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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Juarez Garcia, Ezequiel Stephens, Chad L. Napoli, Nicholas J. Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Crew Systems & Aviation Operations Branch and System-Wide Safety Project NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separ ability of class distributions. Such challenges result in unrepresentative training data, complex skewedor warped decision boundaries, and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions with in the feature space through partitioning. This enables the optimization of local models with inthe regions to overcome the impact of un observ ability and complex decision boundaries indifferent feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of un observability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobserv ability in predictive mo

关键词： Information fusion

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Improved High-Speed Thermal Analysis Based on Two-Step Simulation for High-Frequency Transformers 14

Improved High-Speed Thermal Analysis Based on Two-Step Simul...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Yi, Zheyuan Sun, Kai Li, Qiang Liu, Zengyang Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China University Blacksburg Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State BlacksburgVA United States

ISBN: (纸本)9798331516116

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process. © 2025 IEEE.

关键词： Thermal modeling

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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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Smart Safety system for Driver and Passenger Protection

Smart Safety System for Driver and Passenger Protection

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sudharsan S Rajesh S Selvam N Sangeeth Raj P S Babu Prasad R Duraimurugan A Department of Electrical and Electronics Engineering M. Kumarasamy College of Engineering Karur Department of Computer Science and Business System M. Kumarasamy College of Engineering Karur

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The Smart Safety system is designed to enhance road safety by monitoring both the driver and passenger activities and ensuring proactive measures are taken to prevent accidents. The system leverages sensors and real-time data monitoring to ensure driver sobriety and attentiveness while also alerting the driver about potential passenger hazards. The system primarily focuses on two areas: driver safety and passenger detection. On the driver’s side, an alcohol sensor detects any sign of intoxication, and an eye-blink sensor monitors the driver’s focus, especially if distracted by using a phone. If intoxication is detected, the vehicle is automatically disabled through the motor control system, and an alert is triggered via an alarm and notification sent to a web-based platform. If the driver is distracted or drowsy, a warning alarm is activated. On the passenger side, an IR sensor and an ultrasonic sensor detect whether passengers are improperly standing or not seated, and a notification is displayed to the driver. The system integrates an Arduino Uno with several sensors, including alcohol, eye-blink, IR, and ultrasonic sensors. The collected data is transmitted wirelessly through a Wi-Fi module to a cloud platform, such as ThingSpeak, and then displayed on a website built with Java, HTML, and JavaScript. The website provides a login interface to enroll driver information and monitor alcohol sensor readings. In case of driver intoxication, the system triggers the suspension of the vehicle by turning off the motor. This smart safety system provides an efficient, real-time mechanism for preventing road accidents by monitoring driver behavior and ensuring passenger safety.

关键词： Cloud computing Motors Sensor systems Real-time systems Road safety Safety Protection Monitoring Intelligent sensors Vehicles

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Traffic Congestion Control with Emergency Awareness and Optimized Communication Infrastructure using Reinforcement Learning and Non-Dominated Sorting Genetic Algorithm

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IEEE Access 2025年

作者： Al-Heety, Othman S. Zakaria, Zahriladha Abu-Khadrah, Ahmed Ismail, Mahamod Shakir, Mohammed Mudhafar Alani, Sameer Alsariera, Hussein Faculty of Electronic and Computer Engineering Durian Tunggal Malaysia Al-Balqa Applied University College of Engineering Technology Department of Electrical Engineering Amman Jordan Universiti Kebangsaan Malaysia UKM Bangi Department of Electrical Electronic and System Engineering Selangor Malaysia Faculty of Engineering and Electronic Engineering Batu Pahat Malaysia University of Anbar Computer Center Anbar5543 Iraq

