The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Raghuraman Mudumbai - One of the best experts on this subject based on the ideXlab platform.
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CDC - The maximum Likelihood Estimate for radiation source localization: Initializing an iterative search
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Kidane Yosief, Soura Dasgupta, Raghuraman MudumbaiAbstract:The maximum Likelihood Estimate approach is adopted in this paper for finding the unknown radiation source location and strength. The problem is nonlinear and has to rely on iterative numerical algorithms. Since the problem has possibly multiple local maxima, the initial Estimate in those iterative algorithms plays a critical role in guaranteeing the global optimum. This paper proposes a way to generate such an initial Estimate which is easy to calculate. Besides some insights that justifies the proposed approach, it is shown that the proposed initial Estimate actually converges to the true but unknown maximum Likelihood Estimate asymptotically thus ensuring that the initial Estimate is indeed in a neighborhood of the maximum Likelihood Estimate and consequently the convergence to the global optimum by local iterative numerical algorithms.
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The maximum Likelihood Estimate for radiation source localization: Initializing an iterative search
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Kidane Yosief, Soura Dasgupta, Raghuraman MudumbaiAbstract:The maximum Likelihood Estimate approach is adopted in this paper for finding the unknown radiation source location and strength. The problem is nonlinear and has to rely on iterative numerical algorithms. Since the problem has possibly multiple local maxima, the initial Estimate in those iterative algorithms plays a critical role in guaranteeing the global optimum. This paper proposes a way to generate such an initial Estimate which is easy to calculate. Besides some insights that justifies the proposed approach, it is shown that the proposed initial Estimate actually converges to the true but unknown maximum Likelihood Estimate asymptotically thus ensuring that the initial Estimate is indeed in a neighborhood of the maximum Likelihood Estimate and consequently the convergence to the global optimum by local iterative numerical algorithms.
Kidane Yosief - One of the best experts on this subject based on the ideXlab platform.
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CDC - The maximum Likelihood Estimate for radiation source localization: Initializing an iterative search
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Kidane Yosief, Soura Dasgupta, Raghuraman MudumbaiAbstract:The maximum Likelihood Estimate approach is adopted in this paper for finding the unknown radiation source location and strength. The problem is nonlinear and has to rely on iterative numerical algorithms. Since the problem has possibly multiple local maxima, the initial Estimate in those iterative algorithms plays a critical role in guaranteeing the global optimum. This paper proposes a way to generate such an initial Estimate which is easy to calculate. Besides some insights that justifies the proposed approach, it is shown that the proposed initial Estimate actually converges to the true but unknown maximum Likelihood Estimate asymptotically thus ensuring that the initial Estimate is indeed in a neighborhood of the maximum Likelihood Estimate and consequently the convergence to the global optimum by local iterative numerical algorithms.
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The maximum Likelihood Estimate for radiation source localization: Initializing an iterative search
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Kidane Yosief, Soura Dasgupta, Raghuraman MudumbaiAbstract:The maximum Likelihood Estimate approach is adopted in this paper for finding the unknown radiation source location and strength. The problem is nonlinear and has to rely on iterative numerical algorithms. Since the problem has possibly multiple local maxima, the initial Estimate in those iterative algorithms plays a critical role in guaranteeing the global optimum. This paper proposes a way to generate such an initial Estimate which is easy to calculate. Besides some insights that justifies the proposed approach, it is shown that the proposed initial Estimate actually converges to the true but unknown maximum Likelihood Estimate asymptotically thus ensuring that the initial Estimate is indeed in a neighborhood of the maximum Likelihood Estimate and consequently the convergence to the global optimum by local iterative numerical algorithms.
Corianne Rogalsky - One of the best experts on this subject based on the ideXlab platform.
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the relationship between the neural computations for speech and music perception is context dependent an activation Likelihood Estimate study
Frontiers in Psychology, 2015Co-Authors: Arianna N Lacroix, Alvaro Diaz, Corianne RogalskyAbstract:The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel's Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch's neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation Likelihood Estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation Likelihood Estimates within each category for music vs. speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music.
Soura Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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CDC - The maximum Likelihood Estimate for radiation source localization: Initializing an iterative search
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Kidane Yosief, Soura Dasgupta, Raghuraman MudumbaiAbstract:The maximum Likelihood Estimate approach is adopted in this paper for finding the unknown radiation source location and strength. The problem is nonlinear and has to rely on iterative numerical algorithms. Since the problem has possibly multiple local maxima, the initial Estimate in those iterative algorithms plays a critical role in guaranteeing the global optimum. This paper proposes a way to generate such an initial Estimate which is easy to calculate. Besides some insights that justifies the proposed approach, it is shown that the proposed initial Estimate actually converges to the true but unknown maximum Likelihood Estimate asymptotically thus ensuring that the initial Estimate is indeed in a neighborhood of the maximum Likelihood Estimate and consequently the convergence to the global optimum by local iterative numerical algorithms.
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The maximum Likelihood Estimate for radiation source localization: Initializing an iterative search
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Kidane Yosief, Soura Dasgupta, Raghuraman MudumbaiAbstract:The maximum Likelihood Estimate approach is adopted in this paper for finding the unknown radiation source location and strength. The problem is nonlinear and has to rely on iterative numerical algorithms. Since the problem has possibly multiple local maxima, the initial Estimate in those iterative algorithms plays a critical role in guaranteeing the global optimum. This paper proposes a way to generate such an initial Estimate which is easy to calculate. Besides some insights that justifies the proposed approach, it is shown that the proposed initial Estimate actually converges to the true but unknown maximum Likelihood Estimate asymptotically thus ensuring that the initial Estimate is indeed in a neighborhood of the maximum Likelihood Estimate and consequently the convergence to the global optimum by local iterative numerical algorithms.
F. W. Cathey - One of the best experts on this subject based on the ideXlab platform.
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The Iterated Kalman Filter Update as a Gauss???Newton Method
IEEE Transactions on Automatic Control, 1993Co-Authors: F. W. CatheyAbstract:It is shown that the iterated Kalman filter (IKF) update is an application of the Gauss-Newton method for approximating a maximum Likelihood Estimate. An example is presented in which the iterated Kalman filter update and maximum Likelihood Estimate show correct convergence behavior as the observation becomes more accurate, whereas the extended Kalman filter update does not
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The iterated Kalman filter update as a Gauss-Newton method
IEEE Transactions on Automatic Control, 1993Co-Authors: B.m. Bell, F. W. CatheyAbstract:It is shown that the iterated Kalman filter (IKF) update is an application of the Gauss-Newton method for approximating a maximum Likelihood Estimate. An example is presented in which the iterated Kalman filter update and maximum Likelihood Estimate show correct convergence behavior as the observation becomes more accurate, whereas the extended Kalman filter update does not.
