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pro vyhledávání: '"Sakthi, Madhumitha"'
Autonomous driving systems require extensive data collection schemes to cover the diverse scenarios needed for building a robust and safe system. The data volumes are in the order of Exabytes and have to be stored for a long period of time (i.e., mor
Externí odkaz:
http://arxiv.org/abs/2403.16338
Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies. Acquiring situational awareness using multimodal data collected at high sampling rates by various sensing devices including cameras, LiD
Externí odkaz:
http://arxiv.org/abs/2302.10450
Deep learning model compression is an improving and important field for the edge deployment of deep learning models. Given the increasing size of the models and their corresponding power consumption, it is vital to decrease the model size and compute
Externí odkaz:
http://arxiv.org/abs/2210.05111
Robust and accurate sensing is of critical importance for advancing autonomous automotive systems. The need to acquire situational awareness in complex urban conditions using sensors such as radar has motivated research on power and latency-efficient
Externí odkaz:
http://arxiv.org/abs/2203.03905
Autor:
Sakthi, Madhumitha, Tewfik, Ahmed
The growing urban complexity demands an efficient algorithm to acquire and process various sensor information from autonomous vehicles. In this paper, we introduce an algorithm to utilize object detection results from the image to adaptively sample a
Externí odkaz:
http://arxiv.org/abs/2010.02367
Autor:
Sakthi, Madhumitha
The ubiquitous use of deep learning models for signal processing has led to an increasing computational and storage cost, especially in edge applications. Although deep learning has delivered improved accuracy for a given task, they have also increas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12fd46a95a4869fad996fd0cc875dc76
Autor:
Sakthi, Madhumitha, Tewfik, Ahmed
Publikováno v:
2021 29th European Signal Processing Conference (EUSIPCO).
The growing urban complexity demands an efficient algorithm to acquire and process various sensor information from autonomous vehicles. In this paper, we introduce an algorithm to utilize object detection results from the image to adaptively sample a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ced3e735d2f9b709a07693b1c3a82625
https://doi.org/10.23919/eusipco54536.2021.9616025
https://doi.org/10.23919/eusipco54536.2021.9616025
