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269 results on '"Sharma, Tarun Kumar"'

33. Ultranarrow Band UV Detection in GaN with Simple Device Architecture.

Author

Chatterjee, Abhishek, Khamari, Shailesh K., Kumar, Ravi, Porwal, Sanjay, Bose, Aniruddha, Raghavendra, S., Dixit, Vijay Kumar, and Sharma, Tarun Kumar

Subjects
GALLIUM nitride, VALENCE bands, THERMAL equilibrium, SURFACE photovoltage, HIGH temperatures
Abstract

An ultranarrow‐band ultraviolet (UV) GaN detector with a simple device architecture is presented. The device displays an ultranarrow band response centred at 366 nm with only ≈5 nm width, a UV–visible rejection ratio of >2 × 103, and detectivity of 1.3 × 1010 Jones at room temperature. The device is tested over a wide temperature range where the maximum response is recorded at 200 K. Temperature dependence of device response is explained by considering the thermal activation of shallow donors in low‐temperature regime and the establishment of thermal equilibrium of the donor level with conduction band at elevated temperatures. Moreover, the device displays three orders UV‐to‐visible rejection ratio over a wide temperature range of 150–350 K, showing great potential for narrowband UV detection even at elevated temperatures. Conventional and pump‐probe surface photovoltage spectroscopy measurements are performed with UV and infrared lasers to identify the electronic transitions associated with the narrowband response. It is understood that carrier excitation from valance band to shallow donor level in the interfacial depletion region is responsible for the observed behavior. The results presented here demonstrate an innovative methodology for achieving a stable and high spectrum‐selective UV photodetection without involving complex device architecture. [ABSTRACT FROM AUTHOR]

Published
2024
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48. 6G‐enabled internet of medical things.

Author

Dhanda, Sumit Singh, Singh, Brahmjit, Jindal, Poonam, Sharma, Tarun Kumar, and Panwar, Deepak

Subjects
INTERNET of things, PUBLIC health infrastructure, ARTIFICIAL neural networks, COVID-19 pandemic, MEDICAL personnel
Abstract

Healthcare is the foremost concern for any country. UN has also fixed 'good health' and well‐being' as its third sustainable development goal. CoVID‐19 era has shown the vulnerability of health infrastructure worldwide. As per WHO, aging population of the world would put enormous pressure on health infrastructure in coming decades. With lack of healthcare professionals and limited budget to achieve UNSDG goal‐3, IoMT with its huge umbrella of medical services and applications can address these issues. 5G has been deployed in many countries and vision for 6G have been finalized by different research groups. 6G will help Internet of Medical Things to realize its full potential. In this work, an open '6G enabled IoMT' architecture has been presented. It can integrate a wide number of services. The limitations of 5G in catering to such a network are also highlighted. The challenges and open issues are presented in the light of services and applications that can be provided by the purposed architecture. A brief overview of 6G is also provided. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Soft computing approaches for prediction of specific heat capacity of hybrid nanofluids.

Author

Mathur, Priya, Gupta, Amit Kumar, Panwar, Deepak, and Sharma, Tarun Kumar

Subjects
SPECIFIC heat capacity, SOFT computing, NANOFLUIDS, NANOFLUIDICS, MACHINE learning, HEAT transfer coefficient, SPECIFIC heat, THERMAL diffusivity
Abstract

Nanofluids are thermoelectric substance with a greater thermal transmission effectiveness than traditional thermal fluids. Nanofluid's specific heat capacity (SHC) is a crucial thermophysical parameter since it controls the fluid's heat transfer coefficient. Surprisingly few research investigations have been done on the prognostic modelling of the specific heat capacity of fusion nanofluids, despite the fact that numerous experiments have been conducted on the heat transfer capacitance of blended nanofluids. This study focuses on the use of algorithms based on machine learning procedures (MLP) to estimate the SHC of blended nanofluids. Numerous numbers of hybrid nanofluids are investigated in this investigation. Total of 984 samples of hybrid nanofluids for specific heat capacity has been collected from nine experimental research papers. Various MLP techniques were utilized in this analysis, including extreme boost gradient boost regression (XGB), support vector regression augmented with a genetic algorithm (support vector regression [SVR]‐genetic algorithm [GA]), grid search optimized based gradient boost regression algorithm (GBR) (GBR‐GSO), and voting ensemble procedure (VE). The obtained correlation coefficients of the SVR‐GA, XGB, VE and GBR‐GSO models for the testing dataset are 98.43%, 96.29%, 94.4% and 96.55% respectively. The SVR‐GA model showed a better predictive accuracy relative to other ML models. This SVR‐GA anticipated model could be used for quick and reliable prediction of the SHC of blended nanofluids which reduces the burden associated with experimental measurement possible future work includes applying a machine‐learning strategy to the problem of determining the diffusivity of hybrid nanofluids. [ABSTRACT FROM AUTHOR]

Published
2024
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