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SSRN 2023年

作者： Mkhutazi, Mditshwa Almaktoof, Ali Mfoumboulou, Yohan Darcy Kahn, Mohamed Tariq Dep. of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology South Africa

This survey examines the current state of energy efficiency and management in South Africa, focusing on the country’s key challenges and opportunities. The study highlights the importance of energy efficiency in reducing energy consumption and mitigating the impacts of energy use on the environment. It provides an overview of the current energy efficiency policies and initiatives in South Africa. The survey also considers the barriers to implementing energy efficiency policies in South Africa, including a lack of awareness, resistance to change, and political challenges. The study concludes by identifying critical areas for future research and action in the field of energy efficiency and management in South Africa. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Enhancements to the 5G-NR 2-Step RACH: an Unsourced Multiple Access Perspective

Enhancements to the 5G-NR 2-Step RACH: an Unsourced Multiple...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Agostini, Patrick Chamberland, Jean-Francois Clazzer, Federico Dommel, Johannes Liva, Gianluigi Munari, Andrea Narayanan, Krishna Polyanskiy, Yury Stanczak, Slawomir Utkovski, Zoran Fraunhofer Heinrich-Hertz-Institut Berlin Germany Texas A&M University College Station Dep. of Electrical and Computer Engineering TX United States Wessling Germany Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA United States

ISBN: (纸本)9798331507428

The ever increasing demand for massive connectivity, combined with the specific characteristics of machinegenerated wireless traffic, is pushing the existing solutions for device registration and data transfer beyond their operational limits. In this paper, we target specifically the 3GPP 5G New Radio random access channel, proposing some enhancements inspired by recent research results in the field of unsourced multiple access (UMAC). Aiming to enable massive grant-free connectivity for large machine-type and Internet of things terminals, we focus on modifications of the two-step random access procedures originally introduced in 3GPP Release 16. By means of simulations, we show that remarkable gains in both energy and spectral efficiency can be attained while retaining a strong resemblance to the legacy protocol. © 2024 IEEE.

关键词： 5G mobile communication systems

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Implementation and Design of Physical/Digital Switches Modified from Sonoff Product Based on Internet of Things 4

Implementation and Design of Physical/Digital Switches Modif...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chihcheng Li, Chiyang Wang, Han-Ching National Chi Nan University Dep. of Electrical Engineering Nantou County Taiwan National Quemoy University Dep. of Industrial Engineering and Management Kinmen County Taiwan Providence University Dep. of Computer Science and Communication Engineering Taichung City Taiwan National Central University Dep. of Information Management Taoyuan Country Taiwan College of Science and Technology National Chi Nan University Nantou County Taiwan Xinhe Elementary School Xindian District New Taipei City Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

While more home appliances are designed and manufactured, Sonoff serial home assistant alliances are designed and manufactured by eWeLink for many cheap and convenient devices controlled with exclusive APPs. Sonoff home assistant alliances are popular and well-known to attract many developers who can develop unofficial software to enhance the closed APPs designed by eWeLink and make flexible applications based on original hardware such as Tasmota and parameterized open-source APPs. We follow the above-mentioned developers to design a firmware that controls and is controlled based on message queue telemetry transport (MQTT) protocol from a cloud platform and the internet of thing (IoT) based architecture. The Sonoff home assistant alliances installed by the designed firmware can be controlled with one or more groups through a clouding platform by existing physical power-sockets from one-to-one controlling by eWeLink APPs. Under the proposed architecture, those Sonoff serial home assistant alliances are easily used like traditional power sockets for old-age users, who only use physical switches with touch-panel with MCU-based controllers to control power sockets. We designed digital switches on the website by browsers and physical touch panels as traditional switches work indep.ndently and coexist to meet most users with software-based APPs. The proposed architecture can be implemented and dep.oyed as a proof of concept (PoC) for the usability and validity. © 2022 IEEE.

关键词： Internet of things

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Implementation and Design of Water Flow Meter of Smart Home Integrated with Environment Monitor System 4

Implementation and Design of Water Flow Meter of Smart Home ...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chihcheng Ho, Liulin Wang, Han-Ching National Chi Nan University Dep. of Electrical Engineering Nantou County Taiwan National Quemoy University Dep. of Industrial Engineering and Management Kinmen County Taiwan Providence University Dep. of Computer Science and Communication Engineering Taichung City Taiwan National Central University Dep. of Information Management Taoyuan Country Taiwan College of Science and Technology National Chi Nan University Nantou County Taiwan Xinhe Elementary School Xindian District New Taipei City Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

