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pro vyhledávání: '"Shiv Nath Chaudhri"'
Autor:
Sumit Srivastava, Shiv Nath Chaudhri, Navin Singh Rajput, Saeed Hamood Alsamhi, Alexey V. Shvetsov
Publikováno v:
IEEE Access, Vol 11, Pp 17731-17738 (2023)
Recently, society/industry is getting smarter and sustainable through artificial intelligence-based solutions. However, this rapid advancement is also polluting our air ambience. Hence real-time detection and estimation of hazardous gases/odors in th
Externí odkaz:
https://doaj.org/article/6549d1099e234efa87bae0ec1a189e09
Autor:
Kanak Kumar, Shiv Nath Chaudhri, Navin Singh Rajput, Alexey V. Shvetsov, Radhya Sahal, Saeed Hamood Alsamhi
Publikováno v:
Sensors, Vol 23, Iss 10, p 4885 (2023)
Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial int
Externí odkaz:
https://doaj.org/article/008db993b11b4865abc58bae9b35b3ad
Autor:
Shiv Nath Chaudhri, Navin Singh Rajput, Saeed Hamood Alsamhi, Alexey V. Shvetsov, Faris A. Almalki
Publikováno v:
Sensors, Vol 22, Iss 8, p 3039 (2022)
Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system
Externí odkaz:
https://doaj.org/article/0386e0df552b473192c2dbb7d5ce25a1
Publikováno v:
Journal of Electrical Engineering. 74:102-108
Accurate detection of gas/odor requires highly selective gas sensor. However, the high-performance classification of gases/odors can be achieved using partial-selective gas sensors. Since 1980s, an array of broadly tuned (partial-selective) gas senso
Publikováno v:
Journal of Electrical Engineering. 73:108-115
High-performance detection and estimation of gases/odors are challenging, especially in real-time gas sensing applications. Recently, efficient electronic noses (e-noses) are being developed using convolutional neural networks (CNNs). Further, CNNs p