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RFID-Based Elevator Access control System 15

RFID-Based Elevator Access Control System

引用

15th international conference on computing communication and Networking Technologies, ICCCNT 2024

作者： Kishan, Surapaneni Ravi Hameed, Syed Charan, Jampani Srikar, Yaragudipati VRSEC Dept. of CSE Vijayawada India

ISBN: (纸本)9798350370249

RFID (Radio Frequency Identification) is a wireless, contactless data transfer technology that uniquely identifies items using radio waves. In certain organizations, lifts are designated for specific users to avoid wastage of precious time due to unnecessary usage. To address this challenge, we introduce a management system that prevents unauthorized access to elevators. Within the lift, a barrier controlled by NFC technology is installed, storing user data such as usernames, user IDs and their destinations. Authorized users with tags are permitted access, while unauthorized individuals are denied, ensuring controlled accessibility. Special guest tags are provided for guest usage. The ESP32 and Google AppScript facilitate data transfer to Google Sheets, serving as the database interface. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Proof of Concept Implementation for RSVP TSN control Plane 9

Proof of Concept Implementation for RSVP TSN Control Plane

引用

9th international conference on Computer Science and Engineering, UBMK 2024

作者： Gozuacik, Necip Siemens Research and Development Istanbul Turkey

ISBN: (纸本)9798350365887

TSN is a collection of IEEE 802.1 standards that enable deterministic communication over Ethernet networks. It aims ensuring low latency, high reliability, and precise synchronization, crucial for applications like industrial automation and audio-video systems. RSVP is used in IP networks to reserve resources and maintain QoS by ensuring specific bandwidth, latency, and jitter requirements for data flows. The integration of RSVP into TSN networks offers the potential for a comprehensive control plane that can meet the requirements of deterministic applications. This study explores integrating DetNet with TSN using RSVP to create a robust control plane that supports deterministic services. The proposed implementation in this study includes design and development of RSVP stack with extended features to support TSN in PoC level. Furthermore, communication with TSN for resource allocation and routing capabilities are considered. A demonstration of the implementation in a physical test environment is presented. The results reveal the successful integration of RSVP into TSN networks for providing deterministic services from the point of control plane. © 2024 IEEE.

关键词： Resource allocation

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VLocSense: Integrated VLC System for Indoor Passive Localization and Human Sensing 30

VLocSense: Integrated VLC System for Indoor Passive Localiza...

引用

30th international conference on Mobile computing and Networking, ACM MobiCom 2024

作者： Li, Jiarong Ge, Changshuo Xu, Chihan Gong, Junhao Gui, Weihua Zhang, Chaobo Liang, Xiaojun Ding, Wenbo Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen China Pengcheng Laboratory Shenzhen China Shenzhen International Graduate School Tsinghua University Shenzhen China School of Automation Central South University Changsha China Pengcheng Laboratory RISC-V International Open Source Laboratory Shenzhen China

ISBN: (纸本)9798400704895

The demand for accurate and real-time indoor localization and human sensing is rising with the development of smart environments, with applications in security and smart homes. Effective systems enhance safety, energy efficiency, and user experience by leveraging existing infrastructure, reducing deployment costs, and integrating seamlessly. Traditional methods rely on dedicated hardware, while communication or lighting infrastructure can provide dual-purpose solutions. This research focuses on Visible Light communication (VLC) technology, which uses visible light for data transmission. Our VLC system utilizes existing lighting infrastructure to transmit data, providing localization and human sensing functionalities. The system design strategically incorporates specific VLC transmitters and receivers to enhance sensing performance. The collected data is processed using advanced algorithms and machine learning models, ensuring robust, real-time, and cost-effective localization with an accuracy of 96.6% and human activity recognition with an accuracy of 98.3%. This multi-functionality system demonstrates the potential for VLC in healthcare monitoring and home automation applications. © 2024 Copyright held by the owner/author(s).

关键词： Smart homes

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A Deep Reinforcement Learning Approach for Autonomous Reconfigurable Intelligent Surfaces 59

A Deep Reinforcement Learning Approach for Autonomous Reconf...

