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International Electrical Engineering Congress (iEECON)

作者： Jiran Sithiyopasakul Tuanjai Archevapanich Saran Sithiyopasakul Attasit Lasakul Boonchana Purahong Chawalit Benjangkaprasert School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.

关键词： Performance evaluation Cloud computing Web services System performance Throughput Internet of Things Time factors

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Design and Fabrication of COVID-19 Microstrip Patch Antenna for Wireless Applications

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Progress In Electromagnetics Research C 2022年 118卷 125-134页

作者： Abdaljabar, Jihan S. Madi, Mervat Alhindawi, Asaad Kabalan, Karim Department of Communication Engineering Polytechnique University Sulaymaniyah Iraq School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates Department of Electrical and Computer American University of Beirut Beirut Lebanon

This paper presents a novel unique microstrip fractal patch antenna with a COVID-19 shape designed for wireless applications. The COVID-19 antenna is a compact, miniature size, multiband, low weight, and low-cost patch antenna;the demonstrated patch antenna, simulated using the HFSS software program, consists of a circular printed patch with a radius of 0.4 cm surrounded by 5 pairs of crowns. The antenna is implemented on a double-sided copper plate with an FR4-epoxy substrate of 1 × 1 cm2 area and 1.6 mm thickness. This small patch operates and resonates on two frequencies 7.5 GHz and 17 GHz within C and Ku bands, respectively. The simulated and measured gains were respectively 0.8 dB and 0.2 dB at the lower frequency and 2.21 dB and 2 dB at the higher frequency. A coaxial probe feeding method is used in the simulation, and printed prototypes showed excellent consistency between measured and simulated resonance frequencies. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Microstrip antennas

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Precise Timing over Beyond 5G Networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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IEEE International Symposium on Personal, Indoor and Mobile Radio communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite Systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization Software defined networking Local area networks

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Facial Recognition Impact in Smartphone Sector 4

Facial Recognition Impact in Smartphone Sector

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Jha, Amaresh Sudhakar, P. Lokhande, Sharayu Ashish Kumar Ishodzhanova, Galina Venkatrao, K. Zhakupova, Aliya School of Digital Media Journalism & Mass Communication Gd Goenka University India Vignan's Nirula Institute of Technology and Science for Women Department of Ece India Computer Engineering Army Institute of Technology India International Education Corporation Department of Architecture Kazakhstan Srkr Engineering College Department of Ece India International Education Corporation Department of Linguistics Kazakhstan

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In recent times, technological advancement brings many benefits for people and facial recognition technology is one of them. Nowadays, S martphone users increasingly use facial recognition features that help in verifying identity of users for different purposes, for example, accessing their device before making any payment from their phones. This study paper has analysed impact of facial recognition systems in S martphone sectors by discussing its advantages and challenges to understand both its negative and positive impact. Literature review section has provided relevant information on selected topic as it has elaborated that different large organisation have their own face recognition software for Apple, Facebook, and Google. Additionally, it has mentioned that there are two different types of facial recognition software such as camera-based and infrared;however, regarding effectiveness, infrared works more appropriate rather than camera-based recognition technologies. The paper has also elaborated that this technology offers automating time-tracking systems and it has touch less sign options, which help them in limiting physical contact with others. Result of this study showed that facial recognition helps to protect any type of unauthorised access in a Smartphone as well as it could prevent cyber threats by recognising face of S martphone users. Additionally, due to this software's fast identification feature, it could successfully verify faces of people within a few seconds. However, as this software is based on artificial intelligence, unlike other applications, it is highly vulnerable as hackers can hack Smartphones if strong algorithms are more used. Lastly, to prevent this issue a strong AI infrastructure and model security is required in future. © 2022 IEEE.

关键词： Smartphones

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Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

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A mixed-precision binary neural network architecture for touch modality classification 16

A mixed-precision binary neural network architecture for tou...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Younes, Hamoud Ibrahim, Ali Rizk, Mostafa Valle, Maurizio COSMIC Laboratory Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genova Genoa Italy Department of Computer and Communication Engineering Lebanese International University Bekaa Lebanon Department of Physics and Electronics Faculty of Sciences Lebanese University Lebanon

ISBN: (纸本)9783800755899

Binary Neural Networks (BNN) have been proposed to address the computational complexity and memory requirements of Convolutional Neural Networks (CNN). However, in most of the applications, BNNs suffer from severe accuracy loss due to the 1-bit quantization. In this paper, a Mixed-Precision Binary Weight Network (MP-BWN) is proposed as a compromise between CNN and BNN. Compared to traditional binary networks, MP-BWN offers better performance with an acceptable increase in the network size. MP-BWN achieves up to 99% reduction in both the number of operations and the network size compared to similar state-of-the-art solutions. When validated on a touch modality classification problem, the MP-BWN surpassed similar existing solutions by achieving a classification accuracy of 77.8%. © VDE VERLAG GMBH.

