Tootfinder

Embedded Acoustic Intelligence for Automotive Systems
Renjith Rajagopal, Peter Winzell, Sladjana Strbac, Konstantin Lindstr\"om, Petter H\"orling, Faisal Kohestani, Niloofar Mehrzad
https://arxiv.org/abs/2506.11071

Embedded Acoustic Intelligence for Automotive Systems
Transforming sound insights into actionable streams of data, this abstract leverages findings from degree thesis research to enhance automotive system intelligence, enabling us to address road type [1].By extracting and interpreting acoustic signatures from microphones installed within the wheelbase of a car, we focus on classifying road type.Utilizing deep neural networks and feature extraction powered by pre-trained models from the Open AI ecosystem (via Hugging Face [2]), our approach enable…

fftvis: A Non-Uniform Fast Fourier Transform Based Interferometric Visibility Simulator
Tyler A. Cox, Steven G. Murray, Aaron R. Parsons, Joshua S. Dillon, Kartik Mandar, Zachary E. Martinot, Robert Pascua, Piyanat Kittiwisit, James E. Aguirre
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New level density parameter beyond Egidy-Bucurescu's systematics
Junzhe Zhang, Yanan Zheng, Caixin Yuan, Yangyang Shen, Yingchen Mao
https://arxiv.org/abs/2506.02322

New level density parameter beyond Egidy-Bucurescu's systematics
Extending beyond the Egidy-Bucurescu systematics, the nuclear level density parameters (LDPs) for the back-shifted Fermi gas model were compiled. Three forms of LDPs were fitted: the liquid-drop model (LDM), the droplet model (DM), and the power-law dependence on mass number A. Additionally, the root-mean-square deviations (RMSDs) of the new LDPs and existing literature values were calculated. The newly fitted global LDM-type parameters outperform the commonly used Toke-Swiatecki parameters in …