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Ultrasensitive, online detection of endogenous H2S by a novel ratiometric fluorescent approach

Sensors and Actuators B: Chemical 2025年 439卷

作者： Li, Dan Li, Mao Huang, Zijia Li, Xiaolong Chen, Xiaoming Li, Na Zhao, Yong School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Northeastern University at Qinhuangdao Qinhuangdao066004 China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices School of Optoelectronic Science and Engineering South China Normal University Guangzhou510006 China College of Information Science and Engineering Northeastern University Shenyang110819 China

Endogenous hydrogen sulfide (H2S) is a crucial biological signaling molecule in the mammals. Numerous H2S sensing methods have been developed, yet there remains an urgent need for portable, online and sensitive detection of H2S in complex samples. Herein, a ratiometric fluorescent optical fiber sensor was designed for ultrasensitive, online detection of H2S, using nanohybrids electrostatically self-assembled from silicon quantum dots and gold nanoclusters (AuNCs@SiQDs nanohybrids). The nanohybrids, acting as a ratiometric fluorescent probe, exhibited a specific response to H2S. The mechanism involved static quenching effect of H2S on AuNCs and fluorescence resonance energy transfer between SiQDs and AuNCs. The sensor showed a good linear response to H2S concentration of 0.01 ∼ 100 μM, with an ultralow detection limit of 4.33 nM. The sensor was also successfully employed for rapid detection of H2S in real serum samples, without tedious preprocessing. This approach offers a portable and efficient technique for on-line ultrasensitive measurement of H2S in complex biological samples, as well as the determination of H2S-mediated biomolecules. This method boasts high sensitivity and selectivity, strong anti-interference capability, portability, practicality, and ease of production, making it significant for disease research and emergency analysis. © 2025 Elsevier B.V.

关键词： Sulfur dioxide

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Experimental demonstration of 8190-km long-haul semiconductor-laser chaos synchronization induced by digital optical communication signal

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光：科学与应用（英文版） 2025年第2期14卷 333-343页

作者： Anbang Wang Junli Wang Lin Jiang Longsheng Wang Yuncai Wang Lianshan Yan Yuwen Qin Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of TechnologyGuangzhou 510006China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan 030024China Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan 030024China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China Center for Information Photonics and Communications Southwest Jiaotong UniversityChengdu 610031China Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of TechnologyGuangzhou 510006China Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of TechnologyGuangzhou 510006China

Common-signal-induced synchronization of semiconductor lasers have promising applications in physical-layer secure transmission with high speed and compatibility with the current fiber ***,we propose an ultra-long-distance laser synchronization scheme by utilizing random digital optical communication signal as the common drive *** utilizing the long-haul optical coherent communication techniques,high-fidelity fiber transmission of the digital drive can be achieved and thus ultra-long-distance synchronization is *** were implemented with distributed feedback lasers injected by a random-digital phase-modulated drive *** show that high-quality synchronization can be achieved as the drive signal rate is larger than the laser relaxation frequency and the transmission bit error ratio is below a critical *** synchronization over 8191-km fiber transmission was experimentally *** to traditional common-signal-induced synchronization using analog drive signal such as chaos,the distance is increased by 8 times,and complicated hardware devices for channel impairment compensation are no longer *** addition,the proposed method does not sacrifice communication capacity like traditional methods which need a channel to transmit analog drive *** is therefore believed that this common-digital-signal induced laser synchronization paves a way for secure backbone and submarine transmission.

关键词： communication semiconductor synchronization signal digital chaos laser demonstration experimental induced

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Polarized Modulation for RIS-Aided Vehicular communication Networks

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IEEE Internet of Things Journal 2025年

作者： Shang, Pingping Xie, Longhui Li, Jun Feng, Li Hai, Han Xu, Binbin Li, Jie Yu, Ling Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes Guangzhou510006 China Donghua University School of Information Science and Technology Shanghai201620 China Maritime Communication Research Institute Wuhan430068 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

The reconfigurable intelligent surface (RIS) stands as an emergent technique poised to revolutionize future vehicular communication. In order to facilitate the application of RIS in vehicular communication, this paper proposes two effective RIS-aided schemes tailored for polarization modulation (PM) aiming to achieve remarkable spectral efficiency. Specifically, Scheme I proposes a novel index modulation (IM) aided PM-RIS framework that innovatively encodes information across three distinct dimensions, the indices of dual-polarized antennas (DP-AEs), PM symbols and the reflecting surface indices of the RIS. Scheme II employs a deep neural network method to analyze the channel state information (CSI) for transmit antenna selection, that enhances the reliability and interference immunity in dynamic conditions while reducing computational complexity. Furthermore, a stepwise estimation (SE) method is introduced at the detection stage, which significantly reduces the complexity associated with information decoding. The simulation results highlight the superiority of the proposed system. Compared with traditional PM systems, it demonstrates enhanced performance in terms of spectral efficiency, channel stability, and bit error rate (BER). © 2014 IEEE.

