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Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

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Earth and Planetary Physics 2023年第3期7卷 407-413页

作者： Mao Zhang GaoPeng Lu HaiLiang Huang ZhengWei Cheng YaZhou Chen Steven A.Cummer JiaYi Zheng JiuHou Lei School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China National Key Laboratory on Electromagnetic Environment Effects Army Engineering UniversityShijiazhuang CampusShijiazhuang 050003China Electrical and Computer Engineering Department Duke UniversityDurhamNC 27708USA

Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long *** this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning *** differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),***,the automatic method is capable of recognizing higher harmonic tweek *** evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.

关键词： machine learning method tweek atmospherics reflection height D-region ionosphere

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2 μm optical frequency comb generation via optical parametric oscillation from a lithium niobate optical superlattice box resonator

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Photonics Research 2022年第2期10卷 509-515页

作者： Xiaohan Wang Kunpeng Jia Mengwen Chen Shanshan Cheng Xin Ni Jian Guo Yihao Li Huaying Liu Liyun Hao Jian Ning Gang Zhao Xinjie Lv Shu-Wei Huang Zhenda Xie Shi-Ning Zhu National Laboratory of Solid State Microstructures School of Electronic Science and EngineeringSchool of PhysicsCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Department of Electrical Computer and Energy EngineeringUniversity of Colorado BoulderBoulderColorado 80309USA

Optical parametric oscillators(OPOs) can downconvert the pump laser to longer wavelengths with octave separation via χ^((2)), which is widely used for laser wavelength extension including mid-infrared(MIR) *** a process can be integrated in monolithic resonators, being compact and low in threshold. In this work, we show that the monolithic χ^((2))mini-OPO can also be used for optical frequency comb generation around 2096 nm and enters the boundary of MIR range. A new geometry called an optical superlattice box resonator is developed for this realization with near-material-limited quality factor of 4.0 × 10^(7). Only a continuous-wave near-infrared pump laser is required, with OPO threshold of 80 mW and output power up to 340 mW. Revival temporal profiles are measured at a detectable repetition frequency of 1.426 GHz, and narrow beat note linewidth of less than 10 Hz shows high comb coherence. These results are in good agreement with our simulation for a stable comb generation. Such an OPO-based comb source is useful for carbon dioxide sensing or the mine prospect applications and can be generalized to longer MIR wavelengths for general gas spectroscopy.

关键词： resonator optical pump

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Distributed Bilevel Optimization via Adaptive Penalization with Time-Scale Separation

arXiv

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arXiv 2024年

作者： Niu, Youcheng Xu, Jinming Sun, Ying Chai, Li Chen, Jiming State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electrical Engineering and Computer Science Pennsylvania State University PA16802 United States

This paper studies a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimations involving computationally expensive Hessian information. To address this, we propose an equivalent constrained reformulation by treating the inner-level subproblem as an inequality constraint, and introduce an adaptive penalty function to properly penalize both inequality and consensus constraints based on subproblem properties. Moreover, we propose a loopless distributed algorithm, AHEAD, that employs multiple-timescale updates to solve each subproblem asymptotically without requiring Hessian information. Theoretically, we establish convergence κ4 rates of O((1−ρ)2K1/3 ) for nonconvex-strongly-convex cases and κ2 O((1−ρ)2K2/3 ) for distributed min-max problems. Our analysis shows the clear dependence of convergence performance on bilevel heterogeneity, the adaptive penalty parameter, and network connectivity, with a weaker assumption on heterogeneity requiring only bounded first-order heterogeneity at the optimum. Numerical experiments validate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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NeuroFusion-Trans: A Novel Transformer-Based EEG-EMG Fusion Model for Assistive Robotics

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IEEE Access 2025年 13卷 103245-103263页

作者： Tipu Sultan Guangping Liu Pascal Sikorski Samah Alshathri Walid El-Shafai Madi Babaiasl Aerospace and Mechanical Engineering Department Saint Louis University St. Louis MO USA Computer Science Department Saint Louis University St. Louis MO USA Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Computer Science Department Automated Systems and Computing Laboratory (ASCL) Prince Sultan University Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

