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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Kollias, Konstantinos-Filippos Silva, Luis Miguel Maia Marques Torres E. Sarigiannidis, Panagiotis Syriopoulou-Delli, Christine K. Fragulis, George F. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Porto Faculty of Engineering Porto Portugal University of Macedonia Department of Educational and Social Policy Thessaloniki Greece

ISBN: (纸本)9798350321074

Early Autism Spectrum Disorder (ASD) diagnosis and intervention with state-of-the-art instruments, such as Artificial Intelligence and Robots is a rather challenging and promising research area which can optimise outcomes regarding the quality of life of ASD individuals and their families. Autistic individuals tend to show, among others, deficits in emotion recognition and expression. The present review study, aiming to review the literature concerning the contribution of Robotics to ASD research, found 27 review studies conducted from 2016 to February 2023 on PubMed and Web of Science databases in which robots were either employed in ASD diagnosis, or emotion recognition and expression. Results pointed out beneficial effects of robots' employment as far as ASD diagnosis and emotion recognition and expression are concerned. Limitations and recommendations for future research are also involved, i.e., studies combining the Internet of Things (IoT) and Robotics with an aim to early ASD diagnosis, assessment, intervention, and inclusion. © 2023 IEEE.

关键词： Emotion Recognition

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Emergency Load Shedding to Enhance Resiliency Considering Operator and Customer Equity

Emergency Load Shedding to Enhance Resiliency Considering Op...

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2024 IEEE Green Technologies Conference, GreenTech 2024

作者： Bataduvaarachchi, Nirasha Alexander, Demy Aravinthan, Visvakumar Tamimi, Al Wichita State University Department of Electrical and Computer Engineering WichitaKS United States Transmission Planning & Policy Sunflower Electric Corporation WichitaKS United States

ISBN: (纸本)9798350308587

Recent high impact low frequency events have caused significant concerns on reliability and resiliency of electrical grid. Transmission operators might enforce load curtailment during such events to preserve the integrity of the power network and to prevent catastrophic system failures and long-lasting outages. Although load-shedding procedures employed by system operators are reviewed and constraints are highlighted throughout most hazardous incidents, such mechanisms are not evaluated to ensure consumer fairness and system impacts are addressed. This study was conducted at the transmission level in order to propose a new curtailment algorithm that addresses the limitations of the load shedding mechanism of the Southwest Power Pool. The study was motivated by the North American winter storm occurred in February 2021. The performance of the proposed algorithm is tested and compared with Southwest Power Pool's curtailment model via IEEE RTS-96 test system. © 2024 IEEE.

关键词： Electric power transmission

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Enhanced Multiclass Brain Tumor Detection in MRI Using Similarity-Navigated Graph Neural Networks with Banyan Tree Growth Optimization 4

Enhanced Multiclass Brain Tumor Detection in MRI Using Simil...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu Rao, K. Singh, Pallavi Verma, Gargi Shankar Kannan, L. Mohana Kadam, Swaraj Mangalore Institute of Technology & Engineering Department of Computer Science and Engineering Karnataka Mijar India Saraswati Dental College & Hospital Department of Public Health Dentistry Uttar Pradesh Lucknow India Krishna's Vikash Institute of Technology Department of Computer Science and Engineering Chhattisgarh Raipur India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India Dr. D Y Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune India

ISBN: (纸本)9798331529635

The diagnosis of brain tumors is admittedly difficult because of their varied and complicated nature. Therefore, one has to get the right diagnosis and categorization for it to be treated well. This is a situation where identification of multiple classes such as;meningioma, pituitary, glioma and no tumor is crucial in taking the right course of action. Therefore, a new deep learning architecture is suggested in this work to increase the specificity between different types of tumors by detecting brain tumors with high accuracy in MRI scans. The proposed methodology starts with the Cumulative Curve Fitting Approximation (CCFA) preprocessing algorithm to improve the quality of MRI images. Preprocessed images are used as inputs for feature extraction based on geometric algebra transformers. These transformers include geometric relationships within the data, ensuring more distinctive selection. The categorization is further carried out with assistance of a Similarity-Navigated Graph Neural Network (SNGNN) incorporating the Banyan Tree Growth Optimizer (BTGO) model, which is constructed for efficient detection of the subtlest patterns and their connection between classes of brain tumors. It integrates very well in terms of the accuracy shown by its performance metrics. It shows effectiveness in a multiclass scenario of detecting brain tumors, which helps in the precise and early diagnosis and even tailors proper treatment plans. © 2024 IEEE.

