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Predictive modeling of MXene-based solar absorbers using a deep neural network

Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Mode Competition Dynamics in an Optoelectronic Oscillator: A Study Using an Extended Microwave-Photonic Iterative Nonlinear Gain (MING) Model

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Journal of Lightwave Technology 2025年第11期43卷 5252-5259页

作者： Zheng, Ruiqi Chen, Jingxu Zhang, Jiejun Yao, Jianping Jinan University Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Guangzhou510632 China University of Ottawa Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science OttawaK1N 6N5 Canada

An extended microwave-photonic iterative nonlinear gain (MING) model is proposed to analyze the mode competition dynamics in an optoelectronic oscillator (OEO). The power trajectories of multiple longitudinal modes in the mode competition process are calculated using the proposed extended MING model, by which we observe that mode competition is affected by the loop nonlinearity. On one hand, when the nonlinearity is enhanced, gain saturation effect becomes more significant. The strongest mode will gain the most energy from the optical carrier and win the competition, leading to single-mode oscillation. On the other hand, with enhanced nonlinearity, the intermodulation distortions (IMDs) become more significant. The mixing between the longitudinal modes would lead to energy transfer, causing weaker longitudinal modes to receive more energy, thereby hindering mode competition. Therefore, through suppressing the IMDs by reducing the loop gain, multi-mode operation can be reduced, facilitating stable single-mode operation. The mode competition dynamics of an OEO at different loop gain levels is evaluated by simulations. The simulation results show that by decreasing the loop gain, multi-mode oscillation is quickly evolved to single-mode oscillation, which is validated by an experiment. The proposed extended model is the first comprehensive study of the nonlinear dynamics of an OEO, showing the evolution from an initial multimode oscillation to a stable single-mode oscillation. © 2025 IEEE. All rights reserved,

关键词： Intermodulation distortion

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Analysis of Effective IBR Actuators for Forced Oscillation Mitigation

Analysis of Effective IBR Actuators for Forced Oscillation M...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Lakshmi Thotakura, N Aga Zhao, Yi Liu, Yilu The University of Tennessee Department of Electrical and Computer Engineering KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9798350352528

Forced Oscillations (FOs) occurring at frequencies close to a system's natural oscillation mode can induce resonance and widespread of high-amplitude oscillations. Different from mitigation approaches for natural oscillations, addressing FO mitigation requires special attention due to uncertainties of the FO source localization. Currently, accurate identification of FO source at the component level is time-consuming and unreliable. Supplementary FO damping control (FODC) through Inverter-Based Resources (IBR) is one state-of-the-art FO mitigation method. Selecting effective IBRs for FO mitigation is a critical step to guarantee the FO damping performance. This study explores how to select the most effective IBRs for damping FO resonant event with an inter-area natural oscillation. The study results in the l2l-bus mesh system show that the FODC through the IBRs near the FO source and close to the generators with high participation factor of natural oscillations can effectively mitigate FO events than other locations. © 2025 IEEE.

关键词： Damping

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Cybersecurity advancements for medical image transmission: a hybrid optical-based cryptosystem harnessing chaos, DNA sequences, and mandelbrot keys

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Multimedia Tools and Applications 2025年 1-41页

作者： Alalwan, Nasser El-Shafai, Walid Amoon, Mohammed Benjdira, Bilel Department of Computer and Communication Engineering King Saud University Riyadh Saudi Arabia Prince Sultan University Riyadh11586 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

In today's advanced technological age, characterized by innovations like big data processing, cloud computing, and the Internet of Things (IoT), there is a rising utilization of medical multimedia data, especially medical images. These images, integral to the Internet of Healthcare Things (IoHT), necessitate secure transmission due to the increasing risks of unauthorized breaches and tampering. Current security methods, especially for cloud and mobile platforms, often struggle with challenges related to processing capacity, memory use, data size, and energy, making them ill-suited for extensive medical data or resource-limited environments. To address these challenges, this study introduces a novel hybrid cryptosystem, drawing on the unique qualities of the optical Arnold chaotic map, DNA (DeoxyriboNucleic Acid) sequences, and Mandelbrot keys, providing a fortified approach to the secure streaming of medical images. The proposed framework operates via a precise and structured procedure. It begins by applying the optical Arnold chaotic map cipher to each of the three-color channels (R, G, and B) within a medical image. This is followed by overlaying DNA encoding sequences on the resultant encrypted image from the earlier ciphering phase. Leveraging this groundwork, we incorporate an advanced Mandelbrot set-driven shift mechanism specifically designed to create complex confusion patterns within the R, G, and B segments of the encrypted medical imagery. The efficacy of the proposed cryptosystem is rigorously substantiated through an extensive array of simulations supported by a comprehensive security analysis. The results highlight its unparalleled resilience and security capabilities in the realm of medical image encryption, marking a significant leap over previous systems in the literature. Essentially, our work pioneers a solution to a pressing challenge in medical image security, ensuring enhanced protection of delicate health data among the rapidly evolving advanc

关键词： Cybersecurity

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication Systems

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IEEE Transactions on Information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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Approaching ~0.1ppb Frequency Stability in ~11MHz AlN-on-Si Dual-Ring Bulk Acoustic Wave Mems Resonator 38

Approaching ~0.1ppb Frequency Stability in ~11MHz AlN-on-Si ...

