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A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls
This article presents the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.
Date:
August 10, 2021
Creator:
Harari, Yaar; Shawen, Nicholas; Mummidisetty, Chaithanya K.; Albert, Mark & Kording, Konrad P.
System:
The UNT Digital Library
Artificial Intelligence for Colonoscopy: Past, Present, and Future
Article summarizing the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials, (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities.
Date:
August 2021
Creator:
Tavanapong, Wallapak; Oh, JungHwan; Riegler, Michael; Khaleel, Mohammed I.; Mitta, Bhuvan & de Groen, Piet C.
System:
The UNT Digital Library
Multilevel Topological Interference Management: A TIM-TIN Perspective
Article combining TIN with the topological interference management (TIM) framework that identifies optimal interference avoidance schemes and formulates a TIM-TIN problem for multilevel topological interference management, wherein only a coarse knowledge of channel strengths and no knowledge of channel phases is available to transmitters.
Date:
August 5, 2021
Creator:
Geng, Chunhua; Sun, Hua & Jafar, Syed A.
System:
The UNT Digital Library
sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture
Accepted Manuscript version of an article introducing the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of Agriculture Cyber-Physical System (ACPS). An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.
Date:
August 15, 2021
Creator:
Udutalapally, Venkanna; Mohanty, Saraju P.; Pallagani, Vishal & Khandelwal, Vedant
System:
The UNT Digital Library