User intent recognition from multimodal neurophysiological signals, particularly electroencephalography (EEG) and electromyography (EMG), is critical for enhancing human-machine interaction in assistive robotics. Recent advances in neurophysiological signal processing have enabled enhanced user intent recognition for assistive robotics and human-machine interfaces. However, achieving high accuracy and real-time adaptability in electromyography (EMG) and electroencephalography (EEG)-based gesture recognition remains challenging due to temporal misalignment, weak cross-modality fusion, and lack of adaptive learning. This paper proposes NeuroFusion-Trans, a novel transformer-based framework that improves EEG-EMG gesture recognition by improving temporal resolution, using cross-modality attention, and integrating adaptive online learning. Temporal resolution enhancement ensures dynamic EEG-EMG synchronization for improved signal alignment. The cross-modality attention mechanism captures interdependencies between EEG and EMG signals, leading to more accurate intent classification. Adaptive online learning enables real-time personalization by dynamically adjusting to user-specific variations. The model is evaluated on two publicly available EEG-EMG upper-limb gesture datasets: Dataset 1 (5,296 for training, 1,324 for validation) and Dataset 2 (5,276 for training, 1,304 for validation). NeuroFusion-Trans achieves state-of-the-art performance, with an accuracy of 97% and 96% and Cohen’s Kappa of 0.97 and 0.95 after online adaptation, significantly outperforming baseline models such as CNN-LSTM, GRU, and LSTMNet. Ablation studies reveal that removing the cross-modality attention mechanism reduces accuracy by 6.1%, underscoring its importance in exploiting the EEG-EMG dependencies. Turning off synchronization leads to a 6.7% performance drop, demonstrating the necessity of real-time learning for robust intent recognition. Furthermore, NeuroFusion-Trans enhances EEG-EMG synchr

关键词： Brain modeling Electromyography Electroencephalography Adaptation models Real-time systems Synchronization Accuracy Transformers Robots Gesture recognition

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A 140-GHz FMCW TX/RX-Antenna-Sharing Transceiver With Low-Inherent-Loss Duplexing and Adaptive Self-Interference Cancellation

A 140-GHz FMCW TX/RX-Antenna-Sharing Transceiver With Low-In...

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作者： Chen, Xibi Yi, Xiang Khan, Muhammad Ibrahim Wasiq Li, Xingcun Chen, Wenhua Zhu, Jianfeng Yang, Yang Kolodziej, Kenneth E. Monroe, Nathan M. Han, Ruonan Department of Electrical Engineering and Computer Science CambridgeMA02139 United States South China University of Technology School of Microelectronics Guangzhou511442 China Tsinghua University Department of Electronic Engineering Beijing100084 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Mit Lincoln Laboratory LexingtonMA02421 United States

This article presents a 140-GHz frequency-modulated continuous-wave (FMCW) radar transceiver featuring transmit/receive (TX/RX) antenna sharing that address a TX/RX beam misalignment problem when large-aperture lenses/mirrors/reflectarrays are used for pencil beam forming. A full-duplexing technique based on circular polarization and geometrical symmetry is applied to mitigate the 3 dB + 3 dB insertion loss inherent to conventionally adopted directional couplers, while still maintaining high TX-to-RX isolation. In addition, a self-adaptive self-interference cancellation (SIC) is implemented to suppress extra leakage due to antenna mismatch from a desired frontside radiation scheme. The TX/RX antenna sharing enables the pairing with a large 3-D printed planar lens and boosts the measured effective isotropic radiated power (EIRP) to 25.2 dBm. The measured total radiated power and minimum single-sideband noise figure (SSB NF) including antenna and duplexer losses are 6.2 dBm and 20.2 dB, respectively. The measured total TX-RX isolation is 33.3 dB under 14-GHz wide FMCW chirps. Based on a 65-nm complimentary metal-oxide-semiconductor (CMOS) technology, the chip has a die area of 3.1 mm2 and consumes 405 mW of dc power. Among all reported sub-THz transceivers with TX/RX antenna sharing, this work demonstrates the highest total radiated power and is the only work that has >30 dB of TX-RX isolation while mitigating the inherent 6 dB coupler loss. © 1966-2012 IEEE.

