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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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VLC-enabled UAV Network for IoT with Co-channel Interference: Joint Spatial Deployment and Resource Allocation

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IEEE Internet of Things Journal 2025年

作者： Liu, Jiaji Yang, Fang An, Nan Song, Jian Han, Zhu State Key Laboratory of Widegap Semiconductor Optoelectronic Materials and Technologies Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China 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

Unmanned aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this paper, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Nonsaturated large magnetoresistance and transport dynamics in a n-doped cryogenic WSe2 field-effect device

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Physical Review B 2025年第12期111卷 125307-125307页

作者： Wei-Chen Lin Ching-Chen Yeh Jia-Zhu Zou Nhat Anh Nguyen Phan Inayat Uddin Hai Yen Le Thi Kenji Watanabe Takashi Taniguchi Gil-Ho Kim Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Physics National Taiwan University Taipei 106 Taiwan Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon 164 Republic of Korea Sungkyunkwan Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Interest in the two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) continues to intensify, driven by their suitable band gaps to supplant silicon as next-generation semiconductor materials. Among various TMDs, tungsten diselenide (WSe2) is renowned for its superior electrical properties in carrier density and mobility under ambient conditions. Despite its notable attributes, the behavior of monolayer WSe2 in the electron-doped regime under cryogenic conditions remains largely uncharted, particularly concerning its magnetotransport properties. In this study, we reveal the transport mechanisms of monolayer WSe2 from high temperatures down to the cryogenic regime. As evident by Efros–Shklovskii variable-range hopping (E-S VRH) in the cryogenic regime, strong Coulomb interactions arise between electrons. Above 8 K, an uncommon nonsaturated quadratic large magnetoresistance (MR) can be explained by the wave-function shrinkage model, which is consistent with the E-S VRH transport mechanism. Notably, the nonsaturated quadratic large MR shows a magnitude up to 1740% at 13 T. These findings underscore the potential applications for monolayer WSe2 in cryogenic field-effect devices, magnetic sensors, and memory devices and mark a significant advance in magnetotransport research.

关键词： Monolayers

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Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction

arXiv

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arXiv 2025年

作者： Zhang, Quan Qi, Yuxin Tang, Xi Yuan, Rui Lin, Xi Zhang, Ke Yuan, Chun Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Pseudo-label learning methods have been widely applied in weakly-supervised temporal action localization. Existing works directly utilize weakly-supervised base model to generate instance-level pseudo-labels for training the fully-supervised detection head. We argue that the noise in pseudo-labels would interfere with the learning of fully-supervised detection head, leading to significant performance leakage. Issues with noisy labels include:(1) inaccurate boundary localization;(2) undetected short action clips;(3) multiple adjacent segments incorrectly detected as one segment. To target these issues, we introduce a two-stage noisy label learning strategy to harness every potential useful signal in noisy labels. First, we propose a frame-level pseudo-label generation model with a context-aware denoising algorithm to refine the boundaries. Second, we introduce an online-revised teacher-student framework with a missing instance compensation module and an ambiguous instance correction module to solve the short-action-missing and many-to-one problems. Besides, we apply a high-quality pseudo-label mining loss in our online-revised teacher-student framework to add different weights to the noisy labels to train more effectively. Our model outperforms the previous state-of-the-art method in detection accuracy and inference speed greatly upon the THUMOS14 and ActivityNet v1.2 benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China 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 underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Unavoidable multilevel biaxial symmetry breaking in chiral hybrid liquid crystals

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Physical Review E 2025年第4期111卷 045410-045410页

作者： Jin-Sheng Wu Marina Torres Lázaro Souvik Ghosh Haridas Mundoor Henricus H. Wensink Ivan I. Smalyukh Department of Physics and Chemical Physics Program University of Colorado Boulder Colorado 80309 USA Laboratoire de Physique des Solides–UMR 8502 Université Paris-Saclay and CNRS 91405 Orsay France International Institute for Sustainability with Knotted Chiral Meta Matter Hiroshima University Higashihiroshima Japan Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program and Soft Materials Research Center University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado Boulder Colorado 80309 USA

Chiral nematic or cholesteric liquid crystals (LCs) are fluid mesophases with long-ranged orientational order featuring a quasilayered periodicity imparted by a helical director configuration but lacking long-range positional order. Doping molecular cholesteric LCs with strongly anisotropic uniaxial colloidal particles adds another level of complexity because of the interplay between weak surface-anchoring boundary conditions and bulk-based elastic distortions near the particle-LC interface. Using cylindrical colloidal disks and rods with different geometric shapes and surface conditions, we demonstrate that these colloidal inclusions generically exhibit biaxial orientational probability distributions which may impart anomalously strong local biaxiality onto the hybrid cholesteric LC structure. Unlike nonchiral hybrid molecular-colloidal LCs, where biaxial order emerges only at critical colloid volume fractions exceeding some uniaxial-biaxial transition value, the orientational probability of the colloidal inclusions immersed in chiral nematic hosts is unambiguously biaxial even at infinite dilution. We demonstrate that the colloids induce local biaxial perturbations within the molecular orientational order of the LC host medium which strongly enhances the weak but native biaxial order of chiral nematic LC induced by the chiral symmetry breaking of the director field. With the help of analytical modeling and computer simulations based on the Landau–de Gennes free energy of the host LC around the colloids, we rationalize the observed multilevel biaxial order and conclude that it is not only unavoidable but also strongly enhanced compared to both achiral hybrid LCs and purely molecular cholesteric LCs.