Urban centers are grappling with increasing traffic congestion, which hampers mobility and reduces the effectiveness of emergency responses. Current traffic management systems struggle to dynamically prioritize emergency vehicles during peak congestion, highlighting the need for an advanced solution that improves both everyday traffic flow and emergency response efficiency. Traditional traffic light systems typically follow either fixed schedules ("Fixed"approach) or make real-time adjustments without achieving long-term efficiency ("Greedy"approach), often failing to effectively balance regular traffic dynamics with emergency needs. We propose the "Avg Log "method, an adaptive Q-learning strategy that enhances conventional traffic management systems by incorporating an emergency-aware module. This module uses real-time data to prioritize emergency vehicles and adjust traffic signals accordingly. Additionally, our approach employs a multi-objective optimization algorithm to strengthen communication infrastructure, optimizing latency, throughput, and reliability. Through simulations in a multi-intersection urban setting, we compared the performance of our method against the standard "Greedy"and "Fixed"approaches. Key metrics assessed included average waiting times, system throughput, and the efficiency of emergency vehicle passage. The "Avg Log"method outperformed traditional methods, showing superior long-term adaptability and learning. It demonstrated lower average waiting times and effectively managed emergency situations, significantly reducing delays for emergency vehicles compared to both control methods. This method provides an efficient balance between overall traffic flow and emergency prioritization, suggesting a scalable solution for complex urban environments. © 2025 IEEE.

关键词： Efficiency

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SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for Identifying AMD Grades

SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Niveen Nasr El-Den Mohamed Elsharkawy Mohammed Ghazal Ali Mahmoud Harpal Sandhu Hani Mahdi Ayman El-Baz Department of Computer and System Engineering Ain Shams University Egypt Department of Bioengineering University of Louisville Louisville KY USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate early diagnosis is crucial in addressing Age-related Macular Degeneration (AMD), a chronic retinal disease that is a leading cause of blindness among the elderly. Medical imaging, particularly fundus imaging, is essential in facilitating timely detection and intervention. Due to the variability in image sizes within our dataset, this paper introduces the SABiT-MNet model, which effectively discriminates between healthy retinas, dry AMD, and wet AMD. The model integrates a novel scale-adaptive (SA) approach by combining an autoencoder with Big Transfer (BiT) as its backbone. Unlike traditional resizing methods, which often result in the loss of critical diagnostic information, the SA model dynamically adjusts to varying image sizes, preserving key retinal features essential for accurate diagnosis. The primary aim of this architecture is to retain crucial details in fundus images to ensure precise classification. In this study, 648 subjects were recruited through the Comparisons of AMD Treatments Trials study group, sponsored by the University of Pennsylvania. Experimental results demonstrate that the proposed SABiT-MNet model outperforms state-of-the-art approaches, including transformer-based models, achieving superior diagnostic accuracy. The model recorded performance metrics of 94% accuracy, 97% sensitivity, and 93.94% specificity. To further validate the robustness of the system, we tested it on the public ODiR dataset, where it achieved similarly promising results, confirming the effectiveness of our approach.

关键词： Macular degeneration Accuracy Sensitivity Autoencoders Signal processing Retina Transformers Robustness Speech processing Older adults

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Proposed National Artificial Intelligence Strategy

Proposed National Artificial Intelligence Strategy

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Bilal Zahran Belal Ayyoub Department of computer science University of Petra Jordan Department of computer information system Al-Balqa Applied University Jordan Department of Electrical Engineering Al-Balqa Applied University Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid pace of development in Artificial Intelligence (AI) necessitates formulating effective national strategies for its maximization and minimizing its accompanying repercussions. In this article, a proposed National Artificial Intelligence Strategy for Jordan is formulated, with an orientation towards a systemic and organized national structure relevant to Jordan's socio-economic environment. What is new in this proposed National AI Strategy is its localized orientation, blending AI-facilitated economic development, governance, and ethics relevant to Jordan. It also formulates a governance model, with coordination between principal entities such as government, academia, and private sectors, for effective implementation. The proposed strategy sets specific digital transformation targets, in consonance with Jordan's Vision 2030 and National Strategy for Digital Transformation (2021–2025). AI use in critical sectors such as national security, energy, healthcare, and education takes a top position, with a view towards adhering to regulations, utilizing AI in an ethical manner, and developing technology in a sustainable manner. Outcomes include enhancing AI literacy, diversifying economies, enhancing employment in AI-related industries, and national competitiveness at a global level. This work forms part of an emerging portfolio of studies in national AI strategies and sets a country-specific model in consonance with Jordan's specific development aims, offering a model for AI strategy development in emerging economies.

