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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(Science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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Neuron-neuron attraction shapes morphology and activity of tissue engineered brain constructs

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Neural Regeneration Research 2022年第12期17卷 2655-2656页

作者： Yevgeny Berdichevsky Department of Bioengineering Department of Electrical and Computer EngineeringLehigh UniversityBethlehemPAUSA

The propensity of neuronal stem cells to aggregate is well *** of differentiated neurons,particularly those of the brain regions such as the cortex,has been reported more recently(Hasan et al.,2019;Ming et al.,2020).However,the tendency of these cells to aggregate may play a significant role in the brain’s response to injury,and may also be important in developing regenerative therapies to treat brain *** types of injury,including stroke and trauma,result in formation of liquid-filled cavities in the brain(Kazim et al.,2011;Moreau et al.,2012).Cavities are also produced by resection surgery in patients suffering from epilepsy or by surgical brain tumor *** cavitation represents a loss of neural circuitry and therefore leads to deficits in function and behavior.

关键词： al. epilepsy Neuron

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Pyramid diffractive optical networks for unidirectional image magnification and demagnification

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Light(Science & Applications) 2024年第9期13卷 1841-1864页

作者： Bijie Bai Xilin Yang Tianyi Gan Jingxi Li Deniz Mengu Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCAUSA Bioengineering Department University of CaliforniaLos AngelesCAUSA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCAUSA

Diffractive deep neural networks(D2NNs)are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output ***,we present a pyramid-structured diffractive optical network design(which we term P-D2NN),optimized specifically for unidirectional image magnification and *** this design,the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or *** P-D2NN design creates high-fidelity magnified or demagnified images in only one direction,while inhibiting the image formation in the opposite direction—achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor ***,the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single *** also designed a wavelength-multiplexed P-D2NN,where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions,at two distinct illumination ***,we demonstrate that by cascading multiple unidirectional P-D2NN modules,we can achieve higher magnification *** efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination,successfully matching our numerical simulations.P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

关键词： unidirectional illumination opposite

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Hierarchical Scheduling for Oil Spill SAR Image Detection in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9689-9703页

作者： Son, Seok Bin Park, Soohyun Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of

Oil spills represent significant environmental hazards in ocean ecosystems, requiring rapid and accurate detection and response mechanisms. Due to its efficacy, synthetic aperture radar (SAR) is an important tool for remote monitoring of ocean environments. However, operational constraints such as communication time between low Earth orbit (LEO) satellites and ground stations (GS), as well as finite buffers, pose challenges. To address these issues, this paper proposes a space-air-ground-integrated network (SAGIN) scenario comprising LEO satellites, high altitude platforms (HAPs), and ground stations (GSs), suitable for rapid and accurate oil spill detection using SAR images. In addition, a novel three-step algorithm is proposed to facilitate SAR image transmission effectively. The first step of the proposed algorithm focuses on maximizing the transmission of ocean SAR images from the LEO satellites to the HAPs. This optimization involves the neural Myerson auction (NMA)-based scheduler, a second-price auction mechanism with deep learning methodologies for truthfulness, distributed computation, and low computational complexity. In addition, the pathloss is also considered for effective communication system design. The second step algorithm aims to maximize data transmission efficiency from HAPs to GSs, employing scheduling strategies based on the Markov decision process (MDP). This algorithm enables optimal decision-making over time steps, considering the throughput of each GS and the age of the information (AoI) ratio. Finally, an image compression rate control algorithm based on Lyapunov optimization is employed at the GSs to enhance detection accuracy while ensuring queue stability. The performance of the proposed three-step algorithm is intensively evaluated against various benchmark algorithms, demonstrating its superiority in oil spill detection efficacy. © 1967-2012 IEEE.

关键词： Oil spills

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(Science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Joint Quantum Reinforcement Learning and Neural Myerson Auction for High-Quality Digital-Twin Services in Multi-Tier Networks

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IEEE Internet of Things Journal 2025年第13期12卷 23722-23735页

作者： Park, Soohyun Kim, Gyu Seon Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of

In order to build realistic digital-twin systems, this paper proposes a novel two-stage algorithm for high-quality digital-twin services in cloud-assisted multi-tier networks. In our proposed algorithm, the first stage is quantum multi-agent reinforcement learning (QMARL)-based scheduling for differentiated quality control of individual segments of digital-twin virtual objects in our cloud. As the number of segments selected by each edge increases, the edge's action dimension expands exponentially, posing significant challenges to learning with conventional MARL. To solve this problem, the quantum-inspired MARL-based scheduler is considered in order to reduce the scheduling action dimensions into a logarithmic-scale. For the scheduling formulation, age-of-information (AoI) is also considered for low-latency high-quality digital-twin services. Additionally, the second stage is for the fast and seamless distribution of differentiated quality-controlled segments of virtual objects. For this objective, each user requests its desired segments and one of nearby edges is selected. Among various approaches, this second stage considers second price auction for truthful and distributed computation. Furthermore, low-complexity computation can be realized by avoiding integer programming based computation which is NP-hard. The proposed two-stage algorithm achieves performance levels that are 8.33 and 1.18 times higher in terms of reward value in high dimensions and revenue, respectively, compared to other benchmarks. © 2014 IEEE.

关键词： Scheduling algorithms

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Dynamic Quantum Federated Learning for UAV-Based Autonomous Surveillance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8158-8170页

作者： Park, Soohyun Son, Seok Bin Jung, Soyi Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University School of Electrical Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment. © 1967-2012 IEEE.

关键词： Federated learning

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DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game 39

DualGFL: Federated Learning with a Dual-Level Coalition-Auct...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaobing Zhou, Xiangwei Zhang, Songyang Sun, Mingxuan Division of Electrical and Computer Engineering Louisiana State University United States Department of Electrical and Computer Engineering University of Louisiana Lafayette United States Division of Computer Science and Engineering Louisiana State University United States

ISBN: (纸本)157735897X

Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients’ preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions’ winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL’s effectiveness in improving both server utility and client utility. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios 38

Learning Cortico-Muscular Dependence through Orthonormal Dec...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Shihan Hu, Bo Jia, Tianyu Clarke, Alexander Kenneth Zicher, Blanka Caillet, Arnault H. Farina, Dario Príncipe, José C. Department of Bioengineering Imperial College London United Kingdom Department of Electrical and Computer Engineering University of Florida United States

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://***/bohu615/corticomuscular-eigen-encoder. © 2024 Neural information processing systems foundation. All rights reserved.

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All-optical complex field imaging using diffractive processors

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Light(Science & Applications) 2024年第6期13卷 1122-1139页

作者： Jingxi Li Yuhang Li Tianyi Gan Che-Yung Shen Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA90095USA Bioengineering Department University of CaliforniaLos AngelesCA90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA90095USA

Complex field imaging,which captures both the amplitude and phase information of input optical fields or objects,can offer rich structural insights into samples,such as their absorption and refractive index ***,conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a *** limitation can be overcome using interferometric or holographic methods,often supplemented by iterative phase retrieval algorithms,leading to a considerable increase in hardware complexity and computational ***,we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital *** design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field,forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design,axially spanning~100 *** intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field,eliminating the need for any digital image reconstruction *** experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum,with the output amplitude and phase channel images closely aligning with our numerical *** envision that this complex field imager will have various applications in security,biomedical imaging,sensing and material science,among others.

关键词： field. utilize collective

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