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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kehinde O. Odeyemi Pius A. Owolawi Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In this paper, a non-orthogonal multiple access (NOMA) dual-hop free space optical (FSO)/radio frequency (RF) relaying communication system is presented where reconfigurable intelligent surface (RIS) with phase error and hybrid automatic repeat request (H-ARQ) protocols are considered on the RF link. The FSO link is subjected to Gamma-Gamma distributions with pointing errors while the RF link follows Nakagamim distributions. To quantify the system performance, the exact outage probability expressions for the near and far users are obtained. Under the same conditions, the results reveal the superiority of NOMA over orthogonal multiple access (OMA) and the accuracy of the results are validated via the Monte-Carlo simulations.

关键词： Radio frequency NOMA Protocols Free-space optical communication Monte Carlo methods System performance Cooperative systems Reconfigurable intelligent surfaces Probability Power system reliability

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Comprehensive Modeling and Investigation of Intrinsic Variation Source Fluctuation in 2D-Layered Thin Film Transistors

Comprehensive Modeling and Investigation of Intrinsic Variat...

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Annual International Symposium on Reliability Physics

作者： Tao Du Wei Zhang Xuanyao Fong Guangxi Hu Peng Zhou Ye Lu State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Electrical and Computer Engineering National University of Singapore (NUS) Singapore Singapore

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

In this work, a Voronoi-TCAD co-simulation framework is developed to investigate the impact of intrinsic and extrinsic variation sources on the performance and variability of 2D-layered TFTs. It is found that : i) High trap density exacerbates device variability by amplifying localized energy barriers and enhancing barrier interference; ii) The device variability reaches peak value for intermediate numbers of grains, while increased numbers of grains reduce overall variability due to statistical averaging effects, highlighting the importance of considering both the quantity of grain boundaries and their spatial distribution as key variability sources; iii) interlayer interactions in multilayer structures further exaggerate the device variability, particularly for the on-current. This demands precise control of grain alignment and distribution. These findings emphasize the importance of grain engineering strategies, such as optimizing grain size, reducing defect density, and improving interlayer uniformity, to suppress variability and enhance device performance in large-scale 2D-layered TFT applications.

关键词： Performance evaluation Grain size Grain boundaries Graphical models Fluctuations Interference Reliability engineering Nonhomogeneous media Thin film transistors Physics

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An Efficient Federated Transfer Learning Approach for Multi-UAV systems

An Efficient Federated Transfer Learning Approach for Multi-...

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National Conference on Communications (NCC)

作者： Vedang Bhupesh Shenvi Nadkarni Sandeep Joshi L. Rajya Lakshmi Department of Computer Science and Information Systems Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

Recent advances in multi-unmanned aerial vehicle (UAV) based federated learning do not take into consideration the massive computational requirements of modern deep learning models on mobile UAV s. Additionally, there has been significant progress that shows that the information transmitted between the federated agent and the central hub can be attacked to undermine the privacy of the data. We propose a novel multi-UAV-based federated transfer learning system that drastically reduces the computational burden overall, shifts it from UAV s to the ground fusion center, and reduces the bandwidth requirements while enhancing its secure nature. The proposed system makes multi-UAV learning significantly fast, reliable, power efficient, and practically feasible. Furthermore, we provide simulation and experimental results to demonstrate the effectiveness of the proposed system.

关键词： Deep learning Image segmentation Data privacy Federated learning Power supplies Transfer learning Object detection Autonomous aerial vehicles Data models Reliability

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Sensitivity of DC Network Representation for GIC Analysis

Sensitivity of DC Network Representation for GIC Analysis

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IEEE Texas Power and Energy Conference (TPEC)

作者： Aniruddh Mishra Arthur K. Barnes Jose E. Tabarez Adam Mate Department of Electrical and Computer Engineering University of Texas at Austin Austin TX Information Systems and Modeling Group Los Alamos National Laboratory Los Alamos NM

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, including the method of representing the blocking devices, result in significant variations to the power loss calculations. To analyze these effects, we conclude the paper by comparing the losses on a sample network with different modeling implementations.

