Zobrazeno 1 - 10
of 510
pro vyhledávání: '"Srivastava, Mani"'
Autor:
Siam, Shakhrul Iman, Ahn, Hyunho, Liu, Li, Alam, Samiul, Shen, Hui, Cao, Zhichao, Shroff, Ness, Krishnamachari, Bhaskar, Srivastava, Mani, Zhang, Mi
Publikováno v:
ACM Trans. Sen. Netw.(August 2024)
The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT r
Externí odkaz:
http://arxiv.org/abs/2410.19998
Autor:
Quan, Pengrui, Ouyang, Xiaomin, Jeyakumar, Jeya Vikranth, Wang, Ziqi, Xing, Yang, Srivastava, Mani
Effective processing, interpretation, and management of sensor data have emerged as a critical component of cyber-physical systems. Traditionally, processing sensor data requires profound theoretical knowledge and proficiency in signal-processing too
Externí odkaz:
http://arxiv.org/abs/2410.10741
Mental health disorders are among the most prevalent diseases worldwide, affecting nearly one in four people. Despite their widespread impact, the intervention rate remains below 25%, largely due to the significant cooperation required from patients
Externí odkaz:
http://arxiv.org/abs/2409.10064
Autor:
Cominelli, Marco, Gringoli, Francesco, Kaplan, Lance M., Srivastava, Mani B., Bihl, Trevor, Blasch, Erik P., Iyer, Nandini, Cerutti, Federico
Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to as
Externí odkaz:
http://arxiv.org/abs/2407.04734
Autor:
Cominelli, Marco, Gringoli, Francesco, Kaplan, Lance M., Srivastava, Mani B., Cerutti, Federico
Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for esti
Externí odkaz:
http://arxiv.org/abs/2407.04733
Autor:
Wu, Jason, Wang, Ziqi, Ouyang, Xiaomin, Jeong, Ho Lyun, Samplawski, Colin, Kaplan, Lance, Marlin, Benjamin, Srivastava, Mani
Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different perspectives to estimate the tar
Externí odkaz:
http://arxiv.org/abs/2406.06796
Autor:
Xu, Xiang, Zhao, Tianchen, Zhang, Zheng, Li, Zhihua, Wu, Jon, Achille, Alessandro, Srivastava, Mani
Protecting digital identities of human face from various attack vectors is paramount, and face anti-spoofing plays a crucial role in this endeavor. Current approaches primarily focus on detecting spoofing attempts within individual frames to detect p
Externí odkaz:
http://arxiv.org/abs/2406.03684
Autor:
Kimura, Tomoyoshi, Li, Jinyang, Wang, Tianshi, Kara, Denizhan, Chen, Yizhuo, Hu, Yigong, Wang, Ruijie, Wigness, Maggie, Liu, Shengzhong, Srivastava, Mani, Diggavi, Suhas, Abdelzaher, Tarek
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications. A case study is presented featuring a vehicle
Externí odkaz:
http://arxiv.org/abs/2404.02461
Autor:
Ouyang, Xiaomin, Srivastava, Mani
Most studies on machine learning in sensing systems focus on low-level perception tasks that process raw sensory data within a short time window. However, many practical applications, such as human routine modeling and occupancy tracking, require hig
Externí odkaz:
http://arxiv.org/abs/2403.19857
Autor:
Jeong, Ho Lyun, Wang, Ziqi, Samplawski, Colin, Wu, Jason, Fang, Shiwei, Kaplan, Lance M., Ganesan, Deepak, Marlin, Benjamin, Srivastava, Mani
Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-al
Externí odkaz:
http://arxiv.org/abs/2402.14136