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Mediterranean Electrotechnical Conference (MELECON)

作者： Nila Bagheri Emanuel Teixeira Fernando J. Velez Jon M. Peha Faculdade de Engenharia Instituto de Telecomunicações – DEM Universidade da Beira Interior Covilhã Portugal Dept. of Electrical and Computer Engineering Dept. of Engineering & Public Policy Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Extended reality (XR) is bridging the gap between virtual and real-world interactions enabling users to interact in realistic virtual worlds, removing physical obstacles, and establishing shared areas that promote greater comprehension and teamwork. The growing demand for high-frequency 5G communication systems supporting these new applications motivates the need of compact and efficient antennas capable of operating at millimeter-wave frequency bands. This work explores how the use of photonic crystal (PhC) leverages the properties of a multi-band antenna operating within the 27.0 GHz and 41.49 GHz resonant bands. The High-Frequency Structure Simulator (HFSS) software is utilized in this paper to outline a comprehensive design and modeling approach for the proposed microstrip patch *** design process involves optimizing the geometry and periodicity of the PhC structure to obtain resonant modes at the desired frequency bands by exploiting its bandgap properties, whilst enabling high quality resonances within the targeted frequency bands. The electromagnetic simulations and numerical analysis results demonstrate that the designed multi-band PhC-based antenna achieves a gain of 9.57 dBi. The resonant modes exhibit high quality factors, resulting in improved radiation efficiency. The proposed PhC-based antenna compact size, high gain, and multiple resonant bands make it suitable for a wide range of applications, including next-generation wireless communication systems supporting XR, radar systems, or satellite communications in the upper frequency bands.

关键词： Solid modeling Extended reality Metaverse Resonant frequency Bandwidth Millimeter wave radar Radar antennas

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Peer-to-Peer (P2P) Electricity Markets for Low Voltage Networks

arXiv

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arXiv 2024年

作者： Fernandes, Diana Vieira Christin, Nicolas Kar, Soummya Dept. of Engineering & Public Policy Carnegie Mellon University United States IN+/LARSyS Instituto Superior Técnico Universidade de Lisboa Portugal Dept. of Electrical & Computer Engineering Carnegie Mellon University United States

We develop a clearance and settlement model for Peer-to-Peer (P2P) energy trading in low-voltage networks. The model enables direct transactions between parties within an open and distributed system and integrates unused capacity while respecting network constraints. We evaluate the model through simulations of different scenarios (normal operating conditions and extreme conditions) for 24-hour time blocks. Our simulations highlight the benefits of our model in a decentralized energy system, notably its ability to deal with high-trade *** Codes 49 © 2024, CC BY.

关键词： Power markets

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BEYOND ALGORITHMIC FAIRNESS: A GUIDE TO DEVELOP AND DEPLOY ETHICAL AI-ENABLED DECISION-SUPPORT TOOLS

arXiv

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arXiv 2024年

作者： Gonzalez, Rosemarie Santa Piansky, Ryan Bae, Sue M. Biddle, Justin Molzahn, Daniel H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Public Policy Georgia Institute of Technology United States

The integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. Due to the networked nature of many engineered systems, ethically deploying methodologies at this intersection poses challenges that are distinct from other AI settings, thus motivating the development of ethical guidelines tailored to AI-enabled optimization. This paper highlights the need to go beyond fairness-driven algorithms to systematically address ethical decisions spanning the stages of modeling, data curation, results analysis, and implementation of optimization-based decision support tools. Accordingly, this paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization via case studies in power systems as well as supply chain and logistics. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers and encourage consideration of ethical implications at every step of the decision-making process. Copyright © 2024, The Authors. All rights reserved.

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Research on the Application Risk of computer Network Security Technology

Research on the Application Risk of Computer Network Securit...

