Pub Date : 2024-03-15DOI: 10.53375/ijecer.2024.380
Raban Nghidinwa, Joshua A. Abolarinwa
The Internet of Voting system presented in this paper represents a modern solution to the challenges faced by the traditional electoral processes. In an era characterized by technological advancements, this system harnesses digital innovation to enhance the efficiency, security, and accessibility of elections. Evolving from a rich history of voting systems, this digital platform integrates several influential works and leverages cutting-edge technology to create a user-friendly interface with robust security measures. The system addresses critical issues that have plagued traditional paper and electronic ballots, such as voter confusion and fraud, by implementing stringent encryption techniques, password renewal mechanisms, and real-time prevention of duplicated votes. It enables the elimination of multiple registrations, streamlining the voter registration process. Online voting systems have gained prominence as a means of conducting elections efficiently, reducing logistical challenges, and increasing voter accessibility. The system offers three distinct categories for voting: Regional Elections, Local Elections, and Presidential Elections, each with its own set of independent candidates.
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Pub Date : 2024-03-15DOI: 10.53375/ijecer.2024.383
Amira Chiheb, Hassina Khelladi
The aim of this study is to implement two different types of adaptive algorithms for the noise cancellation. The study explores the well-known least mean squares (LMS) adaptive algorithm, which is based on stochastic gradient descent approach, and its performances in terms of noise attenuation level and swiftness in active noise control (ANC). Another algorithm is considered in this investigation based upon the use of the least squares estimation (LSE), commonly named, the recursive least squares algorithm (RLS), and will be compared to the LMS. In order to evaluate the potential of each one, a few simulations are achieved. The numerical experiments are performed by using several real recordings of different environment noises tested on the two proposed adaptive algorithms. A comparison is emphasized regarding noise suppression ability and convergence speed, by implementing both adaptive algorithms on the same noise sources. From this numerical study, the RLS algorithm reveals a faster convergence speed and better control performances than the LMS algorithm.
{"title":"Performance Comparison of LMS and RLS Algorithms for Ambient Noise Attenuation","authors":"Amira Chiheb, Hassina Khelladi","doi":"10.53375/ijecer.2024.383","DOIUrl":"https://doi.org/10.53375/ijecer.2024.383","url":null,"abstract":"The aim of this study is to implement two different types of adaptive algorithms for the noise cancellation. The study explores the well-known least mean squares (LMS) adaptive algorithm, which is based on stochastic gradient descent approach, and its performances in terms of noise attenuation level and swiftness in active noise control (ANC). Another algorithm is considered in this investigation based upon the use of the least squares estimation (LSE), commonly named, the recursive least squares algorithm (RLS), and will be compared to the LMS. In order to evaluate the potential of each one, a few simulations are achieved. The numerical experiments are performed by using several real recordings of different environment noises tested on the two proposed adaptive algorithms. A comparison is emphasized regarding noise suppression ability and convergence speed, by implementing both adaptive algorithms on the same noise sources. From this numerical study, the RLS algorithm reveals a faster convergence speed and better control performances than the LMS algorithm.","PeriodicalId":507734,"journal":{"name":"International Journal of Electrical and Computer Engineering Research","volume":"42 172","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237017","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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