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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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Trajectory Control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Model-Based Testing of Access Control Requirement in Multi-tenant Application: An Extensive Life Cycle 1

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3rd International Conference on computer Science, Electrical and Electronic engineering, ICCEE 2021

作者： Duraisamy, Gunavathi Ghani, Abdul Azim Abd Zulzalil, Hazura Abdullah, Azizol Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Selangor Serdang Malaysia Faculty of Computer Science and Information Technology Department of Communication Technology and Networking Universiti Putra Malaysia Selangor Serdang Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Sungai Long Campus Selangor Kajang Malaysia

ISBN: (数字)9789811697814

ISBN: (纸本)9789811697807

Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity of model-based testing is the modelling activity. Models are used to explain and represent the behaviour of the product. The requirement will be translated into models and those models will be used in generating the test cases automatically in model-based testing. The modelling activity clarifies test requirement and contribute to automatic generation and execution of large test suites with tool support. Model-based testing provides numerous advantages in testing;mainly on achieving appropriate test coverage and reduce manual effort and time spent since it can execute the test cases automatically. Despite all the benefit it can provide, there are some drawbacks too. However, the challenges faced can overcome by implementing proper test modelling activity and by adopting a well-versed test management activity. Thus, in this paper, we have studied the existing testing process, the taxonomy of model-based testing and model-based testing steps and proposed an extensive lifecycle for model-based testing with detail activities and steps to be carried out in order to test and validate access control requirement for multi-tenant application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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CUDA Parallelization of Teacher-Learner Optimization Algorithm 1

CUDA Parallelization of Teacher-Learner Optimization Algorit...

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1st International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Abbasi, Mahdi Alghanim, Mohammed Sabri, Mohammad Khosravi, Mohammad R. Bu Ali Sina University Engineering Faculty Department of Computer Engineering Hamedan Iran Zarqa University Faculty of Information Technology Jordan Zarqa University Department of Management Information System Jordan Weifang University of Science and Technology Weifang Shandong China

ISBN: (纸本)9798350332742

Evolutionary algorithms are effective methods for solving optimization problems that do not require any special assumptions to find optimal solutions. Optimization based on teaching and learning has been parallelized using CUDA parallelization technology. The results show that the proposed parallel version of the algorithm is fast and efficient in teaching and learning phases. © 2022 IEEE.

关键词： Evolutionary algorithms

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Embedded Machine Learning on Accelerometer Data for Exercise Classification

Embedded Machine Learning on Accelerometer Data for Exercise...

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Applied Sensing Conference (APSCON), IEEE

作者： Rufyid-u- Nissa Nemai Chandra Karmakar Maryam Shojaei Baghini Electrical Engineering Department Indian Institute of Technology Bombay Mumbai India Department of Electrical and Computer System Engineering Monash University Australia

This paper describes the use of embedded machine learning (ML) on raw accelerometer data to classify three lower-limb exercises. The developed model, which uses supervised ML can accurately classify the exercises. The model performed with an accuracy of 99.41 % on the test data and F1 score of 0.99. The trained model is deployed on the microcontroller board, Arduino Nano 33 BLE Sense (ARM Cortex M4F processer). The system has been developed using open-source platforms. The developed wearable device, PhysioSens has the potential to enhance limb exercise monitoring in home-based physical therapy.

关键词： Accelerometers Performance evaluation Microcontrollers Wearable computers Medical treatment Machine learning Brain modeling

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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Abnormal Image Classification Using Vision Transformer for Smart Agriculture 8

Abnormal Image Classification Using Vision Transformer for S...

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8th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Hafeez, Talha Shahid, Muhammad Farrukh Khanzada, Tariq J.S. Fast School of Computing FAST-NUCES Karachi Pakistan King Abdulaziz University Faculty of Computing and Information Technology Department of Information System Jeddah21589 Saudi Arabia Mehran Uet Computer Systems Engineering Departments Jamshoro Pakistan

ISBN: (纸本)9798350327090

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset. © 2023 IEEE.

关键词： Classification (of information)

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IOT Based Smart Home Automation Using ESP32

IOT Based Smart Home Automation Using ESP32

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International Conference on computer and Communications Technologies (ICCCT)

作者： K Mariammal R Arunadevi J Lurdhumary R Ramya M S Vinotheni YuvikaaShree S Swathi D R Department of Electronics Engineering Madras Institute of Technology Anna University Chennai India Department of Computer Science & Business System Sri Sairam Engineering College Chennai India Department of ECE Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

This paper presents the design and implementation of an IoT-enabled smart home automation system utilizing the ESP32 microcontroller to enhance home management through real-time remote control. The proposed system integrates various sensors, including temperature, humidity, motion (PIR), light, and raindrop sensors, to automate tasks based on environmental conditions, thereby improving energy efficiency and convenience. Additionally, the system incorporates an RFID-based door locking mechanism for enhanced security and supports voice commands for hands-free operation, catering to individuals with mobility limitations. A web interface allows users to monitor and control connected devices remotely via Wi-Fi, offering a seamless and user-friendly experience. Emphasis is placed on secure communication protocols to safeguard user data, while a modular architecture ensures scalability for future enhancements. This solution demonstrates the potential of IoT technologies to create energy-efficient, secure, and accessible smart home systems, addressing modern demands for sustainable living.

关键词： Temperature sensors Temperature measurement Automation Smart homes Speech recognition Energy efficiency Sensor systems Real-time systems Intelligent sensors Wireless fidelity

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Comparative Analysis of Fault-Localization Techniques in Adder

Comparative Analysis of Fault-Localization Techniques in Add...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Muhammad Ali Akbar Jeong-A Lee Amine Bermak Division of Information and Computing Technology College of Sci. and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Engineering Computer System Lab Chosun University Gawangju Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Adder is a complex digital circuit because of its interconnectivity, which hinders fault detection and localization. This paper compares two approaches of fault localization in adders based on carry-free addition using signed digits (SD) representation and localized self-checking-full adders for ripple carry adders (RCA). The self-repairing SD adder approach requires computation with standard and shifted inputs toward left and right for fault localization. The resulting complexity caused by the shifting operation is unnecessary for the self-repairing RCA, which simultaneously achieves fault detection and localization. Moreover, the centralized checking mechanism of the SD adder results in system failure if the checker becomes faulty. However, in the case of self-repairing RCA, the failure of individual full adders will not create problems in the self-checking ability of other full adders owing to the distributed fault detection mechanism. It has been observed that the self-checking RCA implemented in FPGA is $\mathbf{6 2 \%}$ more area efficient than the self-checking signed digit adder in terms of LUTs.

关键词： Location awareness Wireless communication Fault detection Integrated circuit interconnections Table lookup Complexity theory Circuit faults

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

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