关键词： Graph neural networks

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Two-Electrode Screen-Printed pH Sensors for Monitoring Soil and Other Growing Media

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IEEE Sensors Journal 2025年第11期25卷 18692-18701页

作者： Crichton, Catherine A. Lahann, Lucas Barba, Juan Pablo Cisneros Yuan, Titan Strand, Elliot J. Bruno, Nicholas Goodrich, Payton J. Baumbauer, Carol L. Atreya, Madhur Bihar, Eloise Silver, Whendee L. Pister, Kristofer Arias, Ana Claudia Whiting, Gregory L. University of California Berkeley Department of Electrical Engineering and Computer Science 253 Cory Hall BerkeleyCA94720 United States University of California Berkeley Department of Environmental Science Policy and Management 130 Mulford Hall BerkeleyCA94720 United States University of Colorado Boulder Paul M. Rady Department of Mechanical Engineering 1111 Engineering Drive BoulderCO80309 United States University of Colorado Boulder Materials Science and Engineering 4001 Discovery Drive BoulderCO80303 United States

Monitoring soil pH is important for optimizing plant biomass production, understanding nutrient cycling, predicting and mitigating greenhouse gas emissions, and improving soil health. Soil pH can vary significantly over time and space, and high spatiotemporal resolution data are needed to enable informed land management decisions. A potential method to address this need is to use large numbers of easily distributed pH sensor systems which can be placed at high density in soils. An approach for fabricating such sensors is described here using a voltammetric screen-printed pH sensor with a working electrode that incorporates Alizarin (a pH-sensitive dye) throughout its bulk. The use of a Nafion membrane on top of the working electrode and a salt membrane over the reference electrode provides near-Nernstian sensitivity and enhanced device stability in buffered media, soil, and hydroponic fluid. Critically, these devices can function effectively in a novel 2-electrode (working and reference) configuration with a sensitivity of 56.38 mV/pH and a resolution of 0.28 pH (n=3), enabling the use of simple, low-cost readout electronics for evaluating the sensor state. These results show that a printed pH sensor paired with affordable electronics can be used to monitor the pH of unbuffered media, providing a potential path to high-spatial density real-time measurement of pH in soils and hydroponics. © 2001-2012 IEEE.

关键词： Alizarin

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Market-Based Environmental Policies in the Power Sector: State of the Art and Future Research Needs

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Current Sustainable/Renewable Energy Reports 2023年第3期10卷 59-74页

作者： Siddiqui, Afzal S. Tanaka, Makoto Chen, Yihsu Department of Computer and Systems Sciences Stockholm University Kista Sweden Department of Mathematics and Systems Analysis Aalto University Espoo Finland National Graduate Institute for Policy Studies Tokyo Japan Department of Electrical and Computer Engineering and Environmental Studies Department University of California Santa CruzCA United States

Purpose of Review: So-called Green New Deals aim to decarbonise the power sector via market-based instruments. Consequently, engineering-economic models for analysing the sustainable-energy transition have proliferated. This review categorises existing approaches and identifies areas for extending the state of the art. Recent Findings: We cluster the extant literature into two groups: engineering/operational research (M1) and environmental economics (M2). While M1 focuses on the power sector’s technical and spatio-temporal aspects, M2’s emphasis is on incentives and externalities. Depending on the nature of the research question, either perspective (or both) may be suitable. Summary: Since the envisaged electrification of the wider economy implies tighter coupling between formerly distinct sectors, e.g., power and heat, both M1 and M2 will have to adapt to the new paradigm in terms of methodology and application areas. Here, eliciting coordinating mechanisms, enhancing algorithms for solving hierarchical models, soft linking bottom-up and top-down models, and crafting robust environmental policy in face of uncertainty over externalities are some of the vistas for future research. © 2023, The Author(s).

关键词： Game theory

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An Ensemble Deep Learning Approach for Accurate Urinary Sediment Detection Using YOLOv9e and KD-YOLOX-ViT

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IEEE Access 2025年 13卷 101934-101954页

作者： Mansura Naznine Abdus Salam Muhammad Mohsin Khan Sadia Farhana Nobi Muhammad E. H. Chowdhury Department of Electrical Engineering Qatar University Doha Qatar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Neurology Hamad Medical Corporation Doha Qatar Department of Public Health and Informatics BSMMU Dhaka Bangladesh

Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling

关键词： Sediments Accuracy Adaptation models Kidney Deep learning Microscopy White blood cells Medical diagnostic imaging Feature extraction Bacteria

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Flow Table Modification Using Behavioral-Based Fingerprinting Technique to Facilitate Zero Trust Identity Management and Access Control 15

Flow Table Modification Using Behavioral-Based Fingerprintin...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Morris, Vinton Kornegay, Kevin Cybersecurity Assurance and Policy Center Morgan State University BaltimoreMD21251 United States Morgan State University Department of Electrical and Computer Engineering BaltimoreMD21251 United States

