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An Array of Silicon Dual Microneedle Electrodes Integrated With mini-LEDs for Electrophysiological Recording And Simultaneous Application of Electrical and Optical Stimuli to the Retina for Artificial Vision 38

An Array of Silicon Dual Microneedle Electrodes Integrated W...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chung, Seung-Han Kim, Chaesung Kim, Yong-Kweon Lee, Seung-Ki Park, Jae-Hyoung Im, Maesoon Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of College of Engineering Korea University School of Electrical Engineering Seoul Korea Republic of Dankook University Department of Electronics and Electrical Engineering Yongin Korea Republic of Seoul Korea Republic of Division of Bio-Medical Science and Technology Seoul Korea Republic of Kyung Hee University KHU-KIST Department of Converging Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798331508890

We report a novel hybrid neural interface device that not only enables simultaneous application of electrical and optical stimuli but also offers electrophysiological recording capability. A 6×6 silicon dual microneedle electrode array was fabricated and each pair consisted of recording and stimulating electrodes. A mini-LED matrix was also integrated underneath the electrode array to deliver optical stimulation. Spiking activities of retinal ganglion cells of an ex-vivo retina were successfully recorded in response to electrical and optical stimuli applied from the fabricated device. © 2025 IEEE.

关键词： Light emitting diodes

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Efficient Multiparty Entanglement Distribution with DODAG-X Protocol 5

Efficient Multiparty Entanglement Distribution with DODAG-X ...

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Quantum Computing, Communication, and Simulation V 2025

作者： Negrin, Roberto Dirnegger, Nicolas Munizzi, William Talukdar, Jugal Narang, Prineha Division of Physical Sciences College of Letters and Science University of California Los Angeles Los AngelesCA90095 United States Electrical and Computer Engineering Department University of California Los Angeles Los AngelesCA90095 United States

ISBN: (纸本)9781510685307

We introduce the DODAG-X protocol for multipartite entanglement distribution in quantum networks. Leveraging the power of Destination Oriented Directed Acyclic Graphs (DODAGs), our protocol optimizes resource consumption and enhances robustness to noise in dynamic and lossy networks. Implementing a variation on the X-protocol within the DODAG, we minimize graph verification and path-finding calculations, significantly reducing computational overhead when compared to other entanglement routing schemes. Additionally, our benchmarks on grid lattice and small-world topologies reveal substantial measurement reduction compared to existing protocols. We demonstrate the success of DODAG-X for generating maximal three-party entanglement in arbitrary networks, and describe the potential for scaling to generic n-party entanglement. The DODAG-X protocol provides a scalable and efficient solution for entanglement routing, advancing current techniques for reliable quantum communication and network applications. © 2025 SPIE.

关键词： Directed graphs

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Assessing aerosol effects on solar irradiance in the NEOM region with the WRF-solar model

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Bulletin of Atmospheric science and Technology 2025年第1期6卷 1-24页

作者： Choudhury, Devanil Computer Electrical Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Mecca Province Thuwal 23955 - 6900 Saudi Arabia National Centre for Medium Range Weather Forecasting A-50 Institutional Area Sector-62 Uttar Pradesh Noida 201309 India

This study demonstrates the usability of the Weather Research and Forecasting (WRF)-Solar model, in predicting solar irradiance in the NEOM region of Saudi Arabia across different sky conditions. This research aims to enhance the accuracy of solar radiation forecasting by systematically evaluating the model's responses to external time-varying two-dimensional (2-D) aerosol optical depth (AOD) data at 550 nm under scenarios with varying cloud cover and aerosol levels. Through experiments representing distinct sky conditions, this study demonstrates consistent trends, with the control experiment (CTRL) consistently producing the highest Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) due to lower representation of AOD in the model. Notably, experiments incorporating time-varying 2-D AOD from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis outperform other experiments with NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA- 2) analysis, and climatological AOD datasets, particularly on overcast days. Model validation against observations from the Tabuk University station further substantiates these findings. Furthermore, experimental results highlight the importance of dynamic aerosol data over the climatological or no-aerosol data in improving the solar energy predictions, emphasizing the need for accurate representation of aerosol-cloud-radiation interactions in numerical models for precise solar irradiance forecasting. © The Author(s) 2025.