关键词： Neural networks

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network

Deep Learning based vehicle image detection using Yolo V5 wi...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： S. Swapna Rani Aditya Mudigonda S V Hemanth P.N. Sundararajan G. Vinoda Reddy G. Amirthayogam Department of Electronics and Communication Engineering Maturi Venkata Subba Rao (MVSR) Engineering College Hyderabad India JNIAS School of Planning and Architecture Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering (AI&ML) CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Information Technology Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique—a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image’s quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region’s borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image’s most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms.

关键词： YOLO Deep learning Image segmentation Accuracy Surveillance Transportation Transforms

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Plant Disease Detection and Classification Using Transfer Learning

Plant Disease Detection and Classification Using Transfer Le...

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Innovative Computing, Intelligent communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Karthigaiveni Madhubala. C G. Avinash E. Saranya P. Rohini G. Yogapriya Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India School of Environment and Architecture Design SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Engineering Sri Eshwar College of Engineering Chennai India Department of Computer Science and Engineering Saranathan College of Engineering Trichy Tamilnadu Chettinad School of Architecture Chettinad Academy of Research and Education Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases.

关键词： Deep learning Productivity Plant diseases Image segmentation Accuracy Computational modeling Transfer learning Crops Agriculture Robustness

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Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT based Self-Sustainable IoT Network

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the communications Society 2025年 6卷 4311-4327页

作者： Sadia, Haleema Hassan, Ahmad Kamal Abbas, Ziaul Haq Abbas, Ghulam Baker, Thar Saeed, Nasir United Arab Emirates University College of Engineering Department of Electrical and Communication Engineering Al Ain United Arab Emirates Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Pakistan University of Brighton School of Architecture Technology and Engineering BrightonBN2 4GJ United Kingdom

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To overcome this challenge, intelligent reflecting surfaces (IRSs) have recently been introduced in IoT networks to improve network efficiency by controlling signal reflections. In this article, we propose a novel IRS-enabled phase cooperative framework that aims to maximize the energy efficiency (EE) of the PS-SWIPT based SS-IoT network. Our framework leverages the idea of phase cooperation between two distinct networks without requiring additional hardware resources. We employed transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS with effective phase cooperation between APs. The EE maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the user’s network using heuristic BF approaches and phase optimization via semidefinite relaxation (SDR) approaches. To combat the computational complexity of AO, we also propose a low-complexity alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. This work demonstrates the potential of IRS phase cooperation for enhancing the EE of different networks without constraining hardware resources. © 2020 IEEE.

关键词： Energy transfer

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Grain Size Distribution Analysis of Landslide Dams Caused by Dynamic Accumulation of Weathered Rock Landslides Using Discrete Element Modelling 29th

Grain Size Distribution Analysis of Landslide Dams Caused by...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Peng, Ming Zhao, Qingxi Shen, Danyi Zhu, Yan Zhao, Qingxin Zhou, Jiawen Cai, Shuo Key Laboratory of Geotechnical & Underground Engineering of Ministry of Education Tongji University Shanghai China Department of Geotechnical Engineering College of Civil Engineering Tongji University Shanghai China Institute of Geotechnical Engineering College of Civil Engineering and Architecture Zhejiang University Hangzhou China Shanghai Research Center of Ocean & Shipbuilding Engineering China Shipbuilding NDRI Engineering Co. Ltd. Shanghai China State Key Laboratory Hydraulic & Mountain River Engineering Sichuan University Chengdu China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9783031425141

The differences in the structure and composition materials of landslide dams often lead to great dissimilarity in dam stability, which often depend on the dynamic accumulation process of landslide dam. In order to investigate the response of dynamic characteristics of landslide to the composition material distribution of landslide dams, the migration and accumulation process of rock slopes with two weathering degrees was simulated by bonded particles and micro-cracks based on discrete element modelling. A new reconstructed distribution identification method was proposed in this paper to quantify the grain size distribution of dam. The results showed that the composition materials of dam, formed by weakly weathered slope, was poorly sorted, with medium particles located in surface layer, coarse particles located in middle and fine particles located in bottom of the valley. The material composition of dam, formed by strongly weathered slope, was well sorted, which spatial distribution showed well reverse segregation structure. It was found that the differences in particle size distribution between two kinds of landslide dams were mainly manifested in fine particles and coarse particles. The kinetic disintegration in weakly weathered slope was weakened, due to the accumulation of force coupling below hard rock at substrate, which occurred because of the fragmentation of particles aside it. As a result, there are more coarse particles and fewer fine particles in grain size distribution of its accumulation dam. This paper contributes to a better understanding of the structure and materials distribution of landslide dams, and assists the prediction of the stability of landslide dams. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle size

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