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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Cuddapah Anitha Dalia Al-Tarazi GD. Vignesh A. Vinotha Vasuki Jayant Giri M. Dinesh Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of Architecture Engineering Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.

关键词： Vehicular ad hoc networks Routing Throughput Synchronization Resource management Dedicated short range communication Particle swarm optimization Artificial intelligence Vehicle dynamics Testing

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STRUCTURE-ORIENTED PROGRESSIVE LOW-RANK IMAGE RESTORATION FOR DEFENDING ADVERSARIAL ATTACKS

STRUCTURE-ORIENTED PROGRESSIVE LOW-RANK IMAGE RESTORATION FO...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhao, Zhiqun Wang, Hengyou Sun, Hao Cao, Wenming He, Zhihai Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States School of Science Beijing University of Civil Engineering and Architecture Beijing100044 China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665438643

Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. On the other hand, human eyes recognize objects based on their global structures and semantic cues, instead of local image textures. In this work, we propose to develop a structure-oriented progressive low-rank image completion method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums. Our experimental results demonstrate the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures. © 2021 IEEE computer Society. All rights reserved.

关键词： Deep neural networks

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A Hybrid Stock optimization Approach for Inventory Management 3

A Hybrid Stock optimization Approach for Inventory Managemen...

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3rd International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2021

作者： Cimen, Egemen Berki Kurban, Ilknur Selmanoglu, Ozgur Sahin, Murat Kilinc, Deniz Data Science Eldor Corporation Izmir Turkey Software Development Eldor Corporation Izmir Turkey Technology Eldor Corporation Izmir Turkey Data Science Kalybe.AI Izmir Turkey Bakirçay University Computer Engineering Department of Faculty of Engineering and Architecture Izmir Turkey

ISBN: (纸本)9781665440585

Modern world is rapidly evolving around knowledge and business that know how to use knowledge shows superiority. Thus, smart decision making becomes vital for the modern business world to achieve sustainability in life and business. Especially, with a world of scarce resources, utilizing knowledge would play critical not only today but also for future. Moreover, using knowledge is inevitable in supply chain and inventory management must be supported with smart algorithms and modern heuristics to avoid excessive inventory while fighting with stockout. Therefore, this study explores the opportunity for inventory planning with heuristics and tailored techniques as well as how to hybridize modern heuristics and tailored techniques. In this study, inventory optimization experiments are proposed to model spare part inventory management and find the best way to determine reorder amount to deal with shortage and excessive inventory. Four heuristics which are i) basic golden ratio, ii) simulated annealing, iii) statistical rule-based heuristic and iv) hybrid algorithm of simulated annealing and statistical rule-based heuristic are evaluated on existing spare part dataset with an example from real-life part supplier named Eldor. In this test case, 400 products are analyzed, and best reorder points and amounts are selected with the help of heuristics. Heuristics' main structures and parameters are adjusted for the problem's need and improvement on quality of results. Parameters are determined according to trial and error with experts' guidance on heuristics. The best result suggests 8% improvement on cost and 37% improvement on inventory load could achieve with the help of heuristics. These solutions are usable against even hard constraints like shortage. © 2021 IEEE.

关键词： Simulated annealing

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Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole for 5G Base Station Antenna Application

Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole f...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Ian Jerome Q. Santos Maleeya Tangjitjetsada Paitoon Rakluea Norakamon Wongsin Chawalit Rakluea Chatree Mahatthanajatuphat Nonchanutt Chudpooti Tawan Thianthong Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Thanyaburi Pathum Thani Thailand Department of Electronics and Telecommunication Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Wongsawang Bangsue Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bnagkok Wongsawang Bangsue Bangkok Thailand Siam Airport Ground Service Co. Ltd. Don Mueang Bangkok Thailand

This article provides an explanation of the use of a Twin-Array Quasi Frequency Selective Surface (FSS) Reflector with Dual Band Cross-Dipole for antenna applications in 5G base stations. The base material employed in this study is FR4 sheet, which has a dielectric constant (ε r ) of 4.3. The antenna obtained has dimensions of 100x100 mm 2 and functions throughout the frequency spectrum spanning from 730 to 860 MHz, 1700 to 2130 MHz. The primary benefits of this antenna design are its lightweight structural composition, compatibility with the frequency range of 730–860 MHz, and discernible performance attributes. The CST Studio Suite software was used to conduct a simulation of the antenna’s performance. The antenna has an operating frequency range of 730–860 MHz and 1700–2130 MHz, enabling it to support a unidirectional wave propagation pattern that is suitable for the implementation of a 5G mobile communication base station.

