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arXiv 2024年

作者： Zhang, Chunlei Kim, Mun Zhang, Yi-Hui Wang, Yi-Pu Trivedi, Deepanshu Krasnok, Alex Wang, Jianbo Isleifson, Dustin Roshko, Roy Hu, Can-Ming Department of Physics and Astronomy University of Manitoba WinnipegMBR3T 2N2 Canada Zhejiang Key Laboratory of Micro-Nano Quantum Chips and Quantum Control School of Physics State Key Laboratory for Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States School of Physical Science and Technology Lanzhou University Lanzhou730000 China Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada

Achieving oscillations with small dimensions, high power, high coherence, and low phase noise has been a longstanding goal in wave physics, driving innovations across classical electromagnetic theory and quantum physics. Key applications include electronic oscillators, lasers, and spin-torque oscillations. In recent decades, physicists have increasingly focused on harnessing passive oscillatory modes to manipulate these oscillations, leading to the development of diverse gain-loss coupled systems, including photon-photon, exciton-photon, photon-magnon, magnon-phonon, and magnon-magnon couplings. This review provides a comprehensive overview of these systems, exploring their fundamental physical structures, key experimental observations, and theoretical insights. By synthesizing insights from these studies, we propose future research directions to further advance the understanding and application of gain-loss coupled systems for quantum science and quantum technologies. (The field of gain-loss coupled systems is vast. The authors welcome suggestions and feedback from the community to continuously improve this review article until it is published). Copyright © 2024, The Authors. All rights reserved.

关键词： Photons

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Periodically driven open quantum systems: Spectral properties and nonequilibrium steady states

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Physical Review B 2024年第18期109卷 184309-184309页

作者： Hao Chen Yu-Min Hu Wucheng Zhang Michael Alexander Kurniawan Yuelin Shao Xueqi Chen Abhinav Prem Xi Dai Department of Physics Princeton University New Jersey 08544 USA Department of Electrical and Computer Engineering Princeton University New Jersey 08544 USA Institute for Advanced Study Tsinghua University Beijing 100084 China Department of Physics Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong China Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Department of Physics Stanford University Stanford California 94305 USA School of Natural Sciences Institute for Advanced Study Princeton New Jersey 08540 USA

In this paper, we investigate periodically driven open quantum systems within the framework of Floquet-Lindblad master equations. Specifically, we discuss Lindblad master equations in the presence of a coherent, time-periodic driving and establish their general spectral features. We also clarify the notions of transient and nondecaying solutions from this spectral perspective, and then prove that any physical system described by a Floquet-Lindblad equation must have at least one physical nonequilibrium steady state (NESS), corresponding to an eigenoperator of the Floquet-Lindblad evolution superoperator UF with unit eigenvalue. Since the Floquet-Lindblad formalism encapsulates the entire information regarding the NESS, it in principle enables us to obtain nonlinear effects to all orders at once. The Floquet-Lindblad formalism thus provides a powerful tool for studying driven-dissipative solid-state systems, which we illustrate by deriving the nonlinear optical response of a simple two-band model of an insulating solid and comparing it with prior results established through Keldysh techniques.

关键词： Nonlinear optics Open quantum systems & decoherence Floquet systems Lindblad equation

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Novel Feature Selection Strategy for Cyclic Loss Prediction of Lithium-ion Electric Vehicle Battery

Novel Feature Selection Strategy for Cyclic Loss Prediction ...

