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IEEE Open Journal of Instrumentation and Measurement 2025年 4卷

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz AlShaikh Saleh, Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Olesz, Marek Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering HatfieldAL10 9EU United Kingdom Hamad Bin Khalifa University Qatar Foundation Electrical and Computer Engineering Program in College of Science and Engineering Doha Qatar Gdańsk University of Technology Department of Electrical Power Engineering Gdańsk80-233 Poland

Partial discharge (PD) is a widespread phenomenon instigated in power transformer (PT) insulation systems. PDs are triggered by voids that vary in size and position within the PT insulation. The electrical characteristics of those damaging, PD-causing cavities must be well understood, to accelerate the development of advanced PD detection techniques. Thus, the impact of varying the radius and position of spherical air voids on the characteristics of PDs in PTs is examined using a 3-D finite element analysis (FEA) model designed in COMSOL Multiphysics. The spherical voids are positioned between two windings of a 512 kV, three-phase (3φ ) PT. The peak electric field (EF) and aggregate energy in the FEA model are used in conjunction with laboratory measurements of the apparent discharge magnitude, for detailed analysis of Polyethylene Terephthalate Glycol (PET-G)-based cylindrical voids with different heights. Simulations demonstrate that the inception of PD activity in the PT model occurs for spherical voids with a radius exceeding 1 mm. Furthermore, the most severe PDs occur within the press-board insulation, adjacent to the uppermost part of the innermost windings. Experiments demonstrate that a significant increase in PD activity was observed for PET-G-based cylindrical voids with heights exceeding 1 mm. © 2022 IEEE.

关键词： Partial discharges

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded Systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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PHORTEX: Physically-Informed Operational Robotic Trajectories for Scientific Expeditions

Field Robotics

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Field Robotics 2025年 4卷 138-181页

作者： Victoria L. Preston Genevieve E. Flaspohler John W. Fisher Anna P. M. Michel Nicholas Roy Applied Ocean Physics and Engineering Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA 02139 Electrical Engineering and Computer Science Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA 02139 Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA 02543

Mobile robots are increasingly used to collect valuable in situ samples during scientific expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal plumes, algal blooms, warm-core eddies, and lava flows—are spatiotemporal distributions that evolve on spatial and temporal scales that complicate sample collection. Here, we consider the problem of charting the space-time dynamics of deep-sea hydrothermal plumes with the state-of-the-art autonomous underwater vehicle (AUV) Sentry. In the hydrothermal plume charting problem, the plume state is driven by complicated and unobserved dynamics in the deep sea. To effectively sample the moving plume, an autonomy system must infer plume dynamics from sparse, point observations, while respecting operational constraints of AUV Sentry that restrict the set of possible trajectories to nonadaptive, uniform-coverage patterns. We frame the plume charting problem as a sequential decision-making problem and formulate a mission planner PHORTEX (PHysically-informed Operational Robotic Trajectories for Expeditions) that strategically designs full mission trajectories for Sentry, where each mission plan is informed by the observations of the last. PHORTEX is composed of a trajectory optimizer, which maximizes expected samples collected within a moving plume, and PHUMES (PHysically-informed Uncertainty Models for Environment Spatiotemporality), a modeling framework that leverages an embedded simulator of idealized plume physics as an inductive bias to enable dynamics learning from extreme partial observations and a few Sentry deployments. In both simulation and in field trials at a hydrothermal site in the Gulf of California, we demonstrate that Sentry using PHORTEX learns to track a moving hydrothermal plume and gather samples that significantly improve upon baseline spatial and temporal diversity for use in downstream science tasks.

