H Carborn
5 Papers
1 Citations
H Carborn is an academic researcher. The author has contributed to research in topics: Computer science & Compressed sensing. The author has an hindex of 1, co-authored 5 publications. Previous affiliations of H Carborn include Chongqing University.
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Papers
Multi-frequency synchronous two-dimensional off-grid compressive beamforming
H Carborn,Jens Lykkegaard Olesen +1 more
TL;DR: In this article , a multi-frequency synchronous two-dimensional off-grid compressive beamforming is proposed to solve the basis mismatch issue and suffer from performance degradation in the case of closely-spaced sources or low signal to noise ratios.
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Multi-frequency synchronous two-dimensional off-grid compressive beamforming
TL;DR: Simulations and experiments show that the proposed multi-frequency synchronous two-dimensional off-grid compressive beamforming has high spatial resolution and strong anti-interference while overcoming basis mismatch, and can locate and quantify the sources accurately even under small source spacings or low SNRs.
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Noise Annoyance Prediction of Urban Substation Based on Transfer Learning and Convolutional Neural Network
TL;DR: In this article , a noise annoyance prediction model based on transfer learning and a convolution neural network was proposed to accurately assess the degree of noise annoyance caused by substations to surrounding residents.
Two-dimensional grid-free compressive beamforming with spherical microphone arrays
TL;DR: In this article , a two-dimensional grid-free compressive beamforming method with spherical microphone arrays was proposed for 360° panoramic identification of acoustic sources, and the DOAs of sources were estimated through polynomial rooting and utilizing the dual optimal variables, and quantified via least-square fitting.
Two-dimensional grid-free compressive beamforming with spherical microphone arrays
TL;DR: In this article, a two-dimensional grid-free compressive beamforming method with spherical microphone arrays is proposed for 360° panoramic identification of acoustic sources, and the DOAs of sources are estimated through polynomial rooting and utilizing the dual optimal variables.