关键词： Analytical models Sensitivity Substations Power transmission lines Windings Circuit faults Security Reliability Electric fields Saturation magnetization

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The Utilization of a Parallel Computing Algorithm for Accelerating Switching-Level Modeling of Power Electronics Simulations in a T-Type PV Inverter

The Utilization of a Parallel Computing Algorithm for Accele...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Buck F. Brown Liwei Wang Zheyu Zhang Johan Enslin Yi Li Department of Electrical and Computer Engineering Clemson University North Charleston SC USA School of Computing Clemson University North Charleston SC USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

High-fidelity, switching-level modeling of power electronics simulations can be computationally intensive and time-consuming. This computational burden has recently escalated further due to the increased number of converters implemented in a system, e.g., renewable energy systems. Unlike switching modeling, average modeling can alleviate the computational burden and adequately represent a converter’s behavior for controller design. However, average modeling does not encapsulate switching harmonics, which are crucial for power electronics design, operation, and reliability studies. This paper proposes a novel way to accelerate the switching-level modeling of simulations by leveraging parallel computing techniques and showcases this technique in a specific case study. This new technique is called the average-to-switching (A2S) model. First, the basic concept of the A2S model is outlined. Second, a case study is designed for a two-stage, T-type PV inverter. Third, electrical and thermal waveforms produced by the case study are given and discussed. The results of testing the A2S model showcase a speedup of greater than 22 times the simulation benchmark while maintaining an R 2 value of greater than 0.99.

关键词： Renewable energy sources Electric potential Accuracy Computational modeling Switches Parallel processing Power system harmonics Reliability engineering Multilevel inverters Harmonic analysis

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Laser-driven high-resolution MeV x-ray tomography

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Optica 2025年第3期12卷 433-436页

作者： Hollinger, Reed Wang, Shoujun Anaraki, Sina Zahedpour King, James Zhang, Ping Zeraouli, Ghassan Bengoa, Alejandro Figueroa Sheats, Matt Scott, Shannon Heidemann, Joel Hunter, James Wang, Yong Edwards, Ray Faulkner, Matt Aedy, Chris Rocca, Jorge J. Gautier, Donald C. Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States Los Alamos National Laboratory Los AlamosNM87545 United States AWE PLC Berkshire United Kingdom Department of Physics Colorado State University Fort CollinsCO80523 United States Menlo ParkCA94025 United States

The need for high-resolution MeV x-ray tomography to observe the three-dimensional structure of dense, large-sized objects is rapidly increasing for the non-destructive evaluation of critical additively manufactured parts, national security, and other applications. We report a demonstration of highresolution MeV computed tomography of a dense, large object with a laser-driven x-ray source. A record detector-limited MeV radiograph resolution of 2 ) with sub-millimeter resolution was performed using 2160 individual radiographs. The small x-ray source size opens the prospect of extremely high-resolution tomographs of large, dense objects. This laser-driven approach has major advantages for non-destructive evaluation. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Femtosecond lasers

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CPM-Based Tunable Phase-Attached Radar–Communications (PARC)

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar systems 2025年 3卷 520-538页

作者： Sahin, Cenk McCormick, Patrick M. Metcalf, Justin G. Jakabosky, John Blunt, Shannon D. Perrins, Erik S. Owen, Jonathan Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States The University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States The University of Oklahoma Advanced Radar Research Center NormanOK73019 United States Naval Research Laboratory Radar Division WashingtonDC20375 United States

