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eess.SP2026
Reconstruction of Piecewise-Constant Sparse Signals for Modulo Sampling
Haruka Kobayashi, Ryo Hayakawa
Modulo sampling is a promising technology to preserve amplitude information that exceeds the observable range of analog-to-digital converters during the digitization of analog sign…
eess.SP2025
Loss Function Design for Deep Unfolded Sparse Signal Recovery: Supervised and Unsupervised Learning
Koshi Nagahisa, Ryo Hayakawa, Youji Iiguni
This paper investigates the impact of loss function design in deep unfolding techniques for sparse signal recovery algorithms. We focus on deep unfolded versions of the fundamental…