C
C. Ghanshyam
Researcher at Council of Scientific and Industrial Research
Publications - 47
Citations - 932
C. Ghanshyam is an academic researcher from Council of Scientific and Industrial Research. The author has contributed to research in topics: Thin film & Electrode. The author has an hindex of 14, co-authored 47 publications receiving 781 citations. Previous affiliations of C. Ghanshyam include Academy of Scientific and Innovative Research & Central Scientific Instruments Organisation.
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Detection mechanism of Metal Oxide Gas Sensor under UV Radiation.
TL;DR: In this article, the effect of ultraviolet radiation on the sensing mechanism of polycrystalline metal oxide gas sensor has been studied analytically, and it has been shown theoretically that due to incident UV radiation, it is possible to detect the gas even at room temperature.
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Index-based groundwater vulnerability mapping models using hydrogeological settings: A critical evaluation
Prashant Kumar,Prashant Kumar,Baban K. S. Bansod,Baban K. S. Bansod,Sanjit K. Debnath,Sanjit K. Debnath,Praveen K. Thakur,C. Ghanshyam,C. Ghanshyam +8 more
TL;DR: A review of the various groundwater vulnerability assessment models developed across the world can be found in this article, where each model has been evaluated in terms of its pros and cons and the environmental conditions of its application.
Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks
TL;DR: A Genetic Algorithm based multi-objective methodology was implemented for a self-organizing wireless sensor network that optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths.
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Alcohol sensing of tin oxide thin film prepared by sol-gel process
TL;DR: The results obtained favour the sol-gel process as a low cost method for the preparation of thin films with a high sensing characteristic as discussed by the authors, and the response time measurement of the sensor was also observed and it was found that======The response time is 30 sec.
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Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)
TL;DR: The work not only demonstrates the efficacy of SITO for feature selection owing to its simplicity in terms of few control parameters, but also the capability of an EN to differentiate Kangra orthodox black tea samples at different production stages.