微瀾
by Jay Kadane。Input:a vector with floats.Output:the maximum submatrix.
標(biāo)簽： submatrix maximum Kadane Output
上傳時(shí)間： 2015-10-13
上傳用戶：彭玖華
Maximum eigenvalue and the corresponding eigenvector.
標(biāo)簽： corresponding eigenvector eigenvalue Maximum
上傳時(shí)間： 2015-10-30
上傳用戶：2525775
The computer program for the maximum entropy estimation of a wave distribution function
標(biāo)簽： distribution estimation computer function
上傳時(shí)間： 2015-11-03
上傳用戶：lwwhust
Amis - A maximum entropy estimator 一個(gè)最大熵模型統(tǒng)計(jì)工具，采用特征進(jìn)行模型構(gòu)建
標(biāo)簽： estimator maximum entropy Amis
上傳時(shí)間： 2013-12-16
上傳用戶：Zxcvbnm
蟲蟲下載站版權(quán)所有 京ICP備2021023401號(hào)-1
