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Iterative Low-rank and rotating sparsity promotion for circumstellar disks imaging

Abstract: Recent astronomical observations open the possibility to directly image circumstellar disks, a key feature for our understanding of extra-solar systems. However, the faint intensity of these celestial signals compared to the brightness of their hosting star make their accurate characterization a challenging processing task.
2019 Proceedings of SPARS'19
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Near Sensor Decision Making via Compressed Measurements for Highly Constrained Hardware

Abstract: This work presents and compare three realistic scenarios to perform near sensor decision making based on Dimensionality Reduction (DR) techniques of high dimensional signals in the context of highly constrained hardware.
2019 2019 53rd Asilomar Conference on Signals, Systems, and Computers
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One-Bit Sensing of Low-Rank and Bisparse Matrices

Abstract: This note studies the worst-case recovery error of low-rank and bisparse matrices as a function of the number of one-bit measurements used to acquire them. First, by way of the concept of consistency width, precise estimates are given on how fast the recovery error can in theory decay.
2019 Proceeding of Sampta 2019
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Performance of Compressive Sensing for Hadamard-Haar Systems

Abstract: We study the problem of image recovery from subsampled Hadamard measurements using Haar wavelet sparsity prior. This problem is of interest in, e.g., computational imaging applications relying on optical multiplexing or single pixel imaging.
2019 Proceedings of SPARS'19
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Quantity over Quality: Dithered Quantization for Compressive Radar Systems

Abstract: In this paper, we investigate a trade-off between the number of radar observations (or measurements) and their resolution in the context of radar range estimation. To this end, we introduce a novel estimation scheme that can deal with strongly quantized received signals, going as low as 1-bit per signal sample.
2019 IEEE Radarconf
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Single Pixel Hyperspectral Imaging using Fourier Transform Interferometry

Abstract: Single-Pixel (SP) imaging is now a reality in many applications, e.g., biomedical ultrathin endoscope and fluorescent spectroscopy. In this context, many schemes exist to improve the light throughput of these device, e.
2019 BASP
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Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars

Abstract: We investigate the problem of sparse target detection from widely distributed multistatic textitFrequency Modulated Continuous Wave (FMCW) radar systems (using chirp modulation). Unlike previous strategies e.g., developed for FMCW or distributed multistatic radars), we propose a generic framework that scales well in terms of computational complexity for high-resolution space-velocity grid.
2019 in Proceedings of IEEE CAMSAP 2019, Le Gosier, France, December, 15-18, 2019
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Structured Illumination and Variable Density Sampling for Compressive Fourier Transform Interferometry

Abstract: Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality for many applications demanding high spectral resolution, e.g., in fluorescence microscopy. However, the effective resolution of FTI is limited by the durability (or photobleaching) of biological elements when exposed to illuminating light.
2019 BASP
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1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

Abstract: We present a novel scheme allowing for 2D target localization using highly quantized 1-bit measurements from a Frequency Modulated Continuous Wave (FMCW) radar with two receiving antennas. Quantization of radar signals introduces localization artifacts, we remove this limitation by inserting a dithering on the unquantized observations.
2018 Proceedings of the 5th International Workshop on Compressed Sensing applied to Radar, Multimodal Sensing, and Imaging (CoSeRa)
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A Low-Memory Compressive Image Sensor Architecture for Embedded Object Recognition

Abstract: This work presents a compact image sensor architecture with end-of-column digital processing dedicated to perform embedded object recognition. The architecture takes advantage of a Compressed Sensing (CS) scheme to extract compressed features and to reduce data dimensionality based on a low footprint pseudo random data mixing.
2018 Proceedings of MWSCAS 2018
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