Motivated by the increasing need for efficient use of the electromagnetic spectrum (EMS) in congested and contested environments, a codesigned dual-function radar-communication (DFRC) waveform framework was introduced, which combines the desirable features of a pulsed radar transmission (i.e., constant amplitude and continuous phase) with the ability to embed information in the phase of the waveform via continuous-phase modulation (CPM). This CPM-based phase-attached radar-communication (PARC) waveform operates in a pulse-agile mode, which introduces a coupling of the fast- and slow-time dimensions through what is known as range-sidelobe modulation (RSM). The flexibility of CPM-based PARC via its multiple tunable parameters provides the ability to control this radar performance degradation at the expense of bit error rate (BER) and/or data throughput. Furthermore, the severity of RSM can likewise be mitigated via mismatched filter pulse compression on receive to reduce the variance of the pulse compression responses. Here, we evaluate the radar and communication performance trade space as a function of the CPM-based PARC parameters when assuming both matched and mismatched filter pulse compression at the radar receiver. The efficacy of the CPM-based PARC framework for both radar and communications is experimentally validated in an open-air environment using a radar in a quasi-monostatic configuration and a communication receiver in the field of view of the radar. © 2023 IEEE.

关键词： Matched filters

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Quantum Driven Dynamic Passivity Based Neuromechanical Control for Wrist Rehabilitation Robot

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年

作者： Khan, Naveed Ahmad Hussain, Fahad Goyal, Tanishka Jamwal, Prashant K. Hussain, Shahid University of Canberra School of Information Technology and Systems ACT Canberra Australia Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan

Robotic-assisted rehabilitation for wrist movements demands adaptive systems capable of balancing patient autonomy with robotic support. The integration of artificial intelligence (AI) into robotic-assisted rehabilitation offers transformative potential in delivering personalized, dynamic, and effective therapeutic interventions. This study introduces a novel neuromechanical control framework integrating a passivity observer with Quantum-Enhanced Deep Reinforcement Learning (QDRL) for adaptive impedance scaling in wrist rehabilitation robotics. The passivity observer continuously monitors energy exchanges to classify patient states into passive (patient requiring robotic assistance) and non-passive (patient actively participating) categories, dynamically guiding the robot’s impedance adjustments. Experiments were conducted with ten unimpaired human subjects (eight male and two female), who were instructed to simulate rehabilitation scenarios, focusing on three key wrist movements, flexion/extension (FL/EX), abduction/adduction (AB/AD), and pronation/supination (PR/SU). Experimental results showed high correlations ( > 0.83 ) between energy-based and electromyography (EMG)-based passivity classifications, confirming the reliability of the proposed approach. Furthermore, the designed QDRL model significantly outperformed traditional reinforcement learning methods, achieving superior adaptability, stability, and higher average rewards during robotic impedance control. The framework offers advancement in optimizing robotic assistance during motor recovery, promoting personalized rehabilitation by tailoring interventions to the specific needs of each patient. © 2018 IEEE.

关键词： Electromyography

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Experimental assessment of a semi-submersible floating solar platform subjected to wave and wind loading 6th

Experimental assessment of a semi-submersible floating solar...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Campbell, M. Baruah, G. Karimirad, M. Van, M. Pakrashi, V. School of Natural and Built Environment Queen’s University Belfast Belfast United Kingdom School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom UCD Centre for Mechanics Dynamical Systems and Risk Laboratory School of Mechanical and Materials Engineering University College Dublin Ireland

ISBN: (纸本)9781032905570

Floating solar platforms are subjected to coupled wave and wind induced responses, straddling the unpredictable wave-wind boundary layer. The simultaneous actions of these coupled responses in real sea conditions are not yet fully investigated for floating solar systems. The fluidstructure interactions are complex due to these coupled responses. The combined response to these wave and wind loads results in a coupled motion and tension in the mooring lines. This paper investigates the wave-only and wave-wind load case scenarios using scaled model wave tank testing. To study these scenarios, the Qualisys motion capture system is used to record the motions of the structure in the tank and load cells to record the mooring loads. This study investigates the correlation of wave, wind, motions, and mooring tension. The contribution of the wind loading component to the coupled wave-wind scenario is assessed and trends are determined for the wind loading contribution effects observed. © 2025 The Author(s).

关键词： Mooring

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