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2023 International Conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Guo, Bingqian Zhao, Wei Chen, Peicheng Zhang, Changsheng Sun, Zhaodong Feng, Wenbin Southwest Jiaotong University SWJTU-Leeds Joint School Chengdu China Huazhong Agricultural University School of Public Administration Wulanchabu China Xi'an Jiaotong University School of Energy and Power Engineering Xi'an China Yan'an University School of Petroleum Engineering and Environmental Engineering Yan'an China Qufu Normal University School of Physics and Physical Engineering Qufu China Nanfang College of Guangzhou School of Electrical and Computer Engineering Guangzhou China

ISBN: (纸本)9798350313314

In the new era, computer network security issues have attracted more and more attention. How to use the latest technology to strengthen the network operating environment and prevent data information leakage has become the focus of network security management. This paper studies the computer network security technology in the era of big data. First, it is proposed that network data has the characteristics of high security factor, fast computing efficiency, and resource sharing. Secondly, it analyzes the security risks of computer networks to clarify the existing virus intrusion risks and network security. Vulnerabilities and operating system risks are different types. Finally, it focuses on the realization path of computer network security technology, and introduces the application points of firewall technology, data encryption technology and PKI technology. © 2023 IEEE.

关键词： Big data

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Nexus Cognizant Pricing of Workplace Electric Vehicle Charging

Nexus Cognizant Pricing of Workplace Electric Vehicle Chargi...

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American Control Conference (ACC)

作者： Minghao Mou Sean Qian Junjie Qin Elmore Family School of Elec-trical and Computer Engineering Purdue University Department of Civil and Environment Engineering Heinz College of Information Systems and Public Policy Carnegie Mellon University

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies the problem of designing work-place electric vehicle (EV) charging tariffs (i.e., fee schedules) while considering their impact to the transportation-electricity nexus. In particular, we consider the morning commute problem where a collection of commuters who drive EV to work must go through a common traffic bottleneck. Individual commuters determine when to leave for work and whether to charge their EV at work by optimizing a payoff function accounting for their generalized travel cost and payoff from charging. As an arrival time dependent charging tariff can directly impact the commuter decisions at the user equilibrium, we tackle the problem of designing tariffs that optimize (a) only the transportation component of the social cost, (b) only the electricity component of the social cost, and (c) the total social cost for the coupled transportation and electricity system. Tariffs incentivizing user equilibria that achieve the same performance as centralized social cost minimization are derived for the first two settings. For the last setting, we establish a tight condition under which it is possible to decentralize social optimal decisions via tariffs, and design optimal and suboptimal tariffs when the condition holds and fails, respectively.

关键词： Schedules Costs Electricity Employment Tariffs Transportation Pricing

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Real-World Community-in-the-Loop Smart Video Surveillance - A Case Study at a Community College

arXiv

引用

arXiv 2023年

作者： Yao, Shanle Pazho, Armin Danesh Ardabili, Babak Rahimi Noghre, Ghazal Alinezhad Neff, Christopher Tabkhi, Hamed Electrical and Computer Engineering Department The University of North Carolina at Charlotte Charlotte United States Public Policy The University of North Carolina at Charlotte Charlotte United States

Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging. This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camera-based system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediately. The paper highlights and addresses different algorithmic and system design challenges to guarantee real-time high-accuracy video analytics processing in the testbed. It also presents an example of cloud system infrastructure and a mobile application for real-time notification to keep students, faculty/staff, and responsible security personnel in the loop. At the same time, it covers the design decision to maintain communities’ privacy and ethical requirements as well as hardware configuration and setups. We evaluate the system’s performance using throughput and end-to-end latency. The experiment results show that, on average, our system’s end-to-end latency to notify the end users in case of detecting suspicious objects is 5.3, 5.78, and 11.11 seconds when running 1, 4, and 8 cameras, respectively. On the other hand, in case of detecting anomalous behaviors, the system could notify the end users with 7.3, 7.63, and 20.78 seconds average latency. These results demonstrate that the system effectively detects and notifies abnormal behaviors and suspicious objects to the end users within a reasonable period. The system can run eight cameras simultaneously at a 32.41 Frame Per Second (FPS) rate. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Arowolo, Micheal Olaolu Oluwatobiloba Ajayi, Williams Olawoye, Prisca Babajide, Oluwatomilola Aigbogun, Happiness Eric Damola Salawu, Mukhtar Adebiyi, Marion Olubunmi Adebiyi, Ayodele University of Missouri Department of Electrical Engineering and Computer Science Columbia65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria Western Illinois University Department of Public Health 61455 United States Stowers Institute for Medical Research Kansas CityMO United States Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350358155

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses. © 2024 IEEE.

