Hussain Albarakati, R. Ammar, Ayman Alharbi, H. Alhumyani
{"title":"An application of using embedded underwater computing architectures","authors":"Hussain Albarakati, R. Ammar, Ayman Alharbi, H. Alhumyani","doi":"10.1109/ISSPIT.2016.7886005","DOIUrl":null,"url":null,"abstract":"Underwater computing systems have recently emerged as a new technology for underwater applications. In our previous work, we employed various nodes (processing nodes, gateway nodes, and sensing nodes) that are used to construct different candidate computing architectures (i.e. single, pipeline, and hybrid of parallel/pipeline). In this paper, we use these computing architectures in a real world scenario of underwater applications. The selected application is the detection and classification of fish species in real-time. The selected application scenario proves the applicability and performance relevance of the developed computing architectures for underwater environment. Our results show that the proposed computing architectures provide high speed-up gain for the selected application.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2016.7886005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Underwater computing systems have recently emerged as a new technology for underwater applications. In our previous work, we employed various nodes (processing nodes, gateway nodes, and sensing nodes) that are used to construct different candidate computing architectures (i.e. single, pipeline, and hybrid of parallel/pipeline). In this paper, we use these computing architectures in a real world scenario of underwater applications. The selected application is the detection and classification of fish species in real-time. The selected application scenario proves the applicability and performance relevance of the developed computing architectures for underwater environment. Our results show that the proposed computing architectures provide high speed-up gain for the selected application.