 Connection
Mithun Diwakar to Algorithms
This is a "connection" page, showing publications Mithun Diwakar has written about Algorithms.
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0.461 |
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Diwakar M, Tal O, Liu TT, Harrington DL, Srinivasan R, Muzzatti L, Song T, Theilmann RJ, Lee RR, Huang MX. Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer. Neuroimage. 2011 Jun 15; 56(4):1918-28.
Score: 0.213
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Cai C, Diwakar M, Chen D, Sekihara K, Nagarajan SS. Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging. IEEE Trans Med Imaging. 2020 03; 39(3):567-577.
Score: 0.095
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Diwakar M, Huang MX, Srinivasan R, Harrington DL, Robb A, Angeles A, Muzzatti L, Pakdaman R, Song T, Theilmann RJ, Lee RR. Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG. Neuroimage. 2011 Jan 01; 54(1):253-63.
Score: 0.051
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Cai C, Long Y, Ghosh S, Hashemi A, Gao Y, Diwakar M, Haufe S, Sekihara K, Wu W, Nagarajan SS. Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution. IEEE Trans Med Imaging. 2023 09; 42(9):2502-2512.
Score: 0.031
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Cai C, Kang H, Hashemi A, Chen D, Diwakar M, Haufe S, Sekihara K, Wu W, Nagarajan SS. Bayesian Algorithms for Joint Estimation of Brain Activity and Noise in Electromagnetic Imaging. IEEE Trans Med Imaging. 2023 03; 42(3):762-773.
Score: 0.030
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Cai C, Hashemi A, Diwakar M, Haufe S, Sekihara K, Nagarajan SS. Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm. Neuroimage. 2021 01 15; 225:117411.
Score: 0.026
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Huang MX, Huang CW, Robb A, Angeles A, Nichols SL, Baker DG, Song T, Harrington DL, Theilmann RJ, Srinivasan R, Heister D, Diwakar M, Canive JM, Edgar JC, Chen YH, Ji Z, Shen M, El-Gabalawy F, Levy M, McLay R, Webb-Murphy J, Liu TT, Drake A, Lee RR. MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images. Neuroimage. 2014 Jan 01; 84:585-604.
Score: 0.016
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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