Pub Date : 2023-02-17DOI: 10.1142/s0218126623502316
M. P. Kumar, Thiyagarajan Jayaraman, M. Senthilkumar, A. S. Begum
{"title":"Performance Investigation of Generalized Rain Pattern Absorption Attention Network for Single-Image Deraining","authors":"M. P. Kumar, Thiyagarajan Jayaraman, M. Senthilkumar, A. S. Begum","doi":"10.1142/s0218126623502316","DOIUrl":"https://doi.org/10.1142/s0218126623502316","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"15 1","pages":"2350231:1-2350231:21"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87883948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-09DOI: 10.1142/s0218126623502298
Ling Liu, N. J. Navimipour
{"title":"A New Design of a 3 × 3 Reversible Circuit Based on a Nanoscale Quantum-Dot Cellular Automata","authors":"Ling Liu, N. J. Navimipour","doi":"10.1142/s0218126623502298","DOIUrl":"https://doi.org/10.1142/s0218126623502298","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"40 1","pages":"2350229:1-2350229:16"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81500269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.1142/s0218126623502262
T. S. Bheemraj, Dandu Prajapathi, V. Karthikeyan, S. Kumaravel
{"title":"A Novel High Gain Non-Isolated Three-Port DC-DC Converter for DC Microgrid Applications","authors":"T. S. Bheemraj, Dandu Prajapathi, V. Karthikeyan, S. Kumaravel","doi":"10.1142/s0218126623502262","DOIUrl":"https://doi.org/10.1142/s0218126623502262","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"5 1","pages":"2350226:1-2350226:26"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83389286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of emerging information technology, the traditional management methods of marine fishes are slowly replaced by new methods due to high cost, time-consumption and inaccurate management. The update of marine fishes management technology is also a great help for the creation of smart cities. However, some new methods have been studied that are too specific, which are not applicable for the other marine fishes, and the accuracy of identification is generally low. Therefore, this paper proposes an ecological Internet of Things (IoT) framework, in which a lightweight Deep Neural Networks model is implemented as a image recognition model for marine fishes, which is recorded as Fish-CNN. In this study, multi-training and evaluation of Fish-CNN is accomplished, and the accuracy of the final classification can be fixed to 89.89%–99.83%. Moreover, the final evaluation compared with Rem-CNN, Linear Regression and Multilayer Perceptron also verify the stability and advantage of our method.
{"title":"Lightweight CNN-Based Image Recognition with Ecological IoT Framework for Management of Marine Fishes","authors":"Lulu Jia, XiKun Xie, Junchao Yang, Fukun Li, Yueming Zhou, Xingrong Fan, Yu Shen, Zhiwei Guo","doi":"10.1142/s0218126623501694","DOIUrl":"https://doi.org/10.1142/s0218126623501694","url":null,"abstract":"With the development of emerging information technology, the traditional management methods of marine fishes are slowly replaced by new methods due to high cost, time-consumption and inaccurate management. The update of marine fishes management technology is also a great help for the creation of smart cities. However, some new methods have been studied that are too specific, which are not applicable for the other marine fishes, and the accuracy of identification is generally low. Therefore, this paper proposes an ecological Internet of Things (IoT) framework, in which a lightweight Deep Neural Networks model is implemented as a image recognition model for marine fishes, which is recorded as Fish-CNN. In this study, multi-training and evaluation of Fish-CNN is accomplished, and the accuracy of the final classification can be fixed to 89.89%–99.83%. Moreover, the final evaluation compared with Rem-CNN, Linear Regression and Multilayer Perceptron also verify the stability and advantage of our method.","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"30 1","pages":"2350169:1-2350169:22"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90206094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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