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Titlebook: Computational Advancement in Communication, Circuits and Systems; Proceedings of 3rd I M. Mitra,Mita Nasipuri,Maitreyi Ray Kanjilal Confere

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發(fā)表于 2025-3-21 16:19:06 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computational Advancement in Communication, Circuits and Systems
副標(biāo)題Proceedings of 3rd I
編輯M. Mitra,Mita Nasipuri,Maitreyi Ray Kanjilal
視頻videohttp://file.papertrans.cn/233/232076/232076.mp4
概述Presents research works in the field of communication circuits and systems.Provides original works presented at ICCACCS 2020 held in Kolkata, India.Serves as a reference for researchers and practition
叢書(shū)名稱Lecture Notes in Electrical Engineering
圖書(shū)封面Titlebook: Computational Advancement in Communication, Circuits and Systems; Proceedings of 3rd I M. Mitra,Mita Nasipuri,Maitreyi Ray Kanjilal Confere
描述This book gathers the proceedings of the Third International Conference on Computational Advancement in Communication Circuits and Systems (ICCACCS 2020), organized virtually by Narula Institute of Technology, Kolkata, India. The book presents peer-reviewed papers that highlight new theoretical and experimental findings in the fields of electronics and communication engineering, including interdisciplinary areas like advanced computing, pattern recognition and analysis, and signal and image processing. The respective papers cover a broad range of principles, techniques, and applications in microwave devices, communication and networking, signal and image processing, computations and mathematics, and control.
出版日期Conference proceedings 2022
關(guān)鍵詞Modelling of Energy Systems; Cloud Computing; Internet of Things; Machine Learning and Big Data Analyti
版次1
doihttps://doi.org/10.1007/978-981-16-4035-3
isbn_softcover978-981-16-4037-7
isbn_ebook978-981-16-4035-3Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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,New Sorting Algorithm—RevWay Sort, to 50,000 elements. We found that the newly proposed RevWay sort yields lesser running time compared to bubble and selection sort. For 10,000 elements, RevWay sort takes 203.636?ms, whereas bubble sort takes 364.8243ms and selection sort consumes 337.5543?ms.
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Springer Tracts in Civil Engineeringis deep learning model is designed to consist of 25-layer for plant disease classification. The trained model achieved 96.64% accuracy to detect the plant disease. The proposed deep learning convolution neural network model may have great potential in disease detection for current cultivation on large scale.
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Structural Analysis with Finite Elementsing two- and three-vertices with given edges and color them accordingly. We also propose a quantum algorithm for coloring of trees. For each of the cases, we discuss the quantum costs and complexities and compare it with the classical approach.
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Structural Analysis with Finite Elements to 50,000 elements. We found that the newly proposed RevWay sort yields lesser running time compared to bubble and selection sort. For 10,000 elements, RevWay sort takes 203.636?ms, whereas bubble sort takes 364.8243ms and selection sort consumes 337.5543?ms.
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