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arXiv 2025年

作者： Guo, Yixuan Xia, Shuaifan Xiong, Mingliang Liu, Qingwen Zhou, Shengli Fang, Wen Jiang, Qingwei Yan, Gang Mu, Jiangchuan The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The College of Electronics and Information Engineering Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China The Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States The School of Physics Science and Engineering Tongji University Shanghai200092 China

To address the challenge of complex beam control in traditional multiple-input multiple-output (MIMO) systems, research has proposed establishing adaptive beam alignment by utilizing retro-directive antenna (RDA) arrays to create echo resonance between the base station (BS) and user equipment (UE), thereby reducing system computational load. However, in conventional resonant beam systems (RBS), the use of the same frequency for uplink and downlink inevitably leads to echo interference issues. Therefore, this paper proposes an innovative design for an resonance beam-based integrated sensing and communication (RB-ISAC) system to achieve efficient passive sensing and bidirectional communication. In this system, the UE does not actively transmit signals but instead relies on a passive phase conjugation and frequency conversion structure to separate the uplink and downlink carrier frequencies. Additionally, through effective compensation for signal propagation loss, resonance is achieved after multiple iterations, at which point the beam’s field distribution becomes a low-diffraction-loss, high-focusing pattern, automatically aligning the transmitter with the receiver. This enables high-precision passive positioning while facilitating uplink and downlink communication. Simulation results demonstrate that the proposed system can achieve resonance after multiple iterations, and can support uplink and downlink communication within a range of 5 meters while achieving passive direction of arrival (DOA) estimation with an error of less than 2 degrees. Copyright © 2025, The Authors. All rights reserved.

关键词： Directive antennas

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Robust Prescribed Performance Control and Adaptive Learning for the Longitudinal Dynamics of Fixed-Wing UAVs

Robust Prescribed Performance Control and Adaptive Learning ...

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International Conference on systems and Control (ICSC)

作者： S. Tzeranis P. S. Trakas X. Papageorgiou K. J. Kyriakopoulos C. P. Bechlioulis Control Systems Lab School of Mechanical Engineering National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (数字)9781665465076

ISBN: (纸本)9781665465083

The objective of this work is to simultaneously control and identify the nonlinear longitudinal dynamics of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs). The main difficulty in this endeavor lies in the satisfaction of the Persistence of Excitation (PE) condition, which eventually ensures accurate learning. Towards this direction, our key components comprise Radial Basis Function - Neural Networks (RBF-NNs), which are suitable mathematical models for universal function approximation, alongside with: i) the recently developed Dynamic Regression Extension and Mixing (DREM) technique; a new procedure for designing parameter estimators with enhanced performance, as well as ii) a novel control design for the longitudinal UAV dynamics utilizing the Prescribed Performance Control (PPC) methodology, which enables robust trajectory tracking with predetermined transient and steady state quality, even in the presence of model uncertainties.

关键词： Uncertainty Trajectory tracking Neural networks Autonomous aerial vehicles Mathematical models Steady-state Object recognition

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UNeLF: Unconstrained Neural Light Field for Self-Supervised Angular Super-Resolution

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zhao, Mingyuan Sheng, Hao Chen, Rongshan Cong, Ruixuan Cui, Zhenglong Yang, Da Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Data Science and Intelligent Computing Laboratory Hangzhou International Innovation Institute Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Compared to supervised learning methods, self-supervised learning methods address the domain gap problem between light field (LF) datasets collected under varying acquisition conditions, which typically leads to decreased performance when differences exist in the distribution between the training and test sets. However, current self-supervised light field angular super-resolution (LFASR) techniques primarily focus on exploiting discrete spatial-angular features while neglecting continuous LF information. In contrast to previous work, we propose a self-supervised unconstrained neural light field (UNeLF) to continuously represent LF for LFASR. Specifically, any LF can be described as the camera pose for each sub-aperture image (SAI) and the two-plane that captures these SAIs. To describe the former, we introduce a SAIs-dependent pose optimization method to solve the issue that arises from the narrow baseline of most LF data, which hinders robust camera pose estimation. This mechanism reduces the number of trainable camera parameters from a quadratic to a constant scale, thereby alleviating the complexity of joint optimization. For the latter, we propose a novel adaptive two-plane parameterization strategy to determine the two-plane that captures these SAIs, facilitating refocusing. Finally, we jointly optimize the camera parameters, near-far planes and neural light field, efficiently mapping each adaptive two-plane parameterized ray to its correspondence color in a continuous manner. Comprehensive experiments demonstrate that UNeLF achieves faster training and inference with fewer computational resources while exhibiting superior performance on both synthetic and real-world datasets. © 1991-2012 IEEE.