关键词： Radar antennas

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STAR-RIS Enhanced Covert Communication with Delay Constraint

STAR-RIS Enhanced Covert Communication with Delay Constraint

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Cheng, Beixi Hu, Jinsong Chen, Youjia Shu, Feng Chen, Zhizhang David Fuzhou University College of Physics and Information Engineering Fujian Fuzhou350108 China Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou350108 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University School of Information and Communication Engineering Haikou570228 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350345384

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results. © 2023 IEEE.

关键词： Convex optimization

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Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment

arXiv

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arXiv 2023年

作者： Dong, Xingrong Wu, Zhaoxian Ling, Qing Tian, Zhi The School of Computer Science and Engineering The Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen University Guangdong Guangzhou510006 China The Department of Electrical and Computer Engineering George Mason University FairfaxVA22030 United States

This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is often characterized by (adversarial) regret, which evaluates the quality of one-step-ahead decision-making when an environment incurs adversarial losses, and a sublinear regret bound is preferred. But we prove that, even with a class of state-of-the-art robust aggregation rules, in an adversarial environment and in the presence of Byzantine participants, distributed online gradient descent can only achieve a linear adversarial regret bound, which is tight. This is the inevitable consequence of Byzantine attacks, even though we can control the constant of the linear adversarial regret to a reasonable level. Interestingly, when the environment is not fully adversarial so that the losses of the honest participants are i.i.d. (independent and identically distributed), we show that sublinear stochastic regret, in contrast to the aforementioned adversarial regret, is possible. We develop Byzantine-robust distributed online momentum algorithms to attain such sublinear stochastic regret bounds for a class of robust aggregation rules. Numerical experiments corroborate our theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise Knowledge graphs Predictive models Fingerprint recognition Transformers

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Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Du, Ye Fu, Zehua Liu, Qingjie The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Hangzhou Innovation Institute Beihang University Hangzhou310051 China

Recent attention has been devoted to the pursuit of learning semantic segmentation models exclusively from image tags, a paradigm known as image-level Weakly Supervised Semantic Segmentation (WSSS). Existing attempts adopt the Class Activation Maps (CAMs) as priors to mine object regions yet observe the imbalanced activation issue, where only the most discriminative object parts are located. In this paper, we argue that the distribution discrepancy between the discriminative and the non-discriminative parts of objects prevents the model from producing complete and precise pseudo masks as ground truths. For this purpose, we propose a Pixel-Level Domain Adaptation (PLDA) method to encourage the model in learning pixel-wise domain-invariant features. Specifically, a multi-head domain classifier trained adversarially with the feature extraction is introduced to promote the emergence of pixel features that are invariant with respect to the shift between the source (i.e., the discriminative object parts) and the target (i.e., the non-discriminative object parts) domains. In addition, we come up with a Confident Pseudo-Supervision strategy to guarantee the discriminative ability of each pixel for the segmentation task, which serves as a complement to the intra-image domain adversarial training. Our method is conceptually simple, intuitive and can be easily integrated into existing WSSS methods. Taking several strong baseline models as instances, we experimentally demonstrate the effectiveness of our approach under a wide range of settings. © 2024, CC BY.

关键词： Semantic Segmentation

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Learning robotic cutting from demonstration: Non-holonomic DMPs using the Udwadia-Kalaba method

arXiv

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arXiv 2022年

作者： Straižys, Artūras Burke, Michael Ramamoorthy, Subramanian School of Informatics University of Edinburgh United Kingdom Department of Electrical and Computer Systems Engineering Monash University Australia

Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of nonholonomic constraints. We obtain the closed-form expression for the constraint forcing term using the Udwadia-Kalaba method. This approach offers a clean and practical solution for guaranteed constraint satisfaction at run-time. Further, the proposed analytical form of the constraint forcing term enables efficient trajectory optimization subject to constraints. We demonstrate the usefulness of this approach by showing how we can learn robotic cutting skills from human demonstration. © 2022, CC BY.

关键词： Robotics

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