arXiv:2508.00307v3 Announce Type: replace-cross
Abstract: We introduce a U-net model for 360{\deg} acoustic source localization formulated as a spherical semantic segmentation task. Rather than regressing discrete direction-of-arrival (DoA) angles, ou…
arXiv:2603.26475v1 Announce Type: cross
Abstract: Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent foundation models (FMs) have shown promise for learning …
arXiv:2505.16662v3 Announce Type: replace
Abstract: This paper presents a new approach for jointly calibrating magnetometers and inertial measurement units, focusing on improving calibration accuracy and computational efficiency. The proposed method f…
arXiv:2603.23831v1 Announce Type: new
Abstract: Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine learning tasks, including image recognitio…
arXiv:2603.23544v1 Announce Type: cross
Abstract: Peak-to-average power ratio (PAPR) remains a major limitation of multicarrier modulation schemes such as orthogonal frequency-division multiplexing (OFDM), reducing power amplifier efficiency and limit…
arXiv:2511.08851v4 Announce Type: replace-cross
Abstract: This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks. Using 10 Hz metro-train measurement traces with ser…