关键词： Support vector machines

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Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression

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Bioelectronic Medicine 2024年第1期10卷 1-14页

作者： Albizu, Alejandro Indahlastari, Aprinda Suen, Paulo Huang, Ziqian Waner, Jori L. Stolte, Skylar E. Fang, Ruogu Brunoni, Andre R. Woods, Adam J. Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida Gainesville United States Department of Neuroscience College of Medicine University of Florida Gainesville United States J. Crayton Pruitt Family Department of Biomedical Engineering Herbert Wertheim College of Engineering University of Florida Gainesville United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida Gainesville United States Department of Clinical and Health Psychology College of Public Health and Health Professions University of Florida PO Box 100165 1225 Center Drive GainesvilleFL32610-0165 United States Faculdade de Medicina da Universidade de São Paulo São Paulo Brazil

Background/Objectives: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation intervention that shows promise as a potential treatment for depression. However, the clinical efficacy of tDCS varies, possibly due to individual differences in head anatomy affecting tDCS dosage. While functional changes in brain activity are more commonly reported in major depressive disorder (MDD), some studies suggest that subtle macroscopic structural differences, such as cortical thickness or brain volume reductions, may occur in MDD and could influence tDCS electric field (E-field) distributions. Therefore, accounting for individual anatomical differences may provide a pathway to optimize functional gains in MDD by formulating personalized tDCS dosage. Methods: To address the dosing variability of tDCS, we examined a subsample of sixteen active-tDCS participants’ data from the larger ELECT clinical trial (NCT01894815). With this dataset, individualized neuroimaging-derived computational models of tDCS current were generated for (1) classifying treatment response, (2) elucidating essential stimulation features associated with treatment response, and (3) computing a personalized dose of tDCS to maximize the likelihood of treatment response in MDD. Results: In the ELECT trial, tDCS was superior to placebo (3.2 points [95% CI, 0.7 to 5.5;P = 0.01]). Our algorithm achieved over 90% overall accuracy in classifying treatment responders from the active-tDCS group (AUC = 0.90, F1 = 0.92, MCC = 0.79). Computed precision doses also achieved an average response likelihood of 99.981% and decreased dosing variability by 91.9%. Conclusion: These findings support our previously developed precision-dosing method for a new application in psychiatry by optimizing the statistical likelihood of tDCS treatment response in MDD. © The Author(s) 2024.

关键词： Noninvasive medical procedures

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Detecting Reentrancy Vulnerability in Smart Contracts using Graph Convolution Networks

Detecting Reentrancy Vulnerability in Smart Contracts using ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Hozefa Lakadawala Komla Dzigbede Yu Chen Dept. of Electrical & Computer Engineering Binghamton University Binghamton NY Department of Public Administration Binghamton University Binghamton NY

Because of many advanced features, Decentralized Finance (DeFi) has become a hot topic in the past decade. As an application of blockchain technology, DeFi allows people to trade cryptocurrencies and other financial products more efficiently, securely, and privately. Specifically, using smart contracts further improves transaction rate and the quality of user experience (QoE) by defining the business logic via code. However, if smart contracts are not audited before compiling on an immutable blockchain, it may result in losses of millions. Therefore, there is a compelling need for efficient and effective measures to ensure the robustness and genuineness of smart contracts. In this paper, we propose a Homogeneous Graph Machine Learning Algorithm for Reentrancy attacks DEtection using Graph Convolution Networks (HARDEN). Reentrancy attacks are one of the most infamous vulnerabilities in smart contracts. The experimental results are encouraging and validate the feasibility of applying a Deep Learning (DL) approach to detect vulnerabilities in smart contracts. We hope this preliminary study will inspire more interest and discussions in the raising area.

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Machine Learning and Deep Learning Approach for Predicting Neonatal Mortality in Indonesia

Machine Learning and Deep Learning Approach for Predicting N...

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International Conference on Applied System Innovation (ICASI)

作者： Nur Hamid Fidya Rumiati Rizal Dwi Prayogo Hidetaka Nambo Division of Electrical Engineering and Computer Science Kanazawa University Japan Department of Epidemiology Faculty of Public Health Universitas Indonesia Indonesia

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and CNN. We found that low birth weight and early breastfeeding becomes the most significant features related to neonatal mortality in Indonesia. For prediction task, implementing feature importance task as feature selection can improve prediction performance and reduce algorithm complexity. LSTM and CNN achieved the best prediction model with 90.91% accuracy.

关键词： Deep learning Pediatrics Technological innovation Predictive models Feature extraction Prediction algorithms Complexity theory

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