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IEEE Symposium on Wireless technology and Applications (ISWTA)

作者： Shehab Khan Noor Mohamed Nasrun Osman Mohd Najib Mohd Yasin Arif Mawardi Ismail Tanvir Islam Nurulazlina Ramli Advanced Communication Engineering (ACE) Centre of Excellence Faculty Electronic Engineering Technology University Malaysia Perlis Kangar Perlis Malaysia Department of Electrical and Computer Engineering University of Houston Houston TX USA Centre for Advanced Electrical & Electronic System (CAEES) Faculty of Engineering Built Environment and Information Technology SEGi University Petaling Jaya Selangor Malaysia

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

In this paper, a flexible wearable antenna based on uniform circular array of four identical microstrip patch antennas on a felt textile substrate was designed to generate multiple orbital angular momentum (OAM) modes. The proposed antenna was simulated and analyzed to confirm the generation of OAM mode +1 and −1. The concept of reconfigurable method with 16 p-i-n diodes was implemented for the switching purpose. The proposed antenna covered overall bandwidth above 700 MHz for both the modes in free space while the obtained gain by mode 1 was 1.981 dBi and mode −1 was 1.993 dBi. Moreover, the mode purity analysis was carried out to verify the generation of OAM mode +1 and −1 with acceptable SAR value. The purity obtained was 47.78% for mode +1 and 47.91% for mode −1 at free space. This study shows that using textile antennas for modal multiplexing to produce multimode OAM waves is feasible in order to increase channel capacity and spectrum efficiency.

关键词： Space vehicles Wireless communication Orbital calculations Microstrip antenna arrays Switches Bandwidth Wearable antennas

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IoT Security Frameworks: A Comparative Review with a Focus on Privacy 2

IoT Security Frameworks: A Comparative Review with a Focus o...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Hasnawi, Abduljaleel Niu, Yitong Tawfeq, Jamal F. Ranjan Pradhan, Manas Salahat, Mohammed Ghazal, Taher M. Ministry Of Higher Education And Scientific Research Baghdad Iraq College Of Information Technology Imam Ja'afar Al-Sadiq University Department Of Communication Technology Engineering Baghdad Iraq School Of Aeronautical Engineering AnYang University Anyang China Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions. © 2024 IEEE.

关键词： Internet of things

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An IoT-enabled monitoring and control system for electrochromic smart windows

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Discover Internet of Things 2024年第1期4卷 30页

作者： Koay, Hsien-Hern Thien, Gregory Soon How Au, Benedict Wen-Cheun Abdelhamed, Abdelrahman Hamed Ebrahem Balachandran, Ruthramurthy Ng, Zi-Neng Chan, Kah-Yoong Centre for Advanced Devices and Systems Faculty of Engineering Multimedia University Selangor Cyberjaya63100 Malaysia Micron Semiconductor Asia Operations Pte Ltd. 1 North Coast Drive Singapore757432 Singapore Sri Desa International Secondary School Taman Desa Kuala Lumpur58100 Malaysia Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University P O Box 1888 Adama Ethiopia School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Jalan Sunsuria Bandar Sunsuria Selangor Sepang43900 Malaysia

Electrochromic (EC) smart windows are highly efficient in preventing solar irradiation. Nevertheless, an insufficient automated monitoring and control system for EC windows has been observed. This study outlined the framework for designing a control system that effectively regulated the voltage polarity of an EC smart window. Integrating Internet of Things (IoT) capabilities and environmental sensors enhanced the smart window control system. This integration allowed for automated monitoring and adjustment of light intensity (below and above 30,000 lx), temperature (below and above 32 °C), and UV index (below 3 and above 7) for the smart window. The magnitude of the current supplied from the output load of the operational circuit module was directly correlated with the switching time of the electrochromic window. Moreover, voltage fluctuation was observed during the evaluation testing of the control system, which was ascribed to the reliability of the breadboard platform. This study also provided a functional design analysis for a Wi-Fi-enabled control system, presented insights into manual and automatic control modes under diverse environmental conditions, and improved user interaction through real-time data visualization using the IoT Blynk platform. Overall, this study presented a range of cost-effective approaches for managing the dynamic EC window using Wi-Fi and sensor-enabled technology. © The Author(s) 2024.

关键词： Wi-Fi

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Red Palm Weevil Detection System for Early Warning and Mitigation of Crop Loss

Red Palm Weevil Detection System for Early Warning and Mitig...

