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607 results on '"Annapurna, K."'

1. Warm White Light Emission from Dy3+-Doped Mixed Alkaline-Earth Zinc Silico-Aluminate Glass Under Ultra-Violet Excitation

Author

Mohapatra, Sushanta Kumar, Khan, Sultan, Das, Subrata, Annapurna, K., Ghosh, Arindam, Series Editor, Chua, Daniel, Series Editor, de Souza, Flavio Leandro, Series Editor, Aktas, Oral Cenk, Series Editor, Han, Yafang, Series Editor, Gong, Jianghong, Series Editor, Jawaid, Mohammad, Series Editor, Molla, Atiar R., editor, Kalyandurg, Annapurna, editor, and Parker, J. M., editor

Published
2024
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9. Simulation of Topology Based VANET Routing Protocols Using NS3

Author

Pathan, Mahabub Subhani, Annapurna, K., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Bestak, Robert, editor, and Fernando, Xavier, editor

Published
2023
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10. Thermal, structural, and conductivity properties of As14Sb26S(60−x)–(AgI)x chalcogenide glasses.

Author

Prabhudessai, Akila G., Balaji, Sathravada, Prasad, Sakthi, Chahal, Shweta, Biswas, Kaushik, Ramesh, K., Yadav, Anupama, Chakraborty, Saswata, Kongar, Partha Sarathi, Chatterjee, Sayan, Dutta, Sutanu, Dasgupta, Rana, Sarkar, Pratik, and Annapurna, K.

Subjects
CHALCOGENIDE glass, SILVER iodide, GLASS construction, DIFFERENTIAL scanning calorimetry, GLASS transition temperature
Abstract

The present work describes the preparation of a new series of chalcogenide glasses in an As14Sb26S(60−x) (AgI)x system intending to explore its thermal, structural, optical, mechanical, and electrical properties. The differential scanning calorimetry results of the studied glasses show the sharp decrease in glass transition temperature (Tg) with the successive incremental inclusion of AgI in the composition, implying the structural changes in the glass network. A thorough Raman analysis corroborates the occurrence of changes in the glass network due to the formation of AsI3 units and Ag–S–As bonds with increasing AgI content. Also, structural changes can be reflected with the change in the optical bandgap (Eg) that was calculated using Tauc equations where it was found that Eg is in harmony with the observed structural variations of glasses. The studied glasses possess a transmittance window (∼0.68–12 μm) with transmittance above 60% in the mid-infrared region. These structural changes are closely related to the significant enhancement of conductivity of the present glasses from 10−8 to 10−6 S/cm at 373 K with a decrease in activation energies. Impedance spectra for the glass with highest AgI revealed the presence of two different relaxation processes. AC conductivity data followed an Arrhenius behavior as well as Jonscher's power law. The present work provides insights into glass network modifications due to silver iodide inclusion and its role in the enhancement of conductivity. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Quality Assisted Spectrum Allocation in Cognitive NOMA Networks

Author

Varma, D. Prasanth, Annapurna, K., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nayak, Padmalaya, editor, Pal, Souvik, editor, and Peng, Sheng-Lung, editor

Published
2022
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22. Timeout-Aware Inter-Queuing for QoS Provisioning of Real-Time Secondary Users in Cognitive Radio Networks

Author

Annapurna, K., Seetha Ramanjaneyulu, B., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Chowdary, P. Satish Rama, editor, Chakravarthy, V.V.S.S.S., editor, Anguera, Jaume, editor, Satapathy, Suresh Chandra, editor, and Bhateja, Vikrant, editor

Published
2021
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27. Thermal, structural, and conductivity properties of As14Sb26S(60−x)–(AgI)x chalcogenide glasses

Author

Prabhudessai, Akila G., primary, Balaji, Sathravada, additional, Prasad, Sakthi, additional, Chahal, Shweta, additional, Biswas, Kaushik, additional, Ramesh, K., additional, Yadav, Anupama, additional, Chakraborty, Saswata, additional, Kongar, Partha Sarathi, additional, Chatterjee, Sayan, additional, Dutta, Sutanu, additional, Dasgupta, Rana, additional, Sarkar, Pratik, additional, and Annapurna, K., additional

Published
2024
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46. DEEP REINFORCEMENT LEARNING BASED MULTIPREDICTOR ENSEMBLE DECISION FRAMEWORK FOR REGIONAL GDP PREDICTION.

Author

Sir, B. Sateesh Kumar and Annapurna, K.

Subjects
*REINFORCEMENT learning, *DEEP reinforcement learning, *GROSS domestic product
Abstract

Gross Domestic Product (GDP) is a good way to show how the economy is growing and how resources are being used in different areas. It's not enough to just look at a single factor when trying to figure out a region's GDP; you also need to look at factors like schooling, business, jobs, population, and more. It is important for both economics and government to be able to predict regional GDP because it tells us a lot about the economic health and growth of areas. We suggest using a Deep Reinforcement Learning-based multi-predictor ensemble decision system in this project to make area GDP forecasts more accurate and reliable. To train the GDP Prediction, we can use the Gated Recurrent Unit (GRU), the Temporal Convolutional Network (TCN), the Voting Classifier, the XGBooster, and the Deep Belief Network (DBN). These models can predict the data. In a multipredictior ensemble decision structure, a vote algorithm comes to a decision by putting together results from different predictors. The Deep Q-Network (DQN) method quickly checks how well these neural networks change to different GDP datasets so that an ensemble model can be made that gives correct results. [ABSTRACT FROM AUTHOR]

Published
2024

49. Preface

Author

Amaresan, N., primary, Senthil Kumar, M., additional, Annapurna, K., additional, Kumar, Krishna, additional, and Sankaranarayanan, A., additional

Published
2020
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