关键词： Signal modulation

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Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Chuankun Li, Shuai Gao, Yanbo Gao, Xingyu Chen, Ping Li, Jian Li, Wanqing North University of China State Key Laboratory of Dynamic Testing Technology School of Information and Communication Engineering Taiyuan030051 China Shandong University School of Control Science and Engineering School of Software Jinan250100 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and UAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods. © 1991-2012 IEEE.

关键词： Convolution

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Multi-temporal image fusion empowered convolutional neural networks for recognition of 9 common mice actions

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Knowledge-Based Systems 2025年 320卷

作者： Li, Jian Du, Chen Zhao, Yuliang Shan, Peng Wang, Xingqi Zhang, Huawei Wang, Ying School of Information Science and Engineering Northeastern University Shenyang110004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China School of Biological and Medical Engineering Beihang University BeiJing100191 China

The study of complex behaviors and social interactions necessitates precise and efficient methodologies for the recognition and tracking of animal actions. However, existing methods such as depth perception and wearable devices for mice behavior recognition pose risks of physical harm to the subjects and exhibit limited applicability across species with low precision. To redress these deficiencies, this paper proposes the multi-temporal image fusion empowered Convolutional Neural Networks (CNN), aimed at achieving accurate and efficient recognition of nine common mice actions. In this study, we employ mice at various time points as subjects and employ a multi-temporal approach to process image sequences, integrating various frame difference extraction techniques to address the limitations inherent in single-frame prediction for capturing dynamic changes in actions. Subsequently, we utilize a Deformable Convolution Network (DCN) in conjunction with multi-stacked residual units to enhance the feature extraction capacity of the CNN, particularly focusing on mice action contours, while mitigating the risk of overfitting. Furthermore, we investigate the efficacy of fused images derived from varying frame differences in representing the nine actions, culminating in the establishment of a robust mice action recognition model through ensemble learning techniques. Experimental findings demonstrate an impressive precision rate of 92.9% in recognizing mice actions. The proposed method effectively eliminates background interference and exhibits superior generalization and adaptability properties. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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EAVFormer: an end-to-end audio and visual emotion recognition network based on transformers

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Multimedia Systems 2025年第3期31卷 1-22页

作者： Liu, Haoran Sun, Zijian Li, Haibin Li, Yaqian Zhang, Wenming Song, Tao School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Shenzhen Research Institute of Yanshan University Shenzhen China Key Lab of Measurement Technology & Instrumentation of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao China

Audio-visual emotion recognition, integrating information from various sources, enhances the comprehensive understanding and analysis of human emotions. However, existing methods in this field primarily focus on feature fusion, often overlooking the equally crucial stage of feature extraction. Typically, these methods employ pre-trained models to extract features during the initial phase, complicating modality-specific feature extraction and increasing system complexity and coordination challenges, which hinder effective audio-visual feature fusion. To address these issues, we propose an end-to-end audio-visual emotion recognition model, EAVFormer. For the audio modality, we input raw audio signals along with a mix of mel spectrograms and Mel Frequency Cepstral Coefficients (MFCCs), employing a hierarchical transformer technique that facilitates both shallow local and deep global feature extraction to fully harness complementary acoustic information, significantly enhancing the acoustic modality’s contribution. For the visual modality, we input raw video frames and employ a hierarchical transformer feature extraction method that integrates three-dimensional convolution and spatio-temporal self-attention mechanisms to address spatial and temporal redundancies and dependencies, thereby enhancing the expression of dynamic visual information. In the modal fusion stage, we construct dual-modality representations and encode inter-modal information while preserving modality-specific and meaningful intra-modal features. Testing on three popular datasets RAVDESS, CREMA-D, and CMU-MOSEI demonstrates that EAVFormer surpasses existing state-of-the-art methods in audio-visual emotion recognition accuracy, achieving rates of 95.17%, 86.13%, and 78.22%, respectively.

关键词： Image coding

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A Novel Consecutive Artificial Noise Secrecy Enhancing Scheme for RIS Aided MIMO System With Imperfect CSI

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IEEE Transactions on Vehicular technology 2025年

作者： Shang, Pingping Tao, Zhuofan Xie, Zhonghui Huang, Min Sui, He Li, Jun Hai, Han Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Civil Aviation University of China College of Aeronautical Engineering Tianjin300399 China Guangzhou University School of Electronics and Communication Engineering Guangzhou510006 China Donghua University College of Information Science and Technology Shanghai201620 China