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IEEE General Meeting Power& Energy Society

作者： Huzaifa Rauf Muhammad Khalid Naveed Arshad Michael Pecht Center for Advanced Life Cycle Engineering (CALCE) University of Maryland College Park United States Electrical Engineering Department K.A. CARE Energy Research and Innovation Center King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Computer Science Syed Babar Ali School of Science and Engineering (SBASSE) Lahore University of Management Sciences (LUMS) Lahore Pakistan

Battery cyclic loss is a key parameter to assess lithium-ion battery degradation in electric vehicles (EVs), while machine learning (ML) methods can be used in evaluating and predicting the degradation trend of battery health due to cyclic loss. The accuracy of ML methods is influenced by the input parameter selection of the model. This paper develops a feature selection strategy based on the utilization of a data pre-processing method, which extracts useful model input parameters from the battery data. To show the advantages of the method, eight widely used ML algorithms are applied to a case study and compared for battery cyclic loss prediction. The results show that the developed feature selection method has improved the prediction accuracy by at least 9%, in the case of LASSO regression The results also depict that the random forest (RF) regression, Gaussian Process Regression (GPR), and XGBoost methods, when applied in combination with the developed feature selection method, show an improvement of 44%, 48% and 52% in the prediction accuracy, respectively.

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Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification

arXiv

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arXiv 2023年

作者： Wang, Meng Gao, Feng Dong, Junyu Li, Heng-Chao Du, Qian The School of Information Science and Engineering Ocean University of China Qingdao266100 China The Sichuan Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu610031 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a Nearest Neighbor-based Contrastive Learning Network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at https://***/summitgao/NNCNet. © 2023, CC BY.

关键词： Image classification

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Joint Offloading and Resource Allocation for Hybrid Cloud and Edge Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Huang, Chong Chen, Gaojie Xiao, Pei Xiao, Yue Han, Zhu Chambers, Jonathon A. State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangdong China Home for 5GIC & 6GIC University of Surrey GuildfordGU2 7XH United Kingdom The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering the University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Engineering University of Leicester LeicesterLE1 7RU United Kingdom

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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An Analysis of The NASA CSDA Bistatic Radar GNSS-R Dataset

An Analysis of The NASA CSDA Bistatic Radar GNSS-R Dataset

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International Conference on Electromagnetics in advanced Applications (ICEAA)

作者： Mohammad Al-Khaldi Joel T. Johnson Darren S. McKague Anthony Russel Dorina Twigg Dept. of Electrical and Computer Engineering Electro Science Lab The Ohio State University Columbus OH USA Department of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USA Space Physics Research Laboratory University of Michigan Ann Arbor MI USA

The remote sensing of the Earth’s surface through a variety of spaceborne platforms has a long history of investigation. The selection of one sensing technology over another is typically governed by the geophysical products of interest and requirements of spatial and temporal sampling. Recent studies have highlighted the utility of GNSS-R (Global Navigation Satellite System-Reflectometry) measurements for the sensing of multiple geophysical properties. The modest cost of GNSS-R receive-only platforms also promotes the simultaneous launch of multiple receivers, further improving revisit times, and the L-band frequency used provides all weather operations given the insensitivity of L-band measurements to cloud cover and even heavy rain rates.

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Survey of Spectral Amplitude Coding Optical Code Division Multiple Access Families

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Journal of Lightwave Technology 2025年

作者： Ullah, Rahat Ullah, Sibghat Ali, Aitizaz Jianxin, Ren Alwageed, Hathal Salamah Yaya, Mao Qi, Zhipeng Yu, Yongyi Imtiaz, Waqas A. Khan, Maqbool School of Physics and Optoelectronics Nanjing University of Information Science and Technology China Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science Technology Nanjing210044 China Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science Technology Nanjing210044 China School of Electronics Science and Technology Southeast University China School of Technology NSF Group Asia Pacific University Malaysia Saudi Arabia College of Computer Science Jouf University Saudi Arabia Electrical Engineering Department University of Engineering and Technology Peshawar Pakistan Pak-Austra Fachhochschule Haripur Pakistan

