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RISS 인기검색어
- 검색키워드
- 저자 : Sanjoy Mitra (검색결과 2 건)
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Majumdar Parijata,Mitra Sanjoy,Bhattacharya Diptendu 한국농업기계학회 2021 바이오시스템공학 Vol.46 No.4
Purpose The main focus is to concisely cover contemporary methods of IoT-oriented agriculture automation using weather monitoring. In this article, IoT components, communication protocols, data analysis and prediction methods are studied to reveal scientific and commercial perspectives like security vulnerabilities of communication protocols, cost of IoT hardware, data repository comparison, and dependency analysis of weather parameters. Reasons for striving towards Agriculture 4.0 are discussed to outline challenges for researchers to adjust irrigation schedules as per non-linear weather changes for developing an economically viable, real-time monitoring method. Methods Weather monitoring using IoT technologies for data processing and storage are extensively surveyed. Besides, we also reviewed how these approaches are enhanced using Agriculture 4.0 for different crops. Results Extensive survey reveals shortcomings of existing approaches and shows that temperature and humidity are most commonly monitored in IoT technologies. Cost analysis shows minimum cost incurred for hardware is 1–10$ for machine learning and edge computing and 10–20$ for cloud computing. Conclusions Cost analysis helps to identify cheaper sensors and micro-controllers for prediction of weather changes. Weather parameters that were given least importance in existing literature needs consideration to understand how its non-linearity impacts the accuracy of irrigation schedule prediction to maximize soil fertility and crop yield.
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Development of an Image Enhancement Method and a GUI
Rima Deb,Dipali Sengupta,Rupasree Das,Subhankar Mahajan,Sanjoy Mitra 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.6
This article introduces a new adaptive local image enhancement method and a graphical user. The algorithm for image enhancement will correct contrast for some certain areas of an image which is based on edges contained in the image and some statistical properties; variance of the whole image and mean value of an image block. A contrast correction gain is chosen for each image tile based on statistical properties. The algorithm improves the visual quality of an image and also restricts noise amplification. The algorithm is tested with grayscale images and results are compared with histogram equalization and adaptive histogram equalization techniques by image quality metrics parameters.
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