Reconfigurable intelligent surface (RIS) is a technology with great promise, which is widely used in wireless networks to improve the coverage and enhance the performance. However, security concerns pose a major challenge for wireless networks, especially given inevitable errors in channel estimation. This paper proposes a high spectrum efficiency security scheme using generalized spatial modulation (GSM) and space-time block coding (STBC) for scenarios with imperfect channel state information (CSI). In this scheme, GSM uses RIS block indices as unique identifiers, ideal for secure scenarios. It operates by activating different RIS blocks over two time slots to reflect unmodulated carrier signals, while STBC is designed based on the phase shifts of the RIS. To ensure secure communication, a low-complexity continuous artificial noise (CAN) is transmitted with STBC signals to interfere with eavesdroppers and prevent them from decoding the signals. The ergodic secrecy rate is deduced, and the bit error rate (BER) is validated through experiments. The simulation results indicate that the proposed solution can remarkably improves the security performance of the RIS-assisted wireless communication systems. © 1967-2012 IEEE.

关键词： Space-time block coding (STBC)

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Retired Lithium-ion Batteries Screening Based on Partial Discharge Curves and an Improved Crossformer

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IEEE Transactions on Transportation Electrification 2025年

作者： Lin, Mingqiang Xu, Zijun Zheng, Gengfeng Meng, Jinhao Wang, Wei Wu, Ji Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of Matter Fujian Jinjiang362200 China Fujian Special Equipment Inspection and Research Institute Fuzhou350008 China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control China Xi’an Jiaotong University School of Electrical Engineering Xi’an710049 China Xi’an Jiaotong University School of Management Xi’an710049 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

With the increasing number of retired lithium-ion batteries from electric vehicles, effective screening and classification are necessary to enable their secondary utilization. The existing retired lithium-ion battery screening methods have limitations in accuracy and high energy consumption. This paper proposes a method based on local discharge curves and an improved Crossformer model to reduce energy consumption and improve screening accuracy. Considering the actual conditions of retired lithium-ion batteries, features are extracted from the discharging stage. Select the segments of the capacity sequence that exhibit the most significant variation with the voltage sequence and construct a partial capacity-voltage sequence. The standard deviation and Shannon entropy of the capacity sequence, as well as the variance, energy, and the maximum amplitude of the low-frequency part of the Fourier-transformed voltage sequence, are used as features. Then, the Crossformer utilizes a flexible embedding mechanism and a hierarchical structure for handling the dependencies of the feature. The Crossformer is improved for classification by adding a cross-attention layer at the final stage of the Crossformer decoder, which enhances its ability to capture the dependencies between features and improves the model’s performance in battery screening classification. Finally, the validation of 252 retired batteries shows that the method achieves a classification accuracy of 94.74%, outperforming other advanced models, including the original Crossformer, Transformer, ResNet, and MLP models. © 2015 IEEE.

关键词： Lithium-ion batteries

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ASTARS Aided NOMA Covert communication Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Yue, Xinwei Song, Xinlong Zhao, Jingjing Huang, Chongwen Jin, Hongxu Xiang, Wei Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing102206 China Beijing Information Science and Technology University Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing102206 China Beihang University School of Electronic and Information Engineering Beijing100191 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Hangzhou310027 China Zunyi Normal University School of Information Engineering Zunyi563006 China La Trobe University School of Computing Engineering and Mathmatical Sciences MelbourneVIC3086 Australia

The active simultaneously transmitting and reflecting surface (STARS) has been proposed as a complement of passive STARS (PSTARS) to inhibit the double path-loss. This paper applies the active STARS (ASTARS) to aid in non-orthogonal multiple access (NOMA) covert communications over cascaded rice fading channels, where two wardens are located on the opposite sides of ASTARS to monitor the transmission information. More specifically, we derive approximate expressions for detection error probability of two wardens. The optimal detection threshold and minimum detection error probability are presented in the worst covert scenarios. The covert transmission rate expressions for a coupe of covert users are further deduced by taking into account imperfect/perfect successive interference cancellation. The experimental simulation results are presented to confirm that: 1) The detection error probability of ASTARS-NOMA networks is superior to that of ASTARS assisted-orthogonal multiple access (ASTARS-OMA) and PSTARS aided NOMA networks;2) As the number of active elements increases, the covert transmission rates of ASTARS-NOMA networks become larger relative to ASTARS-OMA;and 3) ASTARS-NOMA has ability to provide the enhanced system throughput in delay-tolerant transmission mode. © 2025 IEEE.

关键词： Fading channels

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Multi-view Unsupervised Representation Learning via Integration of Fuzzy Rules and Graph-Based Adaptive Regularization

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhu, Yi Li, Dong Xi, Chao Pedrycz, Witold Macau University of Science and Technology School of Computer Science and Engineering China East China University of Technology School of Mechanical and Electronic Engineering Nanchang330013 China Gliwice Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach. Thecode is available at https://***/r/MvTSK-GAR-E3FB/***. © 1993-2012 IEEE.

关键词： Unsupervised learning

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