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Likelihood

  • * acousticfeatures.m: Matlab script to generate training and testing files from event timeseries. *

    * 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.

    標簽: acousticfeatures timeseries generate training

    上傳時間: 2013-12-26

    上傳用戶:牛布牛

  • 實現PET/SPECT 幻影圖像regression的matlab源代碼 algorithms for Poisson emission tomography PET/SPECT/ Poisson

    實現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

    標簽: Poisson SPECT regression algorithms

    上傳時間: 2014-01-07

    上傳用戶:cuiyashuo

  • 傳感器網絡中基于到達時間差有效的凸松弛方法的穩健定位

    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.

    標簽: 傳感器網絡

    上傳時間: 2016-11-27

    上傳用戶:xxmluo

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