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Titlebook: Recent Trends in Signal and Image Processing; ISSIP 2020 Siddhartha Bhattacharyya,Leo Mr?i?,Mario Koeppen Conference proceedings 2021 The E

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發(fā)表于 2025-3-21 16:37:34 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Recent Trends in Signal and Image Processing
副標(biāo)題ISSIP 2020
編輯Siddhartha Bhattacharyya,Leo Mr?i?,Mario Koeppen
視頻videohttp://file.papertrans.cn/824/823501/823501.mp4
概述Presents research works in the field of signal and image processing.Results of ISSIP 2020 held at Kolkata, West Bengal, India during March 18-19, 2020.Serves as a reference for researchers and practit
叢書名稱Advances in Intelligent Systems and Computing
圖書封面Titlebook: Recent Trends in Signal and Image Processing; ISSIP 2020 Siddhartha Bhattacharyya,Leo Mr?i?,Mario Koeppen Conference proceedings 2021 The E
描述This book gathers selected papers presented at the Third International Symposium on Signal and Image Processing (ISSIP 2020), organized by the Department of Information Technology, RCC Institute of Information Technology, Kolkata, during March 18–19, 2020. It presents fascinating, state-of-the-art research findings in the field of signal and image processing. It includes conference papers covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. It addresses various types of signals, such as image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals; MRI, PET and CT scan images; THz signals; solar wind speed signals (SWS); and photoplethysmogram (PPG) signals, and demonstrates how new paradigms of intelligent computing, like quantum computing, can be applied to process and analyze signals precisely and effectively..
出版日期Conference proceedings 2021
關(guān)鍵詞ISSIP 2020 Conference Proceedings; Digital Signal Processing; Remote Sensing Imagery; Video compression
版次1
doihttps://doi.org/10.1007/978-981-33-6966-5
isbn_softcover978-981-33-6965-8
isbn_ebook978-981-33-6966-5Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 21:19:44 | 只看該作者
https://doi.org/10.1007/978-981-33-6966-5ISSIP 2020 Conference Proceedings; Digital Signal Processing; Remote Sensing Imagery; Video compression
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Recent Trends in Signal and Image Processing978-981-33-6966-5Series ISSN 2194-5357 Series E-ISSN 2194-5365
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發(fā)表于 2025-3-22 09:00:29 | 只看該作者
Siddhartha Bhattacharyya,Leo Mr?i?,Mario KoeppenPresents research works in the field of signal and image processing.Results of ISSIP 2020 held at Kolkata, West Bengal, India during March 18-19, 2020.Serves as a reference for researchers and practit
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發(fā)表于 2025-3-22 12:59:05 | 只看該作者
Implementing a Hardware Accelerator to Enhance the Recognition Performance of the Fruit Mature, that the color-threshold technique required 16% of the total logic elements and performed the recognition task in 10.25?ms. In contrast, the k-means clustering technique required 62% of the logic elements and performed the recognition task in 64.88?ms. Thus, color-threshold technique is more efficient and much faster than the k-means technique.
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Intelligent Hybrid Technique to Secure Bluetooth Communications,onnected using Bluetooth technique. Because of the advantages of key management of the MD5 algorithm, we used it to encrypt the secret key of Blowfish algorithm which used for encryption of plaintext. Therefore, the proposed hybrid encryption (Blowfish and MD5) will positively improve the data security during communication in Bluetooth media.
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發(fā)表于 2025-3-23 07:18:22 | 只看該作者
A Fog-Based Retrieval of Real-Time Data for Health Applications,t the edge of the network. In this paper, we use a fog computing network approach for efficiently retrieving the real-time patient data. The performance of our proposed approach has been compared with the cloud computing approach in terms of retrieval time of real-time data.
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