关键词： Self-supervised learning

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels

arXiv

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arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. The Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg The Microwave and Fiber Optics Laboratory Greece The Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Zografou15780 Greece

Low-cost reconfigurable intelligent surfaces (RISs) are being considered as promising physical-layer technology for next-generation wireless networks due to their ability to re-engineer the propagation environment by tuning their elements. Although RIS-aided systems have many advantages, one of their major bottlenecks is that they can provide coverage only in front of the surface. Fortunately, simultaneous transmitting and reflecting surface (STARS)-assisted systems have emerged to fill this gap by providing 360◦ wireless coverage. In parallel, full-duplex (FD) communication offers a higher achievable rate through efficient spectrum utilization compared to the half-duplex (HD) counterpart. Moreover, two-way/bi-directional communications in an FD system can further enhance the system’s spectral efficiency. Hence, in this paper, we propose a STARS-enabled massive MIMO deployment in an FD two-way communication network for highly efficient spectrum utilization, while covering the dead zones around the STARS. This model enables simultaneous information exchange between multiple nodes, while potentially doubling the spectral efficiency (SE). By invoking the use-and-then-forget (UaTF) combining scheme, we derive a closed-form expression for an achievable SE at each user of the system considering both uplink and downlink communications based on statistical channel state information (CSI), while also accounting for imperfect CSI and correlated fading conditions. Moreover, we formulate an optimization problem to obtain an optimal passive beamforming matrix design at the STARS that maximizes the sum achievable SE. The considered problem is non-convex and we propose a provably-convergent low-complexity algorithm, termed as projected gradient ascent method (ProGrAM), to obtain a stationary solution. Extensive numerical results are provided to establish the performance superiority of the FD STARS-enabled system over the HD STARS-enabled and FD conventional RIS (cRIS)-enabled coun

关键词： Digital television

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Learning of Nash Equilibria in Risk-Averse Games

Learning of Nash Equilibria in Risk-Averse Games

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American Control Conference (ACC)

作者： Zifan Wang Yi Shen Michael M. Zavlanos Karl H. Johansson Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology and also with Digital Futures Stockholm Sweden Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically, the agents adopt the conditional value at risk (CVaR) as a risk measure with possibly different risk levels. To solve this problem, we propose a first-order risk-averse leaning algorithm, in which the CVaR gradient estimate depends on an estimate of the Value at Risk (VaR) value combined with the gradient of the stochastic cost function. Although estimation of the CVaR gradients using finitely many samples is generally biased, we show that the accumulated error of the CVaR gradient estimates is bounded with high probability. Moreover, assuming that the risk-averse game is strongly monotone, we show that the proposed algorithm converges to the risk-averse Nash equilibrium. We present numerical experiments on a Cournot game example to illustrate the performance of the proposed method.

关键词： Reactive power Costs Estimation Games Nash equilibrium Numerical simulation Cost function

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Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

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computers, Materials & Continua 2022年第10期73卷 1449-1462页

作者： Wei Song Haolin Wang Xinping Zhang Jianxin Xia Tongmu Liu Yuxi Shi School of Information and Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourceGuangzhou510300China National Language Resource Monitoring&Research Center of Minority Languages Beijing100081China School of Ocean Science China University of GeosciencesBeijing100191China Department of Buoy Engineering South China Sea Marine Survey and Technology CenterGuangzhou510300China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew Jersey07102USA

Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and *** application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of *** existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly *** order to improve the segmentation performance of underwater mineral images,this paper uses the Pix2PixHD(Pixel to Pixel High Definition)algorithm based on Conditional Generative Adversarial Network(CGAN)to segment deep-sea mineral *** model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks,and multiple multi-scale discriminators with same network structures but different input pictures to generate highquality *** test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other *** evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net *** is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.