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59th Annual IEEE international conference on communications (IEEE ICC)

作者： Choi, Hyuckjin Nguyen, Ly V. Choi, Junil Swindlehurst, A. Lee Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea Univ Calif Irvine Ctr Pervas Commun & Comp Irvine CA USA

ISBN: (纸本)9798350304060;9798350304053

A reconfigurable intelligent surface (RIS) is a prospective wireless technology that enhances wireless channel quality. An RIS is often equipped with passive array of elements and provides cost and power-efficient solutions for coverage extension of wireless communication systems. Without any radio frequency (RF) chains or computing resources, however, the RIS requires control information to be sent to it from an external unit, e.g., a base station (BS). The control information can be delivered by wired or wireless channels, and the BS must be aware of the RIS and the RIS-related channel conditions in order to effectively configure its behavior. Recent works have introduced hybrid RIS structures possessing a few active elements that can sense and digitally process received data. Here, we propose the operation of an entirely autonomous RIS that operates without a control link between the RIS and BS. Using a few sensing elements, the autonomous RIS employs a deep Q network (DQN) based on reinforcement learning in order to enhance the sum rate of the network. Our results illustrate the potential of deploying autonomous RISs in wireless networks with essentially no network overhead.

关键词： Autonomous RIS DQN deep learning MU-MISO rate maximization wireless communication

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Enhanced Plagiarism Detection in Online Assignments by Natural Language Processing and Machine Learning Techniques 3

Enhanced Plagiarism Detection in Online Assignments by Natur...

引用

3rd international conference on automation, computing and Renewable Systems, ICACRS 2024

作者： Hemandkumar, E. Varun Kumar, A. Gracewell, Jeffin Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798331532420

As online projects become more common in the educational setting, ensuring academic integrity is a growing concern. Traditional plagiarism detection algorithms often have trouble meeting some of the more sophisticated practices that students adopt for avoiding detection such as paraphrasing or word substitution. We present a machine learning based approach that was designed to enhance the effectiveness and accuracy of online assignment plagiarism detection, the proposed system can also detect minor plagiarism cases like paraphrased information and cross-linguistically similar. Due to the fact that the model proposed is capable of learning complex patterns and it was trained on a large dataset of a marginally homogenous dataset (academic publications), it maintains relatively high rates of recall and precision. The system uses multiple approaches of information retrieval to combine the interactions of textual relationships and elements such that it captures the text semantics of how person is feeling through word embedding. Later, it trains supervised learning models to classify possible plagiarism cases, like neural networks and Support Vector Machines (SVM). Also, the model can learn continuously from new data to improve its results. Our method improves the detection of traditional plagiarism and is also effective for more complex and subtle types of content reuse based on preliminary evaluation results. © 2024 IEEE.

关键词： Contrastive Learning

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Network Based control Systems for Six Axis Robot System

Network Based Control Systems for Six Axis Robot System

引用

international conference on computing communication control and automation (ICCUBEA)

作者： Ajit Nikam Sachin Argade Dipak Kole Prajwal Nimbokar Electrical Engineer JSPM Rajarshi Shahu College Of Engineering Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Principal network traffic in network-based control systems (NBCS) is driven by plant Sensor–controller–Actuator nodes operating in closed-loop. The performance provided by an NBCS's quality-of-control (QoC), and Every closed-loop operation is dependent on both the message scheduling method and the controller *** this paper, We discuss the problem of NBCS afterwards we describe the token ring scheduling algorithm solution ,sampling algorithm calculation and the simulation results.

关键词： automation Scheduling algorithms Simulation Telecommunication traffic control systems Robot sensing systems Stability analysis Scheduling Delays

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Machine Learning for Intricate Detection in 6G Networks 2

Machine Learning for Intricate Detection in 6G Networks

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2nd IEEE international conference on IoT, communication and automation Technology, ICICAT 2024

作者： Tiwari, Annapurna Nand Revathi, N. Almusawi, Muntather Prabha, Sakthi Karpagam, J. Hafeena, Md. IES College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Technology T.M Department of Electronics and Communication Engineering Tamil Nadu Palayam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Academy of Higher Education Department of Electrical and Electronics Engineering Karpagam TN India CMR Technical Campus Medchal Hyderabad India

