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arXiv 2025年

作者： Liu, Meiding Zhou, Zhengchun Shi, Qiao Li, Guyue Liu, Zilong Fan, Pingzhi Lee, Inkyu School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Key Laboratory of Analytical Mathematics and Applications [Fujian Normal University Ministry of Education China Purple Mountain Laboratories for Network and Communication Security Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu611756 China School of Electrical Engineering Korea University Seoul02841 Korea Republic of

In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication. © 2025, CC BY.

关键词： Mean square error

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Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method

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China communications 2021年第12期18卷 94-107页

作者： Shilian Zheng Linhui Ye Xuanye Wang Jinyin Chen Huaji Zhou Caiyi Lou Zhijin Zhao Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing314000China College of Information Engineering Zhejiang University of TechnologyHangzhou310012China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou310012China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou310018China

The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been *** this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing *** adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing *** design three PUAA methods in black box *** or-der to defend against PUAA,we propose a defense method based on autoencoder named *** apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective *** experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of *** show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing *** addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.

关键词： spectrum sensing cognitive radio deep learning adversarial attack autoencoder defense

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Widely Tunable Middle Infrared Optical Parametric Oscillator Pumped by the Q-Switched Ho：GdVO_4 Laser

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Chinese Physics Letters 2018年第5期35卷 48-51页

作者： Xiao-Ming Duan Cheng Chen Yu Ding Bao-Quan Yao Yue-Zhu Wang National Key Laboratory of Tunable Laser Technology Harbin Institute of Technology Harbin 150001 Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308

We demonstrate a middle infrared ZnGeP_2 optical parametric oscillator pumped by the Q-switched Ho：GdVO_4 laser. When the incident Ho pump power is 4.12 W, the maximum average output power of the ZGP-OPO laser is 2.05 W, corresponding to a slope efficiency of 74.6%. The ZGP-OPO laser produces 4.2 ns mid-infrared pulses at a pulse repetition rate of 5 kHz. In addition, we obtain 0.8 um of tunable range for the signal wave and 2.1 um of tunable range for the idler wave.

关键词： Ho OPO Widely Tunable Middle Infrared Optical Parametric Oscillator Pumped by the Q-Switched Ho:GdVO4 Laser

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Passive satellite localization using TDOA/FDOA/AOA measurements

Passive satellite localization using TDOA/FDOA/AOA measureme...

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Bin, Yang Zheng Lei, Wang Qun, Chen Pei Nan, Lu An Science and Technology on Communication Information Security Control Laboratory Jiaxing Zhejiang 314033 China

ISBN: (纸本)9781479916603

Dual satellite TDOA/FDOA localization scheme has problems of multiple ambiguous solutions and poor localization results in the sub-satellite area along the dual satellite baseline direction. Passive localization using TDOA and FDOA measurements usually does not have direct solution and requires numerical methods to determine the emitter's geolocation. We suggest a satellite passive localization method fusing the TDOA/FDOA/AOA measurements based on particle swarm optimization(PSO), which provides an evolutionary computation method to solve the nonlinear optimal problem. To be more close to the practical use, the WGS-84 earth model is adopted in this work. However, PSO can not be directly applied to geolocation problem due to the nonlinear constraint of earth surface. We examined a PSO based method which let the particle swarm fly on the earth surface. Computer simulation results show the effectiveness of the new localization method. © 2013 IEEE.

关键词： Time difference of arrival

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Probing spatial effects on quantum reservoir sensing in quasi-one-dimensional Bose-Einstein condensates

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Physical Review Research 2025年第2期7卷 023100-023100页

作者： Lan Xu Ya Yang Yunlan Zuo Abdelâali Boudjemâa Qing-Shou Tan School of Physics and Chemistry Hunan First Normal University Changsha 410205 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of the Ministry of Education Hunan Normal University Changsha 410081 China Department of Physics Faculty of Exact Sciences and Informatics and Laboratory of Mechanics and Energy Hassiba Benbouali University of Chlef P.O. Box 78 02000 Chlef Algeria Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang 414000 China

We explore the impact of spatial separation between two sensor qubits on quantum sensing performance within a quasi-one-dimensional Bose-Einstein condensate reservoir. By analyzing the quantum Fisher information and quantum signal-to-noise ratio for sensor qubits initialized in both entangled and product states, we reveal distinct behaviors in estimating key reservoir parameters, including spectral density coupling strength and temperature. For coupling strength estimation, smaller spatial distances between the sensor qubits enhance sensing precision. In contrast, for temperature sensing, the spatial separation emerges as a valuable resource, enabling significant improvements in estimation accuracy. Additionally, we discover that product states may outperform entangled states for parameter estimation in quantum reservoir sensing. These findings provide insights into leveraging spatial configurations and state initialization for optimizing quantum sensing protocols in quantum reservoirs.