关键词： Deep-sea mineral image segmentation generative adversarial network

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In-situ synthesis of zeolitic imidazolate framework-8 on an optical microbubble cavity for enhanced ethanol sensing

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Optics Express 2025年第11期33卷 23065-23074页

作者： Zhi, Yanyan Liu, Li Huang, Yan Sun, Li-Peng Wang, Wei Li, Jie Zhang, Jiejun Yao, Jianping Guan, Bai-Ou Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Jinan University Guangzhou510632 China College of Physics & Optoelectronic Engineering Jinan University Guangzhou510632 China Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

Optical whispering-gallery-mode microcavities have been applied for ultra-sensitive detection of physical parameters and nano-scale analytes due to strong light-matter interactions. Surface treatments to enhance mode confinement or increase analyte interaction with the optical field are effective methods to improve the sensing performance. In this work, an optical microbubble cavity fabricated via heat-and-pull and arc discharge techniques is functionalized with zeolitic imidazolate framework-8 (ZIF-8), a subclass of metal-organic frameworks (MOFs), for concentration sensing of ethanol solutions. The bubble-shaped structure forms an internal microfluidic channel to sustain the analyte solution, which provides an excellent platform for trace detection. By growing a multilayer of MOFs on the inner surface of the microcavity, the sensitivity is significantly enhanced, with distinct behaviors observed when detecting ethanol solution concentrations lower or higher than 0.1%. Compared to a pure silica microbubble cavity, the sensitivity is enhanced by 47 times and 7 times at lower and higher concentrations, respectively. The knowledge gained on sensitivity enhancement of microcavities induced by surface functionalization will guide the combinations of novel materials and optical sensors for superior detection demands. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Microcavities

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Versatile and Efficient Dual-Range Frequency Shifts by Intracavity Epsilon-Near-Zero Nanolayers

Versatile and Efficient Dual-Range Frequency Shifts by Intra...

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IEEE Photonics Conference (IPC)

作者： Jiaye Wu Gang Wang Marco Clementi Ji Zhou Chenxingyu Huang Xuanyi Liu Hongyan Fu Qian Li Camille-Sophie Brès École Polytechnique Fédérale de Lausanne Photonic Systems Laboratory (PHOSL) Lausanne Switzerland Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electronic and Computer Engineering Peking University Shenzhen China

ISBN: (数字)9798350361957

ISBN: (纸本)9798350361964

We experimentally demonstrate for the first time, the efficient input-dependent dual-range frequency shifts of 279.73 GHz and 3.63 THz in a mode-locked fiber laser with an intracavity epsilon-near-zero element. Real-time observation reveals that the presented frequency shifts are non-adiabatic processes, unlike the reported extracavity settings.

关键词： Time-frequency analysis Laser mode locking Switches Frequency conversion Real-time systems Photonics Fiber lasers

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Propagation Time Estimation of Transmission Lines using Time Domain Reflectometry

Propagation Time Estimation of Transmission Lines using Time...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Abu Bakr Pengwah Reza Razzaghi Lachlan L. H. Andrew Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia School of Computing and Information University of Melbourne Melbourne Australia

This paper proposes a novel time domain reflectome-try method to estimate the propagation time of transmission lines in medium-voltage (MV) networks. In the proposed method, a single travelling wave detector (TWD) unit is installed at the zone substation where a switching action is performed. The subsequent recorded transients are then matched to the arrival times and amplitudes of expected transients corresponding to characteristic paths in the network. A combinatorial approach is proposed to mitigate interference from multiple-paths reflected transients. The matching method generates a list of possible propagation times from which the one that best fits the measurements is selected. The performance of the proposed method is evaluated on multiple branched networks. The results exhibit accurate estimation of propagation times for test networks.

关键词： Power transmission lines Substations Estimation Interference Switches Reflectometry Transmission line measurements

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