关键词： Aerosol-cloud-radiation interaction AOD CAMS MERRA- 2 Solar irradiance WRF-Solar

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Every Angle Is Worth A Second Glance: Mining Kinematic Skeletal Structures from Multi-view Joint Cloud

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Jiang, Junkun Chen, Jie Au, Ho Yin Chen, Mingyuan Xue, Wei Guo, Yike Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong Hong Kong University of Science and Technology Division of Art and Machine Creativity Division of Emerging Interdisciplinary Areas Hong Kong Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong

Multi-person motion capture over sparse angular observations is a challenging problem under interference from both self- and mutual-occlusions. Existing works produce accurate 2D joint detection, however, when these are triangulated and lifted into 3D, available solutions all struggle in selecting the most accurate candidates and associating them to the correct joint type and target identity. As such, in order to fully utilize all accurate 2D joint location information, we propose to independently triangulate between all same-typed 2D joints from all camera views regardless of their target ID, forming the Joint Cloud. Joint Cloud consist of both valid joints lifted from the same joint type and target ID, as well as falsely constructed ones that are from different 2D sources. These redundant and inaccurate candidates are processed over the proposed Joint Cloud Selection and Aggregation Transformer (JCSAT) involving three cascaded encoders which deeply explore the trajectile, skeletal structural, and view-dependent correlations among all 3D point candidates in the cross-embedding space. An Optimal Token Attention Path (OTAP) module is proposed which subsequently selects and aggregates informative features from these redundant observations for the final prediction of human motion. To demonstrate the effectiveness of JCSAT, we build and publish a new multi-person motion capture dataset BUMocap-X with complex interactions and severe occlusions. Comprehensive experiments over the newly presented as well as benchmark datasets validate the effectiveness of the proposed framework, which outperforms all existing state-of-the-art methods, especially under challenging occlusion scenarios. © 1995-2012 IEEE.

关键词： Motion capture

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Intelligent Traffic Management System using Multi-Agent Reinforcement Learning

Intelligent Traffic Management System using Multi-Agent Rein...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Dalaali Shaheen Getzi Jeba Leelipushpam Paulraj Immanuel Johnraja Jebadurai Stewart Kirubakaran Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of CSE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-Learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-Learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-Learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control.

关键词： Adaptation models Renewable energy sources System performance Urban areas Reinforcement learning Traffic control Real-time systems Vehicle dynamics Traffic congestion Optimization

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Safe platooning control of connected and autonomous vehicles on curved multi-lane roads

arXiv

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arXiv 2025年

作者： Chen, Xiao Tang, Zhiqi Johansson, Karl H. Mårtensson, Jonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct parts: a lateral control law that ensures a geometrical convergence towards the reference path regardless of the translational velocity, and a longitudinal control design for each vehicle to achieve the desired relative arc length and velocity with respect to its neighboring vehicle. The second novelty is exploiting the constructive barrier feedback as an additive term to the nominal tracking control, ensuring both lateral and longitudinal collision avoidance. This constructive barrier feedback acts as a dissipative term, slowing down the relative velocity toward obstacles without affecting the nominal controller’s performance. Consequently, our proposed control method enables safe platoon formation of vehicles on curved multi-lane roads, with theoretical guarantees for safety invariance and stability analysis. Simulation and experimental results on connected vehicles are provided to further validate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback

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Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks

arXiv

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arXiv 2025年

作者： Ihsan, Asim Asif, Muhammad Safi, Hossein Tavakkolnia, Iman Haas, Harald Electrical Engineering Division Department of Engineering University of Cambridge Cambridge United Kingdom School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