ISBN: (纸本)9798331507695

Traditional security mechanisms and architectures are falling short, impacting both organizations and end users alike. The rapid growth and variety of Internet of Things (IoT) devices have significantly expanded the attack surface and increased network vulnerabilities. In recent years, Zero Trust (ZT) has gained traction in its attempt to solve the inherent flaws of the traditional perimeter-centric security architecture. While there have been great strides in supporting users, applications, and general-purpose computing devices (GPD), current ZT architectures lack the necessary capabilities to support IoT devices. In this paper, we present a method of authentication that profiles the network traffic and builds unique fingerprints for IoT devices. The fingerprints are a representation of the behavior of the IoT devices. We then compare the on-demand generated fingerprints of a device attempting to communicate to those in the database and update the flow tables of a software-defined networking (SDN) switch, accordingly ensuring that only devices with matching profiles can communicate on the network. We argue that the predictive nature of IoT device traffic and their limited protocol set make them a candidate for this method of authentication and access control that can satisfies the Zero Trust Architecture's identity management component. © 2025 IEEE.

关键词： Authentication

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Deep Neural Network Ensembles for the Detection of Alzheimer's Disease Using Imbalanced Clock Drawing Test Images 15

Deep Neural Network Ensembles for the Detection of Alzheimer...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Danda, Sai Santosh Reddy Uysal, Alper Kursat Qin, Tian Kumar, Anu Sannareddy, Varshitha Hu, Mengyao Gonzalez, Richard Murphey, Yi Lu University of Michigan-Dearborn Department of Electrical & Computer Engineering Dearborn United States Alanya Alaaddin Keykubat University Department of Computer Engineering Alanya Turkey School of Public Health The University of Texas Health Science Center at Houston Houston United States University of Michigan Department of Psychology and Statistics Ann ArborMI United States

ISBN: (纸本)9798331507695

This paper explores innovative machine learning technologies for the detection of Alzheimer's Disease (AD) from large and imbalanced clock drawing test (CDT) images. We present a deep ensemble learning framework, 2Level MM-MD, which includes two-level ensemble of MultiModel deep neural networks (DNN) trained on MultiData blocks. The data blocks are generated by applying partition based and bootstrap statistical data resampling methods to the large but imbalanced CDT image data. To produce a more robust system that is better adapted to imbalanced CDT data, the 2Level MM-MD ensemble framework attempts to leverages both 1) the richness of large data blocks generated using effective data re-sampling schemes, and 2) multiple deep neural network systems that are trained on the resembled data blocks through transfer learning to produce a more robust system that is better adapted to more generalized CDT image domains. Three deep neural network models (EfficientNet, ResNet101, and ViT), along with six different sampling methods are used to generate re-balanced data blocks, and five different decision schemes are implemented and evaluated. The ensemble systems are trained and evaluated using a large CDT image dataset from Rounds 1 to 9 of the National Health and Aging Trends Study (NHATS), which contains over 47,723 CDT images, where 92% are class 0 (no dementia) and 8% are class 1 (probable dementia). The top classification system is a 2Level MM-MD system E5 with a two-level decision rule SoM. The system generated 0.67 Recall and 0.35 F1-Score on the test data. This represents a 123% improvement in Recall and a 25% improvement in F1-Score compared to the best baseline system, which is a classifier trained using EfficientNet with the training data balanced by oversampling the minority class. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation 30

Dynamic Reward Adjustment in Multi-Reward Reinforcement Lear...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Min, Do June Pérez-Rosas, Verónica Resnicow, Kenneth Mihalcea, Rada Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States School of Public Health University of Michigan Ann ArborMI United States

ISBN: (纸本)9782493814104

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text properties for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DYNAOPT and C-DYNAOPT, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DYNAOPT and C-DYNAOPT, outperform existing naive and bandit baselines, demonstrating their potential for enhancing language models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Reinforcement learning

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Dual-Band Slot Array Antenna for W-Band Application Based on Gap Waveguide

Dual-Band Slot Array Antenna for W-Band Application Based on...

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European Conference on Antennas and Propagation, EuCAP

作者： Mu Fang A Uz Zaman J. Yang D. Santiago I. Arregui Miguel A. G. Laso Ahmed A. Kishk Department of Electrical Engineering Chalmers University of Technology Sweden Department of Electrical and Communications Engineering Public University of Navarre Spain Department of Electrical and Computer Engineering Concordia University Canada

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

A co-aperture dual-band linearly polarized slot array utilizing ridge gap waveguide (RGW) technology has been developed to operate within the 74 – 78 GHz and 102 – 106 GHz frequency ranges. The subarray features two sets of longitudinal slots positioned on the broad wall of the single waveguide channel, allowing for dual-band radiation through a planar aperture. Furthermore, two bandpass filters (BPFs) are placed on either side of the subarray, enabling independent feeding and filtering characteristics for the dual bands. A subarray column of several slot elements has been designed and simulated to demonstrate the dual-band concept. The subarray can produce high-gain beams when expanded into a planar array configuration.

关键词： Band-pass filters Wireless communication Slot antennas Filtering Dual band Gap waveguide Planar arrays Aperture antennas Planar waveguides Linear antenna arrays

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