Zobrazeno 1 - 10
of 6 339
pro vyhledávání: '"Haeri So"'
Non-contact manipulation is an emerging and highly promising methodology in robotics, offering a wide range of scientific and industrial applications. Among the proposed approaches, airflow stands out for its ability to project across considerable di
Externí odkaz:
http://arxiv.org/abs/2412.03254
To protect large-scale computing environments necessary to meet increasing computing demand, cloud providers have implemented security measures to monitor Operations and Maintenance (O&M) activities and therefore prevent data loss and service interru
Externí odkaz:
http://arxiv.org/abs/2412.01655
Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis
Despite recent progress in reducing road fatalities, the persistently high rate of traffic-related deaths highlights the necessity for improved safety interventions. Leveraging large-scale graph-based nationwide road network data across 49 states in
Externí odkaz:
http://arxiv.org/abs/2411.02542
Autor:
Mehrabi, Niloufar, Boroujeni, Sayed Pedram Haeri, Hofseth, Jenna, Razi, Abolfazl, Cheng, Long, Kaur, Manveen, Martin, James, Amin, Rahul
Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection, thanks to their ability to access remote areas and transmit real-tim
Externí odkaz:
http://arxiv.org/abs/2410.10843
Autor:
Maleki, Morteza, Haeri, Foad
Cardiovascular diseases are the leading cause of mortality globally, necessitating advancements in diagnostic techniques. This study explores the application of wavelet transformation for classifying electrocardiogram (ECG) signals to identify variou
Externí odkaz:
http://arxiv.org/abs/2404.09393
Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their indi
Externí odkaz:
http://arxiv.org/abs/2404.08013
Autor:
Gu, Hongyan, Yang, Chunxu, Magaki, Shino, Zarrin-Khameh, Neda, Lakis, Nelli S., Cobos, Inma, Khanlou, Negar, Zhang, Xinhai R., Assi, Jasmeet, Byers, Joshua T., Hamza, Ameer, Han, Karam, Meyer, Anders, Mirbaha, Hilda, Mohila, Carrie A., Stevens, Todd M., Stone, Sara L., Yan, Wenzhong, Haeri, Mohammad, Chen, Xiang 'Anthony'
As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance migh
Externí odkaz:
http://arxiv.org/abs/2404.04485
Autor:
Gu, Hongyan, Yan, Zihan, Alvi, Ayesha, Day, Brandon, Yang, Chunxu, Wu, Zida, Magaki, Shino, Haeri, Mohammad, Chen, Xiang 'Anthony'
The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in A
Externí odkaz:
http://arxiv.org/abs/2404.01656
Many modern systems, such as financial, transportation, and telecommunications systems, are time-sensitive in the sense that they demand low-latency predictions for real-time decision-making. Such systems often have to contend with continuous unbound
Externí odkaz:
http://arxiv.org/abs/2403.09588
Autor:
Wang, Hao, Boroujeni, Sayed Pedram Haeri, Chen, Xiwen, Bastola, Ashish, Li, Huayu, Zhu, Wenhui, Razi, Abolfazl
Wildfires are a significant threat to ecosystems and human infrastructure, leading to widespread destruction and environmental degradation. Recent advancements in deep learning and generative models have enabled new methods for wildfire detection and
Externí odkaz:
http://arxiv.org/abs/2403.03463