Zobrazeno 1 - 10
of 56 207
pro vyhledávání: '"Activity detection"'
Autor:
Roy, Avirup1 (AUTHOR) royaviru@msu.edu, Dutta, Hrishikesh1 (AUTHOR), Bhuyan, Amit Kumar1 (AUTHOR), Biswas, Subir1 (AUTHOR) sbiswas@msu.edu
Publikováno v:
Sensors (14248220). Jul2024, Vol. 24 Issue 14, p4444. 20p.
Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for
Externí odkaz:
http://arxiv.org/abs/2408.02359
This work presents the solution of the Signal Sleuths team for the 2024 HASCA WEAR challenge. The challenge focuses on detecting 18 workout activities (and the null class) using accelerometer data from 4 wearables - one worn on each limb. Data analys
Externí odkaz:
http://arxiv.org/abs/2408.03947
This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizing a combination of MFCC features and a bidirectional LSTM-based classi
Externí odkaz:
http://arxiv.org/abs/2407.01452
Autor:
Ginige, Yasod, Gunasekara, Ransika, Hewavitharana, Darsha, Ariyarathne, Manjula, Rodrigo, Ranga, Jayasekara, Peshala
Maritime surveillance is vital to mitigate illegal activities such as drug smuggling, illegal fishing, and human trafficking. Vision-based maritime surveillance is challenging mainly due to visibility issues at night, which results in failures in re-
Externí odkaz:
http://arxiv.org/abs/2406.08294
Modern wireless networks must reliably support a wide array of connectivity demands, encompassing various user needs across diverse scenarios. Machine-Type Communication (mMTC) is pivotal in these networks, particularly given the challenges posed by
Externí odkaz:
http://arxiv.org/abs/2406.07160
Autor:
Kumar, Satyam, Buddi, Sai Srujana, Sarawgi, Utkarsh Oggy, Garg, Vineet, Ranjan, Shivesh, Ognjen, Rudovic, Abdelaziz, Ahmed Hussen, Adya, Saurabh
Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need
Externí odkaz:
http://arxiv.org/abs/2406.09443
Publikováno v:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8963-8974, Torino, Italia. ELRA and ICCL
InaGVAD is an audio corpus collected from 10 French radio and 18 TV channels categorized into 4 groups: generalist radio, music radio, news TV, and generalist TV. It contains 277 1-minute-long annotated recordings aimed at representing the acoustic d
Externí odkaz:
http://arxiv.org/abs/2406.04429
Autor:
Bai, Jianan, Larsson, Erik G.
The wireless channel is undergoing continuous changes, and the block-fading assumption, despite its popularity in theoretical contexts, never holds true in practical scenarios. This discrepancy is particularly critical for user activity detection in
Externí odkaz:
http://arxiv.org/abs/2405.09425
We consider the identifiability issue of maximum likelihood based activity detection in massive MIMO based grant-free random access. A prior work by Chen et al. indicates that the identifiability undergoes a phase transition for commonly-used random
Externí odkaz:
http://arxiv.org/abs/2406.01138