quantization

Asymmetric compressive learning guarantees with applications to quantized sketches

Abstract: The compressive learning framework reduces the computational cost of training on large-scale datasets. In a sketching phase, the data is first compressed to a lightweight sketch vector, obtained by mapping the data samples through a well-chosen feature map, and averaging those contributions.

Asymmetric compressive learning guarantees with applications to quantized sketches

Abstract: The compressive learning framework reduces the computational cost of training on large-scale datasets. In a sketching phase, the data is first compressed to a lightweight sketch vector, obtained by mapping the data samples through a well-chosen feature map, and averaging those contributions.
2022 IEEE Transactions on Signal Processing
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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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Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model.
2019 Information and Inference
DOI DIAL arXiv

Taking the edge off quantization: projected back projection in dithered compressive sensing

Joint work with C. Xu and V. Schellekens.

Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Time for dithering! Quantized random embeddings with RIP random matrices

Time for dithering! Quantized random embeddings with RIP random matrices

Invited by Simon Foucart.

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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An extreme bit-rate reduction scheme for 2D radar localization

Abstract: In this paper, we further expand on the work in [1] that focused on the localization of targets in a 2D space using 1-bit dithered measurements coming from a 2 receiving antennae radar.
2018 Proceedings of “International Traveling Workshop on Interactions Between Sparse Models and Technology”
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