关键词： Quantum entanglement

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PSO Based Passive Satellite Localization Using TDOA and FDOA Measurements

PSO Based Passive Satellite Localization Using TDOA and FDOA...

引用

2011 10th IEEE/ACIS International Conference on Computer and information science, ICIS 2011

作者： Bin, Yang Zheng Yan, Qiu Nan, Lu An Science and Technology on Communication Information Security Control Laboratory Jiaxing Zhejiang 314033 China

ISBN: (纸本)9780769544014

Passive localization using TDOA and FDOA measurements usually does not have direct solution and requires numerical methods to determine the emitter's geolocation. We examine a satellite passive localization method based on particle swarm optimization(PSO), which provides an evolutionary computation method to solve the nonlinear optimal problem. However, PSO can not be straightforwardly applied to satellite passive localization because it is a constrained nonlinear optimal problem when using the constraint condition of the earth surface. We suggest "flying" the particle swarm directly using the earth surface constraint. Computer simulation results show the effectiveness of the new localization method. © 2011 IEEE.

关键词： Time difference of arrival

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Reinforcement learning assisted non-reciprocal optomechanical gyroscope

arXiv

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arXiv 2025年

作者： Tan, Qing-Shou Jiao, Ya-Feng Zuo, Yunlan Xu, Lan Liao, Jie-Qiao Kuang, Le-Man Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang414000 China School of Electronics and Information Zhengzhou University of Light Industry Zhengzhou450001 China Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou450002 China School of Physics and Chemistry Hunan First Normal University Changsha410205 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

We propose a novel optomechanical gyroscope architecture based on a spinning cavity optomechanical resonator (COM) evanescently coupled to a tapered optical fiber without relying on costly quantum light sources. Our study reveals a striking dependence of the gyroscope’s sensitivity on the propagation direction of the driving optical field, manifesting robust quantum non-reciprocal behavior. This non-reciprocity significantly enhances the precision of angular velocity estimation, offering a unique advantage over conventional gyroscopic systems. Furthermore, we demonstrate that the operational range of this non-reciprocal gyroscope is fundamentally governed by the frequency of the pumping optical field, enabling localized sensitivity to angular velocity. Leveraging the adaptive capabilities of reinforcement learning (RL), we optimize the gyroscope’s sensitivity within a targeted angular velocity range, achieving unprecedented levels of precision. These results highlight the transformative potential of RL in advancing high-resolution, miniaturized optomechanical gyroscopes, opening new avenues for next-generation inertial sensing technologies. © 2025, CC BY.

关键词： Gyroscopes

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Deterministic generation of non-classical mechanical states in cavity optomechanics via reinforcement learning

arXiv

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arXiv 2025年

作者： Liu, Yu-Hong Tan, Qing-Shou Kuang, Le-Man Liao, Jie-Qiao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang414000 China Institute of Interdisciplinary Studies Hunan Normal University Changsha410081 China

Non-classical mechanical states, as vital quantum resources for exploring macroscopic quantum behavior, have wide applications in the study of the fundamental quantum mechanics and modern quantum technology. In this work, we propose a scheme for deterministically generating non-classical mechanical states in cavity optomechanical systems. By working in the eigen-representation of the nonlinear optomechanical systems, we identify the carrier-wave resonance conditions and seek for the optimal driving pulses for state preparations. Concretely, we employ the reinforcement learning method to optimize the pulsed driving fields, effectively suppressing the undesired transitions induced by both the pulsed driving fields and dissipations. This approach enables the high-fidelity preparation of phononic Fock states and superposed Fock states in the single-resonator optomechanical systems, as well as two-mode entangled states in the two-resonator optomechanical systems. The statistical properties of the generated states are also examined. Our results open a way for quantum state engineering in quantum optics and quantum information science via reinforcement learning. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum optics

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

引用

IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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