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Reducing Operational Expenses of Lorawan-Based Solutions that Connect Urban and Rural Areas

SSRN

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SSRN 2022年

作者： Finochietto, José Mariano Santos, Rodrigo Ochoa, Sergio F. Meseguer, Roc GIDI Department of Information Technology Universidad Nacional de Mar del Plata Mar del Plata7600 Argentina Department of Electrical and Computers Universidad Nacional del Sur CONICET Bahía Blanca8000 Argentina Department of Computer Science Universidad de Chile Santiago8370456 Chile Department of Computer Architecture Universitat Politècnica de Catalunya Barcelona08034 Spain

LoRaWAN has become the most widely used Low Power Wide Area Network technology to implement monitoring solutions based on the Internet of Remote Things (IoRT) paradigm. Typically, these solutions interconnect remote sensing areas and data processing infrastructure located in urban centers. The operation expenses of these solutions depend mainly on the traffic sent through the network backhaul;i.e., the link that connects the remote sensing area and urban area where the data is processed and stored. This service is provided by telecommunication companies and represents the main operation cost of IoRT solutions. These expenses usually limit the affordability of IoRT-based systems in developing countries, and also in scenarios where the operational cost is an issue to address. This paper presents an extension to the LoRaWAN protocol, named Node Aware-LoRaWAN (NA-LoRaWAN), that reduces the traffic in backhaul, decreasing thus the operation expenses of IoRT-based systems. In order to evaluate the performance of NA-LoRaWAN, it was compared to a regular LoRaWAN implementation. Depending on the network scenario, the proposed extension reduces the traffic through the backhaul in the range of 12-34 %. This opens several possibilities to use it in application domains with low operational budget, and reduce expenses in already implemented systems. © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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A Resource-Friendly Authentication Protocol for UAV-Based Massive Crowd Management Systems

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Security and Communication Networks 2021年第1期2021卷

作者： Alzahrani, Bander A. Barnawi, Ahmed Chaudhry, Shehzad Ashraf Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Engineering Faculty of Engineering and Architecture Istanbul Gelisim University Istanbul Turkey

As a part of the smart city revolution, crowd management is an emerging trend and it can enhance the quality of life. Unmanned ariel vehicles (UAVs) can help in making the crowd management process more efficient and more accurate. UAVs can monitor and collect environmental-related surveillance data and share real-time information with each other and with the decision makers. However, the battery-operated UAVs communicate over the open public channel making the privacy and security of the UAVs a crucial element in mission-critical applications. The weaknesses of the existing scheme pave the way to design a new lightweight authentication scheme for UAV environments. In this article, we present a symmetric key primitive-based scheme and provide authentication among a user and a UAV through an intermediate control center. Due to usage of symmetric key and elliptic curve cryptography, the proposed scheme fulfils the performance requirements of the UAVs. The security of the proposed scheme is substantiated through BAN logic, along with a discussion on security features extended by the proposed scheme. The performance and security comparisons show that the proposed scheme provides adequate security and efficiency and can be practically deployed in real UAV environments. © 2021 Bander A. Alzahrani et al.

关键词： Authentication

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Redefining Neural architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components

arXiv

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arXiv 2021年

作者： Garciarena, Unai Santana, Roberto Mendiburu, Alexander The Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Donostia20018 Spain The Department of Computer Architecture and Technology University of the Basque Country UPV/EHU Donostia20018 Spain

With neural architecture search methods gaining ground on manually designed deep neural networks -even more rapidly as model sophistication escalates-, the research trend shifts towards arranging different and often increasingly complex neural architecture search spaces. In this conjuncture, delineating algorithms which can efficiently explore these search spaces can result in a significant improvement over currently used methods, which, in general, randomly select the structural variation operator, hoping for a performance gain. In this paper, we investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models. These models have an extensive and complex search space of structures as they require multiple sub-networks within the general model in order to answer to different output types. From that investigation, we extract a set of general guidelines, whose application is not limited to that particular type of model, and are useful to determine the direction in which an architecture optimization method could find the largest improvement. To deduce the set of guidelines, we characterize both the variation operators, according to their effect on the complexity and performance of the model;and the models, relying on diverse metrics which estimate the quality of the different parts composing *** Codes 68T07 © 2021, CC BY-SA.

关键词： Deep neural networks

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CSI-Based Human Activity Recognition using Convolutional Neural Networks 11

CSI-Based Human Activity Recognition using Convolutional Neu...

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11th International Conference on computer Engineering and Knowledge, ICCKE 2021

作者： Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shahid Beheshti University G. C. Cognitive Telecommunication Research Group Department of Electrical Engineering Tehran Iran Standard Research Institute Faculty of Technology and Engineering Department of Electrical Engineering Alborz31745-139 Iran University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9781665402088

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks. © 2021 IEEE.

关键词： Channel state information

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Higher education assessment practice in the era of generative AI tools

arXiv

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arXiv 2024年

作者： Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Sharma, Hemlata Olayinka, Olakunle School of Architecture Technology & Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computing & Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Department of Computing Sheffield Hallam University SheffieldS1 2NU United Kingdom

The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE. © 2024, CC BY.

关键词： Project management

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Construction Progress Monitoring In Smart Cities Using Deep Alexnet

Construction Progress Monitoring In Smart Cities Using Deep ...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： A. Rajavel S. Karkuzhali Sarvdaman Sharma Sitesh Kumar Singh Senthilkumar P S. Jhansi Ida Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Business Systems R.M.K Engineering College India

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle.

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