More home appliances were designed and manufactured in the market, but fewer commercial products related to water-flow monitoring are sold in the current market. Water-related costs and expenses are cheaper than others, but water is one of the critical survival necessities for human beings. Thus, the home appliances or the smart home needs to pay attention to ubiquitous water resources as one of the limited natural earth resources. We follow the above-mentioned issues to achieve an internet of things (IoT) based architecture to design a flow-monitoring information collecting device (FMICD). Hall sensors were used to collect the water-flow consumption and to upload the information of the environment monitor system which was implemented in the previous research. In the proposed architecture, the FMICDs are designed and developed with the MCU-based controller binding to a water-flow meter (Model: YF-S201) equipped to monitor water consumption. We implemented FMICDs in the Xinhe Elementary School in the Xindian District of New Taipei City in Taiwan to validate the usability and feasibility of the study with a website (http://***:9999/***) to offer related users and staff to validate the usability in the study. © 2022 IEEE.

关键词： Internet of things

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda

Energy Access Assessment Report Focusing on Energy Poverty a...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Adimchinobi Asiegbu Ali Almaktoof Abdurazag Khalat Dep. of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Dep. of Electrical and Electronics Engineering Sabratha University Sabratha Libya

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today’s Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented.

关键词：

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Reinforcement Learning Based Joint Resource Allocation and User Fairness Optimization in mmWave-NOMA HetNets 31

Reinforcement Learning Based Joint Resource Allocation and U...

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31st International Conference on Electrical engineering, ICEE 2023

作者： Sobhi-Givi, Sima Nouri, Mahdi Shayesteh, Mahrokh G. Kalbkhani, Hashem Ding, Zhiguo Urmia University Electrical and Computer Engineering Department Urmia Iran Sharif University of Technology Elect. Eng. Dep. Iran Iran Tehran Iran Sharif University of Technology Wireless Research Lab Acri Elec. Eng. Dep. Tehran Iran Urmia University of Technology Faculty of Elec. Eng. Urmia Iran School of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9798350312560

In this paper, we propose heterogeneous network (HetNet) with mmWave and hybrid non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission, where uplink (UL) of macrocell users (MCUs) and downlink (DL) of small cell users (SCUs) share the same resource block (RB) to increase the network capacity. Also, an imperfect successive interference cancelation (I-SIC) decoding is considered due to hardware impairment of the real-world NOMA systems. We formulate the problem of joint power and resource block (RB) allocation to maximize rate-fairness and sum-rate by considering minimum quality of service (QoS) requirements. Reinforcement learning (RL) named Q-learning (QL) and deep Q-learning network (DQN) algorithms are employed to solve these problems. The results indicate that the proposed QL and DQN algorithms increase the fairness and sum-rate while low number of iterations is required. © 2023 IEEE.

关键词： Reinforcement learning

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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Deep Learning technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Bheemalingaiah, M. Srinivasan, R. Nathan, V. Vijey Kolluri, Raju Gupta, Amit J.B Institute of Engineering and Technology Department of CSE Telangana Hyderabad India School of Computer Science and Engineering Galgotia University Department of CSE U.P India SRM TRP Engineering College Department of CSE Tamilnadu Tiruchirappalli India Narasaraopet Engineering College Department of ECE Andhra Pradesh Narasaraopet India J.B Institute of Engineering and Technology Dep. of AI & ML Telangana Hyderabad India

ISBN: (纸本)9798350378092

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

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Biomedical engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

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From Manual to Semi-Automated Safety and Security Requirements engineering: Ensuring Compliance in Industry 4.0

From Manual to Semi-Automated Safety and Security Requiremen...

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Annual Conference of Industrial Electronics Society

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter Institute of Computer Engineering TU Wien Vienna Austria Dep. of Integrated Sensor Systems Institute of Computer Technology TU Wien Danube University Krems Austria

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In response to the growing need for compliance with diverse safety and security regulations, crucial for safeguarding both humans and machines in the era of Industry 4.0 (I4.0), it has also become increasingly crucial for industries to ensure adherence to safety and security standards. However, navigating the complexities of regulations and standards can be daunting, often leading to inefficiencies in compliance processes. Our goal is to foster a comprehensive understanding of an effective approach to meeting compliance requirements in these related fields. Therefore, this paper introduces a semi-automated method for ensuring compliance with unified safety and security standards within the context of I4.0. This approach empowers management teams to thoroughly analyze the specific requirements necessary to maintain a protected environment. Compliance can be achieved at both the asset component and system levels, aligning with the relevant standards. Additionally, we present a detailed analysis of a use case and its output, demonstrating the practical application and efficacy of our approach.

关键词： Industries Accuracy Standards organizations Manuals Regulation Safety Fourth Industrial Revolution Complexity theory Security Certification

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