March 27, 2024, 4:42 a.m. | Sulaiman Aburakhia, Abdallah Shami, George K. Karagiannidis

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.17181v1 Announce Type: cross
Abstract: Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications …

abstract analysis applications arxiv case computing cs.lg eess.sp evolution fusion intersection machine machine learning measurement performance processing reliability sensing signal synergy technologies type

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