ISBN: (纸本)9798350368109

In the world of 6G networks, the merging of micro-services-oriented designing and intelligent network management demonstrates the central role of neural networks (AI), machine learning (ML), and supervised learning (DL). This study looks into strengthening the security of 6G communications networks through the development of a new anomaly recognition System for 6G Connections (AD6GNs) utilizing ensemble learning (EL). The EL-ADCN workflow comprises prior processing, feature selection utilizing the hybrid methodology CFS-RF, and intruder detection applying hybrid EL methods. The system shows impressive performance on datasets with names like NSL-KDD, UNSW-NB2015, CIC-IDS2017, when CICDDOS2019, getting accuracy of 99.5%, 99.9%, 99.8%, while 99.95426%, respectively, with low false alarm rates. The combination of assistance vector machines (SVM) particularly random forest computing (RF) as EL techniques further improves the efficiency of packing and adaboosting in guaranteeing network that self-sustainability and preventive threat prevention in the envisioned 6G model. © 2024 IEEE.

关键词： Network intrusion

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Blockchain-Enabled Energy Trading in Microgrids for Sustainable computing Infrastructure 5

Blockchain-Enabled Energy Trading in Microgrids for Sustaina...

引用

5th international conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Kokila, M.L. Sworna Sunitha, R. Kalyanakumar, P. Raj, J. Relin Francis Krishnan, R. Santhana Gopikumar, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Rajarajeswari College of Engineering Department of Electronics and Communication Engineering Bangalore India Velammal College of Engineering and Technology Department of Information Technology Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India

ISBN: (纸本)9798350379945

This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions. © 2024 IEEE.

关键词： Blockchain technology energy trading GridCoin algorithm microgrids optimization renewable energy integration sustainable computing

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Machine Vision based automated 3-DOF Articulated Robot for fruit defect Identification and Segregation 14

Machine Vision based automated 3-DOF Articulated Robot for f...

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14th international conference on computing communication and Networking Technologies, ICCCNT 2023

作者： Ramkumar, S. Venusamy, Kanagaraj Eswaran, A. Jeevitha, N. Ramyapriya, G. Kannadhasan, S. Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering thandalam Chennai India Roever Engineering College Department of Electrical and Electronics Engineering Perambalur India Study World College of Engineering Department of Electronics and Communication Engineering Coimbatore Chennai India

ISBN: (纸本)9798350335095

The automation scenario in the current industrial as well as domestic applications has seen an exponential growth over the decade. Robot plays an important role in industrial automation but in some cases, it needs some extent of human intervention in quality inspection. This paper focuses on solving problems related to defect identification and segregation of fruits using 3-DOF articulated robot configuration with the integration of machine vision system and deep learning algorithms. To perceive the features of object, A USB camera is utilized. The machine vision algorithm will next use a variety of methods for processing digital images to extract the relevant data and decide whether to continue processing the product, redirect it to a different stage of production, or just throw it away. In recent days, the advancement in deep learning leads to the phenomenal growth in computer vision. There are many state-of-the-art object detection algorithms available in deep learning technology and we are using Inception-V3 algorithm that can be used for the machine vision operation in our solution. Based on the results from the algorithm, segregation operation is carried out successfully. © 2023 IEEE.

关键词： Fruits

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A Hybrid Prediction Model for Disseminating the Factors Related to Anomaly Detection in Software Defined Networks 15

A Hybrid Prediction Model for Disseminating the Factors Rela...

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15th international conference on computing communication and Networking Technologies, ICCCNT 2024

作者： Darshan, S. Radhika, N. Radhika, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

The domain of network traffic and anomaly reporting has its highlights in recent days towards real-time applications. The utilization of network data with its correlating attributes and its measures must be considered before deriving a pattern or decision making process. SDN(Software Define Networks) in turn has been facilitating more towards the efficient and reliable mechanism for data control and network management system. This research focus on the development of a hybrid prediction model for disseminating the factors that contribute towards anomaly detection in SDN. The experimentation can be made by hybridizing different classification schemes with tuned parametric components upon statistical modelling and evaluation. The proposed hybrid model is suggested up with the hybridization of SVM, NN along with Ada Boost algorithm with tuned parameter metrics. From the proposed model it is evident that the selected features will play a significant key role in anomaly prediction in a given network. © 2024 IEEE.

关键词： Prediction models

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