关键词： Ethics Digital transformation Medical services Regulation Proposals Artificial intelligence Socioeconomics National security Portfolios Videos

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DENSE-Guided Deep Motion Networks Accounted by Large Rotations to Improve Myocardial Strain Analysis from Routine Cine MRI

DENSE-Guided Deep Motion Networks Accounted by Large Rotatio...

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IEEE International Symposium on Biomedical Imaging

作者： Pengcheng Lei Jiarui Xing Faming Fang Frederick H. Epstein Miaomiao Zhang Department of Electrical and Computer Engineering University of Virginia USA School of Computer Science and Technology East China Normal University Shanghai China Department of Biomedical Engineering University of Virginia Health System USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Myocardial strain imaging provides a valuable tool for detecting subclinical left ventricular (LV) dysfunction and adding prognostic value in assessing various types of heart disease. Recent studies have utilized highly accurate strain-dedicated techniques, such as displacement encoding with stimulated echoes (DENSE), to train a deep learning (DL) framework to predict the myocardial displacements/deformations from routine cine balanced steady state free precession (bSSFP) images. However, these methods have shown limited performance in capturing the large rotational motion of the myocardium associated with twist and torsion over time, which are important aspects of myocardial mechanics. To address this gap, this paper introduces a novel DENSE-guided DL network that explicitly accounts for large rotational motion to further improve strain analysis of standard cine bSSFP images. Specifically, our proposed network includes two key components: (i) a time-series rotation estimation network employing a 3D convolutional encoder-decoder architecture to model the large rotational dynamics of the LV myocardium over time, and (ii) a radial motion prediction network based on deformable image registration. The output of these two sub-networks was integrated and refined through a fusion network to predict the final myocardial displacements, supervised by DENSE ground truth. Experimental results show that our method improves the accuracy of myocardial strain with effectively captured large rotations.

关键词： Deep learning Solid modeling Accuracy Three-dimensional displays Magnetic resonance imaging Myocardium Predictive models Steady-state Standards Strain

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles 59

Generative AI-Enhanced Cooperative MEC of UAVs and Ground St...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

ISBN: (纸本)9798331513269

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Marching Page Walks: Batching and Concurrent Page Table Walks for Enhancing GPU Throughput

Marching Page Walks: Batching and Concurrent Page Table Walk...

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IEEE Symposium on High-Performance computer Architecture

作者： Jiwon Lee Gun Ko Myung Kuk Yoon Ipoom Jeong Yunho Oh Won Woo Ro Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Computer Science and Engineering Ewha Womans University Seoul Korea Department of System Semiconductor Engineering Yonsei University Seoul Korea School of Electrical Engineering Korea University Seoul Korea

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Virtual memory, with the support of address translation hardware, is a key technique in expanding programmability and memory management in GPUs. However, the nature of the GPU execution model heavily pressures its translation hardware, particularly due to a discrepancy in the behavior of page table walkers and thousands of concurrently running threads. In GPU workloads, multiple threads simultaneously access a number of pages necessitating a substantial number of translations whereas each walker handles only a single walk request at a time. Such a limitation significantly increases the queueing latency of walk requests, which we observe as a major bottleneck for servicing page table walks in GPUs. To tackle this challenge, we investigate a design of page walkers that facilitates multiple walk requests to be handled together in batches. Then, we make the following observations: 1) allowing a page walker to issue beyond a single memory request significantly improves the throughput of walkers, and 2) GPU applications tend to concurrently access pages in wide address ranges. By leveraging the above implications, we propose Marching Page Walks (MPW) that effectively mitigate the contention in GPU page table walkers. MPW scans pending walk requests to identify ones that can be grouped together. Then, MPW batches these requests and concurrently handles them by issuing multiple memory instructions. Experiments show that MPW reduces the queueing latency of page walks by 86.7% and improves GPU performance by 55.6% over the baseline design.

关键词： Translation Filters Instruction sets Memory management Graphics processing units Throughput Hardware Energy efficiency

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