关键词： Trajectory Sensors Oceans Vehicle dynamics Robot sensing systems Adaptation models Spatiotemporal phenomena Decision making Marine robots Environmental monitoring Mobile robots

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2.3-μm deep UV micro-LEDs fabricated by self-aligned selective thermal oxidation process

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Optics Letters 2025年第11期50卷 3628-3631页

作者： Zhiyuan Liu Haicheng Cao Tingang Liu Patsy A. Mira Cortez Zixian Jiang Kexin Ren Na Xiao Yi Lu Xiao Tang Zuojian Pan Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

Recently, techniques for fabricating micro-sized deep ultraviolet light-emitting diodes (DUV LEDs) have been gaining attention. In this work, we propose a selective thermal oxidation (STO) process to fabricate ultra-small DUV micro-LEDs. Annealed in the air at 900°C for 2 h, the p-layers without SiO 2 protection are oxidized into insulating oxides, and oxygen penetrates into parts of the quantum wells. In contrast, the patterned SiO 2 protects the LED structure in the pixel regions. Therefore, micro-LED pixels are formed by the patterned SiO 2 and thermal oxidation process rather than the conventional reactive ion etching. The formed oxide in the unprotected region can function as the insulation layer between p–n electrodes. Meanwhile, the boundary between the pixel and the surrounding oxide is naturally formed during thermal oxidation, which is a “self-aligned” process. Dielectric deposition, precise lithography alignment, and aperture etching in the conventional process are no longer required in STO-based micro-LED fabrication, which reduces production complexity and cost. Based on the STO process, we have achieved 2.3-µm DUV micro-LED standalone pixels and arrays with a 270-nm emission wavelength. The standalone DUV micro-LED is possibly the smallest reported in the literature to date, which has a low-operation voltage of 4.9 and 6.75 V at 10 and 1000 A/cm 2 . Meanwhile, the fabricated DUV micro-LED arrays show leakage current density 4 × 10 −7 A/cm 2 at −5 V and a peak EQE of 0.77% under unpackaged conditions. We hope this work provides a new insight into micro-LED fabrication and further promotes future performance growth of DUV LEDs.

关键词： Light emitting diodes Light extraction Lithography Multiple quantum wells Quantum wells Ultraviolet light emitting diodes

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YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation

arXiv

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arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

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Dual-UNet: An End-to-End Deep Learning Framework for the Automatic Chronic Diabetic Wound Segmentation

Dual-UNet: An End-to-End Deep Learning Framework for the Aut...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Mustafa Alhababi Gregory Auner Hafiz Malik Lubna Alazzawi Muteb Aljasem Electrical and Computer Engineering Wayne State University Detroit MI USA Department of Smart Sensors and Integrated Microsystems Program Wayne State University Detroit MI USA Electrical and Computer Engineering University of Michigan-Dearborn Dearborn MI USA Electronics and Computer Engineering & Robotics Engineering Bowling Green State University Bowling Green OH USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Foot ulcers are one of the chronic wounds commonly occurring in diabetic patients and are associated with a high risk of major or minor amputations. Evaluation and measurement of such wounds are done manually by experts to assess the wound healing and suggest further treatment. Manual assessment methods can cause infections at times and are less feasible in the existence of pandemic situations. The development of artificial intelligence has brought a revolution in many domains of medical imaging. Deep learning-based computer-aided approaches can be applied to assist clinicians in assessing and monitoring chronic wounds. Wound segmentation helps to measure wound area which is useful for assessing its healing progress. In this research work, a deep learning-based segmentation method, namely Dual-UNet is presented for precisely segmenting the diabetic foot ulcers images. The foot ulcer images are passed to Dual-UNet to generate the mask image having the segmented wound area. We proposed two modified UNets each having a customized encoder and decoder with skip connections, atrous spatial pyramid pooling (ASPP) block, and output block to improve the segmentation results. We assessed our framework by performing experimentation on two benchmark datasets, named the foot ulcer segmentation (FUSeg) challenge and the AZH segmentation dataset. Additionally, we have also assessed our Dual-UNet for cross-corpora validation to demonstrate the generalization capability of the proposed approach. The qualitative and quantitative results highlight the accuracy and effectiveness of the Dual-UN et framework for segmenting chronic diabetic wounds. Future research should focus on the development of large and diverse wound datasets and improving the results for cross-corpora evaluation.

