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Physics-driven quantized consensus for distributed diffusion source estimation using sensor networks.

, and . EURASIP J. Adv. Signal Process., (2016)

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Solving physics-driven inverse problems via structured least squares., and . EUSIPCO, page 331-335. IEEE, (2016)Consensus for the distributed estimation of point diffusion sources in sensor networks., and . ICASSP, page 3262-3266. IEEE, (2015)Beyond Binomial and Negative Binomial: Adaptation in Bernoulli Parameter Estimation., , , and . IEEE Trans. Computational Imaging, 5 (4): 570-584 (2019)Estimating Localized Sources of Diffusion Fields Using Spatiotemporal Sensor Measurements., and . IEEE Trans. Signal Process., 63 (12): 3018-3031 (2015)Beyond Binomial and Negative Binomial: Adaptation in Bernoulli Parameter Estimation., , , and . CoRR, (2018)Multi-Depth Computational Periscopy with an Ordinary Camera., , , and . ICASSP, page 9299-9305. IEEE, (2020)Solving Inverse Source Problems for Sources with Arbitrary Shapes using Sensor Networks., and . ESANN, (2017)Edge-Resolved Transient Imaging: Performance Analyses, Optimizations, and Simulations., , , , , , , , and . ICIP, page 2858-2862. IEEE, (2021)A Universal Sampling Framework for Solving Physics-driven Inverse Source Problems., and . CoRR, (2017)Physics-driven quantized consensus for distributed diffusion source estimation using sensor networks., and . EURASIP J. Adv. Signal Process., (2016)