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Connected Systems & Intelligence (CSI), International Conference on

作者： Parvathy S.R. Deepak Jayan P. Nimmy Pathrose Rajesh K.R. Lekshmy Janardhanan R James Varghese Vishnu S. Nimmy Mathew Sujith B. Kallara Centre for Development of Advanced Computing A Scientific Society of the Ministry of Communication and Information Technology Govt. of India Thiruvananthapuram Kerala India

ISBN: (数字)9781665458153

ISBN: (纸本)9781665458160

Red Palm Weevil infestation affecting palm trees is a major problem faced by coconut farmers, causing severe economic losses to palm cultivators worldwide. This infestation is fatal to the trees and can easily spread to nearby palms affecting a large number of trees, if left undetected. Detection of infestation at an early stage is critical for timely action to save palm trees. The paper presents a system for early detection of Red Palm Weevil infestation, by providing a smart, non-invasive and portable solution to the problem. The system based on an accelerometer sensor, analyses the signals from the palm and checks out for the unique features present in the signals generated by the pest. Audio, visual and SMS warnings are generated by the system on detecting infestation. The system also wirelessly communicates the infestation status to a remote database, which stores and maintains the historical data of palm trees monitored using this device, for facilitating pest control and management.

关键词： Economics Productivity Visualization Databases Vegetation Dogs Tools

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Transfer Learning-Enhanced Gradient Boosting Models for Wildfire Detection Using UAV Imagery

Transfer Learning-Enhanced Gradient Boosting Models for Wild...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Pirunthavi Wijikumar Shouthiri Partheepan Jahan Hassan Farzad Sanati Biplob Ray Department of ICT Faculty of Technological Studies University of Vavuniya Vavuniya Sri Lanka School of Engineering and Technology Central Queensland University Australia Department of Computing Faculty of Science Eastern University of Sri Lanka Batticaloa Sri Lanka Centre for Machine Learning Networking and Education Technology (CML-NET) College of Information and Communication Technology School of Engineering and Technology Central Queensland University Australia

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

Wildfires pose a serious hazard to both life and the environment, necessitating accurate and timely detection methods to minimize their impact. However, traditional approaches have limitations in precision, response time, and adaptability to environmental factors. To address this gap, machine learning (ML) and deep learning (DL) approaches are increasingly being adopted for fire detection. This study proposes a hybrid approach that combines deep feature extraction using Transfer Learning (TL) with EfficientNetB0 and traditional Gradient Boosting Models (GBM), specifically LightGBM, XGBoost, and CatBoost, for wildfire detection using UAV imagery. EfficientNetB0 is used to extract features, which are then dimensionally reduced using Principal Component Analysis (PCA). Optuna framework is used for hyperparameter tuning, to maximize the performance of the model. The results show that the GBM models enhanced with TL outperform their baseline counterparts, with CatBoost + TL achieving the highest precision (92. 37%). These findings confirm the role of TL in improving model precision, efficiency, and computational resource utilization, making it ideal for real-time wildfire detection.

关键词： Wireless communication Wildfires Computational modeling Transfer learning Feature extraction Boosting Autonomous aerial vehicles Time factors Tuning Principal component analysis

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Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

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Journal of Dynamics, Monitoring and Diagnostics 2022年第3期1卷 176-187页

作者： Debasish Mishra Ashok Priyadarshi Sarthak M Das Sristi Shree Abhinav Gupta Surjya K Pal Debashish Chakravarty Advanced Technology Development Centre Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India Mathematics and Computing Birla Institute of Technology MesraRanchiJharkhand 835215India Information Technology Birla Institute of Technology MesraRanchiJharkhand 835215India Computer Science and Engineering Birla Institute of Technology MesraRanchiJharkhand 835215India Electronics and Communication Engineering Birla Institute of Technology MesraRanchiJharkhand 835215India Department of Mechanical Engineering Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India Department of Mining Engineering Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business *** ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing *** article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing *** major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)*** consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real *** edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real *** addresses time-critical manufacturing *** receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding *** experiments are conducted for training the neural *** framework successfully tracks process quality and improves the welding by controlling it in real *** system enables faster monitoring and control achieved in less than 1 *** framework is validated through several experiments.