Quest for high capacity communication have led the research community to explore a plethora of technologies. Among these, optical code division multiple access (OCDMA) technique stands tall with support for high data rate connectivity in terms of data, reach and the number of subscribers. The development of OCDMA based networks started with the design and implementation of one dimensional (1D) codes, which provides effective correlation properties to reduce the effect of interference due to multiple users (MAI) and phase-induced-intensity-noise (PIIN) in order to provide the required system capacity. Several families of codes have been developed to determine the most suitable combination between code length, weight against the auto- and cross-correlation property respectively. This survey paper will analyze the most commonly used families of codes from spectral amplitude coding (SAC) based OCDMA systems names Double Weight (DW) codes, Diagonal codes , zero cross-correlation (ZCC) codes, and hybrid codes in terms of different performance parameters and identify the most suitable combination of code length (L), code weight (w), and cross-correlation (λC) to be utilized for next generation communication applications that can provide high capacity and cardinality. © 1983-2012 IEEE.

关键词： Passive optical networks

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Giant photothermoelectric effect in silicon nanoribbon photodetectors

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Light(science & Applications) 2020年第1期9卷 853-860页

作者： Wei Dai Weikang Liu Jian Yang Chao Xu Alessandro Alabastri Chang Liu Peter Nordlander Zhiqiang Guan Hongxing Xu School of Physics and Technology Center for Nanoscience and Nanotechnologyand Key Laboratory of Artificial Micro-and Nano-structures of Ministry of EducationWuhan UniversityWuhan 430072China Department of Physics and Astronomy Department of Electrical and Computer Engineering and Laboratory for NanophotonicsRice UniversityHoustonTX 77005USA The Institute for Advanced Studies Wuhan UniversityWuhan 430072China

The photothermoelectric(PTE)effect enables efficient harvesting of the energy of photogenerated hot carriers and is a promising choice for high-efficiency photoelectric energy conversion and ***,the PTE effect was reported in low-dimensional nanomaterials,suggesting the possibility of optimizing their energy conversion ***,the PTE effect becomes extremely inefficient in low-dimensional nanomaterials,owing to intrinsic disadvantages,such as low optical absorption and immature fabrication *** this study,a giant PTE effect was observed in lightly doped p-type silicon nanoribbons caused by photogenerated hot *** open-circuit photovoltage responsivity of the device was 3-4 orders of magnitude higher than those of previously reported PTE *** measured photovoltage responses fit very well with the proposed photothermoelectric multiphysics *** research proposes an application of the PTE effect and a possible method for utilizing hot carriers in semiconductors to significantly improve their photoelectric conversion efficiency.

关键词： effect utilizing enable

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Data-Driven Wireless Anomaly Detection Using Spectral Features

Data-Driven Wireless Anomaly Detection Using Spectral Featur...

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MILCOM, Military Communications Conference

作者： Stephan Frisbie Mohamed Younis The Johns Hopkins University Applied Physics Laboratory Laurel Maryland United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore Maryland United States

In this work, we present an anomaly detection algorithm for wireless spectrum data and evaluate its ability to accurately detect interfering transmissions. The algorithm considers three types of interfering signals: a co-channel transmission, an interfering continuous-wave transmission, or an adversarial replay attack. At the core of each anomaly detector is a feature space transformation, which learns a compressed representation of the data, followed by various metrics on this representation. Four feature space transformations and four detection metrics are investigated, each of which represents different characterizations of the distribution of the data, totaling 16 configurations of the anomaly detector. Our algorithm is evaluated using Wi-Fi spectrum data from real-world radio frequency (RF) captures in both mild and harsh channel conditions. We present an analysis of the performance of the various configurations under each of the interference types at varying signal-to-interference ratios. Lastly, we discuss the implications of this performance on the the distribution of the data and the recommended model to detect each type of anomaly.

关键词： Training Spatial diversity Interference Detectors Feature extraction Extraterrestrial measurements Anomaly detection

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Multi-Task Semantic Communications via Large Models

arXiv

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arXiv 2025年

作者： Ni, Wanli Qin, Zhijin Sun, Haofeng Tao, Xiaoming Han, Zhu Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Data integration

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