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

作者： Watkins, Connor A. Kaisar, Tahmid Rais-Zadeh, Mina Feng, Philip X.-L. University of Florida Department of Electrical & Computer Engineering GainesvilleFL32611 United States California Institute of Technology Jet Propulsion Laboratory PasadenaCA91109 United States

ISBN: (纸本)9798331508890

We report on excellent frequency stability (Allan deviation, or ADEV) down to ~0.1ppb-level measured by operating an aluminum nitride on silicon (AlN/Si) dual-ring bulk acoustic wave (BAW) MEMS resonator in three schemes: self-sustained oscillator (SSO), phase-locked loop (PLL), and open loop without feedback. The dual-ring BAW resonator is operated in its radial-extensional (RE) mode, with resonance frequency f0 = 11.08MHz and quality factor Q ≈ 6000. The best measured ADEV specifications are σA = 3.76×10-9 at averaging time τ ≈ 0.4s in SSO scheme, σA = 1.69×10-10 at τ ≈ 0.3s in PLL scheme, and σA = 1.23×10-10 at τ ≈ 4.8s for open-loop operation. This is the best short-term frequency stability performance reported to date among AlN piezoelectric MEMS resonators, suggesting suitability for applications where ppb- or sub-ppb level frequency stability is desired, such as real-time sensing and precise timing on chip. © 2025 IEEE.

关键词： Aluminum nitride

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Convolutional Neural Network Approach for Emitter Association using Channel Identification in a MIMO System based on Propagation Features 59

Convolutional Neural Network Approach for Emitter Associatio...

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

作者： Talley, Michael F. Nyarko, Kofi Thompson, Willie L. Cole-Rhodes, Arlene Scott, Craig Johns Hopkins University Applied Physics Laboratory The Wireless Cyber Capabilities Group Laurel United States Morgan State University Electrical and Computer Engineering Baltimore United States

ISBN: (纸本)9798331513269

In this paper, an application of a 1D deep convolutional neural network (DCNN) and 4x4 1D DCNN Multi-channel Model (DCNN-MCM) was developed to predict the probability of a channel being associated with a given transmitter for each emitter in a 4x4 multi-input multi-output (MIMO) system. Counterintuitively, compared to the traditional approach to emitter association (EA), this research argues for the identification of received RF signals based on channel features (CFs) such as the channel impulse response (CIR) and transfer function (TF). Based on the CFs there are unique properties per transmit and receive pair that can be used to identify the different transmitters. More specifically, CFs are often defined by a wide sense stationary (WSS) stochastic process which gives them unique properties such as, being statistically independent and reciprocal for emitter association. Given these CFs, the received RF signals can be used to measure the clutter and objects surrounding the emitters. Safely assuming the surrounding clutter and objects are unique to each emitter, the estimation and tracking of this clutter provides a way of identifying what emitter is transmitting the observed signal. This method successfully classifies and identifies the emitters at 97.22% and 88.89% accuracy for the DCNN and DCNN-MCM respectively. This method was further compared with the accuracy of previous methods to test the validity of the approach and future improvements. © 2025 IEEE.

关键词： Convolutional neural networks

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A New Attention Based UNet and Gated Edge Attention Network for Retinal Vessel Segmentation 27th

A New Attention Based UNet and Gated Edge Attention Network ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Roy, Ayush Palaiahnakote, Shivakumara Pal, Umapada Chanda, Sukalpa Department of Electrical Engineering Jadavpur University Kolkata India School of Science Engineering and Environment University of Salford Salford United Kingdom Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Østfold University College Halden Norway

ISBN: (纸本)9783031781032

Early diagnosis of retinal diseases is crucial for preventing blindness. However, due to background variations and degradation in the images, retinal vessel segmentation has become challenging. As a result, accurate segmentation of the retinal vessels is essential for enhancing diagnosis to identify the disease. To achieve this, inspired by the special ability of the attention mechanism, which detects vital regions, and UNet, which segments the vital region, we propose a combination of a new attention mechanism and modified UNet for segmenting vessels in retina images. In the proposed segmentation model, the convolutional blocks have been modified to capture multiscale spatial information by varying the convolution dilation rates. Similarly, the Trainable tanh activation (T-Tanh) is adapted in a new way to identify changes in the flow of the feature gradients to differentiate between the retinal vessel pixels and the background. Furthermore, to make the segmentation robust, the Gated Edge Attention (GEA) network is proposed. The effectiveness of the segmentation is demonstrated by testing on two benchmark datasets, namely, STARE and CHASE. The results show that the performance of the proposed method is superior to the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ophthalmology

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