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Investigating the wound healing potential of low-power 661 nm laser light in a pigmented hairless murine model

Photochemical and Photobiological Sciences 2025年第5期24卷 779-790页

作者： Statha, Dimitra Sfiniadakis, Ioannis Rallis, Michail Anastassopoulou, Jane Alexandratou, Eleni Laboratory of Biomedical Optics and Applied Biophysics School of Electrical and Computer Engineering National Technical University of Athens Zografou Campus Athens15780 Greece Section of Pharmaceutical Technology Department of Pharmacy National and Kapodistrian University of Athens Panepistimiopolis Zografou Athens15784 Greece Pathologoanatomic Laboratory Athens Naval Hospital Athens11521 Greece

Photobiomodulation (PBM) has emerged as a promising method for enhancing wound healing. However, a standardized therapeutic protocol has not yet been established. This study aimed to determine the optimal irradiation parameters for wound healing in pigmented hairless mice (SKH-hr2). Mice were irradiated daily with energy doses of 2 or 4 J/cm2, achieved with different power densities in each group: 20, 50, or 100 mW/cm2. Various methods were used to evaluate the therapeutic efficacy, including histopathological analysis, clinical observation, photo-documentation, assessment of biophysical skin parameters, and Fourier Transform infrared (FT-IR) spectroscopy. The results indicated that the most favorable outcomes regarding wound healing acceleration and inflammation reduction were achieved with an irradiation setting of 50 mW/cm2 and 2 J/cm2. However, the group subjected to prolonged irradiation times with a power density of 20 mW/cm2 and energy of 4 J/cm2 exhibited subcutaneous bleeding. The FT-IR spectral absorption bands of amide groups provided important molecular-level information about the secondary structure of collagen, particularly in relation to skin regeneration and the response to applied energy, in agreement with histological data. This study highlights the critical need for further investigation into the parameters of photobiomodulation to ensure its effective application to the different skin phototypes and to mitigate potential adverse effects arising from incorrect usage. © The Author(s) 2025.

关键词： Fourier transform infrared spectroscopy

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Top-Down Fabrication of InGaN-Based Nano Light-Emitting Diodes 6

Top-Down Fabrication of InGaN-Based Nano Light-Emitting Diod...

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Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) VI 2025

作者： Chiu, Y.C. Bayram, C. Grainger College of Engineering Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL61801 United States Nick Holonyak Jr. Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign IL61801 United States

ISBN: (纸本)9781510686069

Micro/Nano light emitting diodes (LEDs) have become front-runners for next-generation display architectures compared to organic LED (OLED) and liquid crystal displays in part due to their superior performance. Their versatility has led to additional applications in visible light communications (VLC), sensing and optogenetics. Despite potential benefits of smaller devices, there have been limited efforts in demonstrating sub-micron LEDs due to challenges in scaling fabrication techniques from large area LEDs. In this work, first, we demonstrate a top-down fabrication process using electron beam lithography where nano-LEDs with mesa sizes down to 250nm were fabricated. Then, the optical properties of the nano-LEDs are studied. Spectral properties as a function of injection current density from 1.6 Acm-2 to 240 Acm-2 were measured showing minimum variation in FWHM of approximately 20 nm and invariant peak wavelength of approximately 439 nm from 24 Acm-2 to 240 Acm-2. Spatially resolved optical properties were studied via micro-electroluminescence mapping and confocal microscopy demonstrating sub-micron spatial emission profile width of approximately 800 nm. This work shows the potential of nano LEDs as a suitable technology for next generation displays and advanced light engines. © 2025 SPIE.

关键词： Organic light emitting diodes (OLED)

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Self-Supervised Learning Using Nonlinear Dependence

arXiv

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arXiv 2025年

作者： Sepanj, M. Hadi Ghojogh, Benyamin Fieguth, Paul Vision and Image Processing Group Systems Design Engineering University of Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo ON Canada

Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear correlations, they often neglect the intricate relations between samples and the nonlinear dependencies inherent in complex data. In this paper, we introduce Correlation-Dependence Self-Supervised Learning (CDSSL), a novel framework that unifies and extends existing SSL paradigms by integrating both linear correlations and nonlinear dependencies, encapsulating sample-wise and feature-wise interactions. Our approach incorporates the Hilbert-Schmidt Independence Criterion (HSIC) to robustly capture nonlinear dependencies within a Reproducing Kernel Hilbert Space, enriching representation learning. Experimental evaluations on diverse benchmarks demonstrate the efficacy of CDSSL in improving representation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Reader

FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Read...

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Australian Microwave Symposium (AMS)

作者： Likitha Lasantha Javad Aliasgari Shahed I. Khan Biplob Ray Nemai C. Karmakar Department of Electrical and Computer Systems Engineering Monash University Clayton Australia School of Engineering and Technology Central Queensland University Melbourne Australia

ISBN: (数字)9798350379969

ISBN: (纸本)9798350379976

Ultra-wideband (UWB) technology has gained traction for applications demanding high bandwidth, precise target localisation, and low power consumption. This paper presents an FPGA-based UWB pulse generator tailored for an Impulse-radio (IR) UWB chipless RFID reader. Using a low-cost FPGA platform, the design generates a square wave signal with a pulse repetition frequency (PRF) of 1-50 MHz. This base signal undergoes pulse sharpening to produce picosecond-scale pulses, achieving the bandwidth exceeding 500 MHz necessary for UWB communication. Such a high-bandwidth signal ensures better range resolution, meeting the requirements of applications of UWB chipless RFID readers. By implementing pulse generation and PRF control on an FPGA, our system combines low cost, flexibility, and scalability, making it suitable for diverse UWB applications.