关键词： Deep learning Image segmentation Pandemics Computational modeling Manuals Wounds Diabetes Decoding Information technology Monitoring

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Light extraction improvement via ITO p-electrodes for InGaN red micro-LEDs emitting at 640 nm

Optics Continuum

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Optics Continuum 2025年第5期4卷 1040-1050页

作者： Cesur Altinkaya Mohammed A Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia cesur.altinkaya@kaust.edu.sa Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia daisuke.iida@kaust.edu.sa kazuhiro.ohkawa@kaust.edu.sa

We present light extraction efficiency (LEE) improvement for InGaN red micro-light emitting diodes (micro-LEDs) of various sizes operating at low current densities. We compared the characteristics of micro-LEDs with indium tin oxide (ITO) transparent p-electrodes with conventional opaque metal p-electrodes. 50 µm × 50 µm micro-LEDs with ITO p-electrodes achieved a peak on-wafer external quantum efficiency (EQE) of 2.54% with an emission wavelength of 640 nm at a current density as low as 0.4 A/cm 2 . This represents a 1.18-fold improvement in peak EQE compared to devices with metal p-electrodes. Light ray tracing simulation confirmed that the ITO p-electrodes exhibit 1.18 times higher light escape than metal-based micro-LEDs, validating the role of enhanced light extraction. These findings provide valuable insights for advancing high-definition display and VR applications.

关键词： Atomic layer deposition Indium tin oxide Light extraction Opacity Quantum efficiency Ray tracing

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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A Time-Domain Thin-Sheet Volume Integral Equation for Non-Dispersive Bi-Isotropic Media

A Time-Domain Thin-Sheet Volume Integral Equation for Non-Di...

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European Conference on Antennas and Propagation, EuCAP

作者： S. Celis Sierra R. Zhao M. Khamitova H. Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Bi-isotropic and bi-anisotropic materials are expected to revolutionize wireless communications through the extended use of metasurfaces. This paper proposes a time-domain integral equation solver to simulate non-dispersive biisotropic materials under the thin-sheet approximation. Thin-sheet approximation is used to convert the time-domain volume integral equation for non-dispersive bi-isotropic media into a surface integral equation. This surface equation is discretized in space using Rao-Wilton-Glisson (RWG) basis functions for the tangential components of the electric and magnetic fluxes and pulse basis functions for their normal components (relative to the surface of the thin sheet). In time, shifted Lagrange polynomials are used for discretization. Applying Galerkin testing in space and point matching in time yields a matrix system. This matrix system is solved for the unknown basis expansion coefficients at each time step via time-marching. Numerical results demonstrate the accuracy of the proposed method. Future work includes extending this solver to dispersive bi-anisotropic media and use it in the simulation of metasurface represented by generalized sheet transition conditions (GSTCs).

关键词： Wireless communication Accuracy Integral equations Media Metasurfaces Polynomials Time-domain analysis Dispersion Method of moments Testing

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Material informatics-driven insights into brain cancer nanocarriers: A bibliometric comparison of PLGA vs. liposomes

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OpenNano 2025年 21卷

作者： Makalew, Brilly Andro Abrori, Syauqi Abdurrahman Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta 11480 Indonesia

This study explores a comparative analysis of PLGA nanoparticles and liposomes as potential carriers for brain cancer drug delivery, with a special focus on how material informatics enhances their design, biocompatibility, and drug release profiles to improve treatment efficacy and contribute to sustainable health outcomes. The investigation employed a bibliometric analysis using Scopus and VOSviewer to uncover the role of material informatics in optimizing these nanocarriers. The analysis revealed that material informatics, particularly through the application of machine learning and molecular dynamics simulations, significantly optimizes the performance of both PLGA nanoparticles and liposomes. The results highlighted distinct strengths of each nanocarrier: PLGA nanoparticles excel in biodegradability, while liposomes offer superior drug encapsulation capabilities. However, material informatics techniques bridged these enhancing drug release kinetics, stability, and biocompatibility. These improvements are crucial for effective delivery across the blood-brain barrier, a major challenge in brain cancer treatment. The integration of computational modelling, machine learning, and high-throughput screening enabled by material informatics is shown to be a key factor in advancing the design and optimization of these nanocarriers. By leveraging these tools, researchers can develop more personalized and efficient drug delivery systems tailored to address the specific challenges of glioblastoma therapy, ultimately contributing to sustainable health outcomes © 2024 The Author(s)

关键词： Bibliometric analysis Brain cancer Drug delivery Liposomes Material informatics Nanocarriers PLGA nanoparticles VOSviewer

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