关键词： Cholesteric liquid crystals

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A Multi-Step Multivariate Fuzzy-based Time Series Forecasting on Internet of Things Data

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IEEE Internet of Things Journal 2025年

作者： Bitencourt, Hugo Vinicius Lucas, Patri-Cia de Oliveira Orang, Omid Silva, Petronio C. L. Guimaraes, Frederico Gadelha Universidade Federal de Minas Gerais -UFMG Graduate Program in Electrical Engineering MG Belo Horizonte31270-901 Brazil Federal Institute of Northern Minas Gerais IFNMG MG Salinas Brazil Universidade Federal de Minas Gerais -UFMG Computer Science MG Belo Horizonte31270-901 Brazil Federal Institute of Northern Minas Gerais IFNMG MG Januaria Brazil Universidade Federal de Minas Gerais -UFMG Department of Computer Science MG Belo Horizonte Brazil

Multi-step ahead time series forecasting is essential in Internet of Things (IoT) applications in smart cities and smart homes to make accurate future predictions and precise decision-making. Thus, this study introduces a novel multiple-input single-output (MISO) forecasting method called Multi-Step Embedding-based Fuzzy Time Series (MS-EFTS), designed to predict high-dimensional non-stationary time series data. As a first-order approach, it employs a direct strategy that integrates an embedding transformation with a weighted multivariate FTS (WMVFTS) model. This combination allows for effective predictions over long-term horizons within low-dimensional, learned continuous representations. The effectiveness of the proposed MS-EFTS is assessed using three high-dimensional IoT time series in this investigation. The obtained results showcase the superior performance of the proposed method compared to some deep learning forecasting methods, including LSTM, BiLSTM, TCN, and CNN-LSTM, in terms of accuracy, parsimony, and efficiency. © 2014 IEEE.

关键词： Smart homes

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Air-liquid microfluidics-integrated surface-enhanced Raman spectroscopy for selective molecular adsorption and detection to achieve bacterial discrimination

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Biosensors and Bioelectronics 2025年 285卷 117576页

作者： Ku, Chi-Yao Chiang, Yu-Wei Hsu, Huai-Yuan Cheng, Ho-Wen Chen, Ko-Lun Han, Yin-Yi Wang, Juen-Kai Wang, Yuh-Lin Huang, Nien-Tsu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei Taiwan Department of Anesthesiology National Taiwan University Hospital Taipei100229 Taiwan Department of Trauma National Taiwan University Hospital Taipei100229 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei Taiwan Center for Atomic Initiative for New Materials National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Bacterial discrimination is crucial for accurate microbiological diagnosis and timely antibiotic treatment. Surface-enhanced Raman spectroscopy (SERS) is an ideal technique due to its non-invasive, label-free molecular sensing capabilities. By analyzing bacterial supernatants, containing various purine derivatives, SERS can differentiate bacterial species based on their unique spectral distributions. However, the same bacterial species with different antibiotic resistance may secrete similar purine derivatives, differing only slightly in composition. Furthermore, each purine derivative may have a different molecular affinity to the SERS substrate, making it difficult to distinguish the exact molecular ratio. To improve SERS-based bacterial discrimination, we propose an air-liquid microfluidics-integrated SERS system (ALM-SERS) that selectively adsorbs and detects bacterial secretions. By taking features of precise microdroplet manipulation and contact area from microfluidics and features of selective molecular adsorption and fingerprints characterization from the SERS technique, we successfully perform a "sequential molecular adsorption" strategy to address the signal interference of complicated molecular mixtures in existing SERS methods. As a proof of concept, we first evaluate the molecular affinity of purine derivatives and then demonstrate the competitive analyte adsorption using adenine/cytosine and hypoxanthine/uracil sample mixtures. Finally, we tested six bacterial supernatants, including two Gram types and four strains with identical taxonomy but differing antibiotic resistance. Another six clinically isolated bacterial samples with different antibiotic resistance were also applied. Our results showed that a successful bacterial discrimination between similar species was not possible using a single SERS spectrum. In summary, the ALM-SERS system offers a powerful approach for bacterial discrimination, even when spectral differences are subtle. Beyond microb

关键词： Raman spectroscopy

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Topology Rewiring Strategies to construct robust scale-free medical Internet of Things Networks

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Simulation Modelling Practice and Theory 2025年 142卷

作者： Khan, Muhammad Awais Javaid, Nadeem Alrajeh, Nabil Bouk, Safdar Hussain Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan ComSens Lab International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Old Dominion University 5115 Hampton Blvd NorfolkVA23529 United States

The Internet of Things (IoT) network topologies are now most commonly impacted by cyberattacks. The scale-free network topologies have demonstrated great robustness against random attacks by preserving the connectedness of the nodes. The scale-free network topologies’ susceptibility to malicious attacks, however, is a significant worry. It is due to the significance of the scale-free networks in different fields of life like medical, transportation, education, agriculture, etc. Also, high-degree node removal diminishes the network's resiliency and compromises the connection of the majority of nodes. In this study, we offer several rewiring techniques for building scale-free, reliable Medical Internet of Things (MIoT) networks that can withstand malicious attacks. Initially, the scale-free MIoT network's performance optimization is ensured using a heuristic algorithm known as the Great Deluge Algorithm (GDA). Then, four rewiring strategies are formulated. The initial approach is degree dissortativity, which rewires the network if all nodes have high maximum connectivity to other neighbors with a similar degree. For the second strategy, we introduced a degree difference operation based on degree dissortativity to ensure that the edges that are connected possess low dissortativity and degree difference. Meanwhile, the remaining two strategies take into account the node load bound and enhanced GDA to increase network robustness. The performance of the proposed rewiring strategies is validated through simulations. The results prove that the proposed strategies increase network robustness by up to 25% compared to Hill Climbing (HC) and Simulated Annealing (SA). Additionally, the strategies show great success in improving network connectivity and graph density. However, their computational time is higher compared to HC and SA. © 2025 Elsevier B.V.

关键词： Network topology

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A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

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