In this paper, we propose an innovative predict-and-optimize algorithm designed for hybrid WiFi/LiFi networks, aiming to achieve service differentiation while maximizing energy efficiency (EE). The proposed framework utilizes multi-access technology real-time intelligent controller (mATRIC) to dynamically predict the appropriate network slice for each user based on historically monitored key performance indicators (KPIs). This prediction is facilitated by a deep learning model trained using the resilient backpropagation algorithm, with training conducted on KPIs data at the universal non-real time RAN intelligent controller (non-RT RIC). This trained model enables real-time slice selection by mATRIC. In the subsequent phase, the algorithm focuses on optimizing EE of hybrid network as a function of precoding vectors for the predicted slices by employing techniques from sequential convex approximation and the inner approximation method. We introduce novel approximations to convert non-convex objective functions and constraints into convex forms, and develop an iterative algorithm to achieve sub-optimal solutions. Additionally, the EE maximization problem, ensures alignment with end-to-end latency requirements. It also addresses the various constraints inherent to hybrid systems, such as input signal limitations for LiFi LEDs, data rate restrictions, and power budget considerations. Simulation results validate the effectiveness of the proposed algorithm, demonstrating significant improvements in EE while ensuring service differentiation within hybrid network environments. © 2025, CC BY.

关键词： Budget control

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Quantum Multi-Agent Reinforcement Learning for Joint Cube-Satellites and High-Altitude Long-Endurance Aerial Vehicles in SAGIN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Kim, Gyu Seon Cho, Yeryeong Park, Soouhyun Jung, Soyi Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

Cube satellites (CubeSats) have grown into the primary non-terrestrial network capable of providing global access services in satellite-air-ground integrated networks (SAGIN). Nonetheless, the provision of genuinely global access services solely via CubeSats is challenging due to the frequent handovers and the existence of polar regions where service availability is compromised in SAGIN. To tackle these issues, the design of innovative quantum multi-agent reinforcement learning (QMARL)-based algorithm is tailored for the cooperative scheduling of multi-CubeSat/high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) systems. This algorithm aims to achieve high quality of services, energy efficiency, and high capacity. Furthermore, logarithmic scale reduction in action dimensions can be realized, due to the modification in quantum measurement in QMARL. This is essential when the number of CubeSats and HALE-UAVs increases. Based on a realistic CubeSat/HALE-UAV experimental environment using real-world data, the excellence of our proposed QMARL-based scheduler is demonstrated. © 2025 IEEE.

关键词： Reinforcement learning

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SCALABLE HPC JOB SCHEDULING AND RESOURCE MANAGEMENT IN SST

arXiv

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arXiv 2025年

作者： Abdurahman, Abubeker Hossain, Abrar Brown, Kevin A. Yoshii, Kazutomo Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo ToledoOH United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces a workflow management components that supports the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance. © 2025, CC BY.

关键词： Job shop scheduling

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Silicon Solar Cell-Integrated Flexible Retinal Prosthesis for Artificial Vision 38

Silicon Solar Cell-Integrated Flexible Retinal Prosthesis fo...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Kim, Chaesung Chung, Seung-Han Kim, Yong-Jin Roh, Hyeonhee Lee, Seung-Ki Kim, Yong-Kweon Lee, Hyung-Min Park, Jae-Hyoung Kang, Min Gu Im, Maesoon Korea University School of Electrical Engineering Seoul Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Photovoltaics Research Department Daejeon Korea Republic of Dankook University Department of Electronics and Electrical Engineering Yongin Korea Republic of Brain Science Institute Seoul Korea Republic of Division of Bio-Medical Science and Technology Seoul Korea Republic of Kyung Hee University KHU-KIST Department of Converging Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798331508890

To date, no flexible silicon solar cell capable of directly converting visible light into electrical current has been developed for use in retinal prosthetic devices. In this study, we successfully fabricated silicon solar cells into small, pixelated structures and demonstrated their potential for retinal prostheses. To minimize surgical incisions during implantation and enhance device stability on the retina, we incorporated a flexible polymer substrate. Our results underscore the promise of silicon solar cells in biomedical engineering, especially for retinal prosthesis applications © 2025 IEEE.

关键词： Cell engineering

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