关键词： cloud edge deep neural networks friction stir welding Industry 4.0 internet of things machine learning manufacturing process control process monitoring signal processing

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A Contemporary Review on Drought Modeling Using Machine Learning Approaches

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Computer Modeling in Engineering & Sciences 2021年第8期128卷 447-487页

作者： Karpagam Sundararajan Lalit Garg Kathiravan Srinivasan Ali Kashif Bashir Jayakumar Kaliappan Ganapathy Pattukandan Ganapathy Senthil Kumaran Selvaraj T.Meena School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Faculty of Information and Communication Technology University of MaltaMsidaMSD2080Malta Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Centre for Disaster Mitigation and Management Vellore Institute of TechnologyVellore632014India Department of Manufacturing Engineering School of Mechanical EngineeringVellore Institute of TechnologyVellore632014India School of Civil Engineering Vellore Institute of TechnologyVellore632014India

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Itsbeginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughtsin the last few decades. Predicting future droughts is vital for framing drought management plans to sustainnatural resources. The data-driven modelling for forecasting the metrological time series prediction is becomingmore powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques havedemonstrated success in the drought prediction process and are becoming popular to predict the weather, especiallythe minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecastinginclude support vector machines (SVM), support vector regression, random forest, decision tree, logistic regression,Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzyinference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models,and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presentsa recent review of the literature using ML in drought prediction, the drought indices, dataset, and performancemetrics.

关键词： Drought forecasting machine learning drought indices stochastic models fuzzy logic dynamic method hybrid method

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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Computer Systems Science & Engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Towards Blockchain-based Decentralized Employee Transfer System

Towards Blockchain-based Decentralized Employee Transfer Sys...

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Blockchain computing and Applications (BCCA), International Conference on

作者： Alam Zaib Ehzaz Mustafa Ahsan Khan Hasan Ali Khattak Isa Ali Ibrahim Abdallah Namoun Junaid Shuja Raja Wasim Ahmad Department of Computer Science Comsats University Islamabad Pakistan Faculty of Computing & Information Technology Sohar University Sohar Oman School of Information and Communication Technology Federal University of Technology Owerri Imo Nigeria AI Centre Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia Department of Computer Science Southeast Missouri State University Cape Girardeau MO USA Department of Computer & Information Sciences Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Employees are the main driving force in organizations that undergo recruitment, promotion, and transfer. There are many employee management systems, but most of them are centralized. Centralization leads to problems such as lack of transparency, auditability issues, vulnerability, and a single point of failure. Blockchain technology addresses these concerns. Despite some blockchain-based HRM and employee management systems, none of the existing blockchain-based systems have addressed the problems of employee transfers. We propose a secure and transparent blockchain-based employee transfer (E-transfer) system to address this. The proposed system uses smart contracts for transfer management, recording outcomes on an unchangeable ledger. We conducted tests on Hyperledger Fabric for our proposed system. Our study aims to introduce an automated, auditable, and efficient transfer process. The findings of the study validate the effectiveness of the blockchain-based E-transfer system.

关键词： Electric potential Distributed ledger Smart contracts Force Focusing Organizations Fabrics Blockchains Recording Recruitment

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Anticipation of Parking Vacancy during Peak/Non-peak Hours using Convolutional Neural Network-YOLOv3 in University Campus

Anticipation of Parking Vacancy during Peak/Non-peak Hours u...

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2021 International Conference on Green Energy, computing and Sustainable technology, GECOST 2021

作者： Davan, Jasvina Mae A.P. Ealn Koh, Tieng Wei Tong, Dong Ling Tseu, Kwan Lee First City University College Artificial Intelligence Lab Faculty of Computing and Engineering Petaling Jaya Malaysia University of Putra Malaysia Faculty of Computer Science and Information Technology Selangor Malaysia Universiti Tunku Abdul Rahman Centre of IoT Big Data Faculty of Information and Communication Technology Perak Malaysia

ISBN: (纸本)9781665438650

Searching for a publicly available parking space has become a nightmare to many drivers. With the constant development of global urbanization, human population has increased drastically in the past decades. Searching for a publicly available parking space in a highly populated area can be daunting and time consuming. No matter how much time is spent to find a vacant parking space, it always causes traffic congestion in the area. To alleviate these problems, it is of utmost importance to have a system that can detect and display the vacant parking spaces in real-time. This paper has conducted a study of anticipation of parking vacancy using convolutional neural network called YOLOv3 in a university campus. Image data is gathered from the video capture of the university's campus open space parking lot. The YOLOv3 algorithm is used to train and predict whether the space is vacant or occupied. Results showed that YOLOv3 has been able to correctly predict the vacant space. The result of the rendering video will then be transformed into an image and is sent to the students via a Telegram group. © 2021 IEEE.

关键词： Convolutional neural networks

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