关键词： Pulse generation Costs Scalability Ultra wideband communication Chipless RFID Bandwidth Field programmable gate arrays Tuning Signal resolution Radiofrequency identification

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Grid Sweep: Active and Passive Signal Processing

Grid Sweep: Active and Passive Signal Processing

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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Philip Yop Halah Shehada Lawrence Livermore National Laboratory Livermore California Electrical and Computer Engineering Department Virginia Tech Blacksburg Virginia

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

The GridSweep device is an actively probing wave-form measurement unit developed by McEachern Laboratories in conjunction with Lawrence Livermore National Lab and Lawrence Berkeley National Lab. The active probing of the device combined with high sensitivity, and GPS time synchronization allow coordinated measurements of distribution system frequency response and other grid characteristics from common electrical outlets. This paper presents an initial review of the signal processing techniques applied to the collected data, a summary of interesting results from several different processing modalities including the active signal response, machine learning techniques, and ambient signal correlation. The early results indicate the measurements from the GridSweep device have potential for use to detect local grid properties like system impedance, and characteristics and identification of distributed energy resources connected on the system.

关键词： Measurement units Correlation Sensitivity Reviews systems operation Machine learning Signal processing Time measurement Distributed power generation Synchronization

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Determination of Parasitic Inductance of Surge Protective Devices

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Hadjicostas, Alexandros Y. Chrysochos, Andreas I. Koumlis, Stylianos Peppas, Georgios D. Tsovilis, Thomas Aristotle University of Thessaloniki High Voltage Laboratory School of Electrical and Computer Engineering Thessaloniki54124 Greece Hellenic Cables Research and Development Department Maroussi Athens15125 Greece Technical University of Crete School of Electrical and Computer Engineering Chania73100 Greece

This work investigates the effectiveness of various state-of-the-art techniques for determining the parasitic inductance of surge protective devices (SPDs) across a wide frequency range. This is critical, as the intrinsic inductive characteristics of SPDs influence their maximum residual voltage, which directly affects their protection efficiency against fast-front transients with high current derivatives. To provide an innovative solution to the problem of parasitic inductance determination, the parasitic inductance of single-phase and three-phase Deutsche Institut für Normung (DIN) rail SPDs is estimated by replacing the voltage-limiting components with copper blocks. Three methods are employed to estimate the parasitic inductance: 1) impedance spectroscopy measurements, 2) impulse current tests, and 3) finite element analysis (FEAs) simulations. The results are analyzed to evaluate the accuracy of the proposed methods and address key challenges encountered in determining the parasitic inductance. It is found that the parasitic inductance of the protection mode exhibiting the longer surge current path is up to 1.6 times greater than that of the shortest surge current path for the three-phase (3 + 1) SPD, and consequently the maximum residual voltage increases by ~10% when subjected to its nominal discharge current of 20 kA with an 8/20 μs waveform for the same protection modes. © 1963-2012 IEEE.

关键词： Electromagnetic transients

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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IEEE Symposium on High-Performance computer Architecture

作者： Yuzong Chen Ahmed F. AbouElhamayed Xilai Dai Yang Wang Marta Andronic George A. Constantinides Mohamed S. Abdelfattah Computer Systems Lab Cornell University Systems and Networking Research Group Microsoft Research Department of Electrical and Electronic Engineering Imperial College London

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD 1 , an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types; our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with < 0.5% accuracy loss on average. For generative tasks, BitMoD is able to quantize LLM weights to 3-bit while achieving better perplexity than prior LLM quantization scheme. Combining the superior model performance with an efficient accelerator design, BitMoD achieves an average of $1.69 \times$ and $1.48 \times$ speedups compared to prior LLM accelerators ANT and OliVe, respectively. 1 Code is available at: https://***/yc2367/BitMoD-HPCA-25

关键词： Technological innovation Quantization (signal) Accuracy Machine learning algorithms Costs Large language models Machine learning computer architecture Hardware Energy efficiency

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Connecting the Unconnectable through Feedback

arXiv

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arXiv 2025年

作者： Li, Yimeng Shao, Yulin The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China

Reliable uplink connectivity remains a persistent challenge for IoT devices, particularly those at the cell edge, due to their limited transmit power and single-antenna configurations. This paper introduces a novel framework aimed at connecting the unconnectable, leveraging real-time feedback from access points (APs) to enhance uplink coverage without increasing the energy consumption of IoT devices. At the core of this approach are feedback channel codes, which enable IoT devices to dynamically adapt their transmission strategies based on AP decoding feedback, thereby reducing the critical uplink SNR required for successful communication. Analytical models are developed to quantify the coverage probability and the number of connectable APs, providing a comprehensive understanding of the system's performance. Numerical results validate the proposed method, demonstrating substantial improvements in coverage range and connectivity, particularly for devices at the cell edge, with up to a 51% boost in connectable APs. Our approach offers a robust and energy-efficient solution to overcoming uplink coverage limitations, enabling IoT networks to connect devices in challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Energy utilization

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Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks

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IEEE Internet of Things Journal 2025年第9期12卷 11708-11721页

作者： He, Jingchao Cheng, Nan Sun, Ruijin Zhang, Ruqian Zhou, Conghao Quan, Wei Li, Changle Xidian University State Key Laboratory of ISN School of Telecommunications Engineering Xi’an710071 China AI and Intelligent Operation Center China Mobile Research Institute Xi’an710071 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm. © 2014 IEEE.

关键词： Mixed-integer linear programming

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