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arXiv 2025年

作者： Zhang, Zhong-Yi Xu, Chen-Yang Zhao, Li-Xuan Hou, Hui-Rang Meng, Qing-Hao Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring, widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depression-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity;the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models. Ablation studies confirmed both modules’ contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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BEV2PR: BEV-Enhanced Visual Place Recognition with Structural Cues

BEV2PR: BEV-Enhanced Visual Place Recognition with Structura...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Fudong Ge Yiwei Zhang Shuhan Shen Weiming Hu Yue Wang Jin Gao State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) CASIA School of Artificial Intelligence University of Chinese Academy of Sciences School of Information Science and Technology ShanghaiTech University State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we propose a new image-based visual place recognition (VPR) framework by exploiting the structural cues in bird’s-eye view (BEV) from a single monocular camera. The motivation arises from two key observations about place recognition methods based on both appearance and structure: 1) For the methods relying on LiDAR sensors, the integration of LiDAR in robotic systems has led to increased expenses, while the alignment of data between different sensors is also a major challenge. 2) Other image-/camera-based methods, involving integrating RGB images and their derived variants (e.g., pseudo depth images, pseudo 3D point clouds), exhibit several limitations, such as the failure to effectively exploit the explicit spatial relationships between different objects. To tackle the above issues, we design a new BEV-enhanced VPR framework, namely BEV 2 PR, generating a composite descriptor with both visual cues and spatial awareness based on a single camera. The key points lie in: 1) We use BEV features as an explicit source of structural knowledge in constructing global features. 2) The lower layers of the pretrained backbone from BEV generation are shared for visual and structural streams in VPR, facilitating the learning of fine-grained local features in the visual stream. 3) The complementary visual and structural features can jointly enhance VPR performance. Our BEV 2 PR framework enables consistent performance improvements over several popular aggregation modules for RGB global features. The experiments on our collected VPR-NuScenes dataset demonstrate an absolute gain of 2.47% on Recall@1 for the strong Conv-AP baseline to achieve the best performance in our setting, and notably, a 18.06% gain on the hard set. The code and dataset will be available at https://***/FudongGe/BEV2PR.

关键词： Image sensors Visualization Laser radar Three-dimensional displays Robot vision systems Cameras Sensor systems Sensors Streams Visual place recognition

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A Microstrip Magnetic Dipole Antenna with Reconfigurable Beamwidth

A Microstrip Magnetic Dipole Antenna with Reconfigurable Bea...

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IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Renhang Liang Zhixi Liang Yuanxin Li Hongyan Jiang Yunliang Long Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou China

This paper presents a novel microstrip magnetic dipole (MD) antenna with reconfigurable beamwidth. The proposed design is composed of a microstrip magnetic antenna and two metal planes. The angle of the metal planes is adjustable, and the metal planes are used as a reconfigurable corner reflector. As the angle varies, the reflection coefficients in the operating band (5.67-5.91GHz) are retained below −10dB, and the E-plane half-power beamwidth (HPBW) varies from 37° to 142°. The maximum gain of 12.1 dBi is obtained when the beamwidth is 37°, and the minimum gain of 8.9 dBi is obtained when the beamwidth is 142°. The proposed design is a good candidate for base station applications to enhance traffic capacity.

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Temperature Prediction for Stored Grain: A Multi- model Fusion Approach Based on Machine Learning

Temperature Prediction for Stored Grain: A Multi- model Fusi...

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Electronic Technology and information Science (ICETIS), International Conference on

作者： Donghao Chen Binkun Liu Key Laboratory of Grain Information Processing and Control Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

Temperature fluctuations significantly affect microorganism growth and pest activities in grain pile, precise monitoring and forecasting temperature of stored grain are essential for maintaining the quality and safety of grain storage. This paper proposes a multi-model fusion approach to predict grain temperature using historical temperature data of stored grain and meteorological data from the region. Firstly, four distinct machine learning models, namely Adaboost, decision tree, extra trees, and random forest, are fine-tuned through parameter optimization to enhance their predictive capabilities respectively; Subsequently, these optimized models are fused to form different ensemble models, which are compared for prediction accuracy to obtain the optimal fusion model. In essence, the fusion process integrates the predictions of each individual model as new feature inputs into the fusion models. Furthermore, random forest is utilized to identify the key factors influencing grain temperature, providing insights into the importance of different influencing factors. The experimental results demonstrate that the proposed fusion models can achieve higher prediction accuracy and robustness compared with the single-model prediction methods. Additionally, the analysis of feature importance also offers empirical evidence for understanding the factors influencing grain temperature.

关键词： Temperature sensors Temperature measurement Temperature distribution Humidity Predictive models Prediction algorithms Robustness

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Inter-Channel Nonlinearity Compensation in Long-Haul Optical Fiber Communication Using Overlapped-Dense Lstm Network: Complexity Reduction and Visual Understanding

SSRN

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SSRN 2023年

作者： Hu, Xubin Yi, Xingwen Chen, Yifan Li, Fan Lu, Chao Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou510275 China

The nonlinear distortion resulted from the Kerr effect in optical fibers limits the transmission capacity in long-haul optical fiber communications. In wavelength-division multiplexing (WDM) systems, the inter-channel nonlinear distortion is generally stronger than the intra-channel one, and also more difficult to be compensated, despite that there exist many reports on the nonlinearity compensation methods. Recently, recurrent neural networks have emerged as a potential solution without relying on detailed transmission link information. However, these networks often suffer from high computational complexity and lack interpretability due to their black-box nature. In this paper, we propose a novel overlapped-dense long short-term memory network (OD-LSTM). It can track the correlation of cross-phase-modulation (XPM) and consequently is capable of compensating the inter-channel nonlinear distortion. For a lower complexity, the overlapped-dense structure reduces the hidden units at time steps of different regions, since the correlation of XPM is reduced for the more distant symbols. To evaluate the effectiveness of our proposed scheme, we conduct a numerical simulation on a nine-channel WDM coherent optical transmission over a 1600-km standard single-mode fiber (SSMF) with DP-16QAM at 40 Gbaud. At the optimal optical power of -3 dBm, we observe a 0.52 dB Q-factor gain with the OD-LSTM equalizer, outperforming the digital back-propagation (DBP) with three steps per span and the Co-LSTM regressor. In terms of complexity, our proposed approach achieves a 13.5% reduction compared to the Co-LSTM classifier, and its complexity is slightly lower than that of the ultralow complexity Co-LSTM regressor. We also discuss the visual understanding of the OD-LSTM, which provides insights into the internal mechanisms of the network. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Tunable bound states in the continuum with high Q factors

Tunable bound states in the continuum with high Q factors

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Hu, Guiying Li, Zhixin Yang, Shuixian Xia, Di Wu, Jiayue Huang, Jianteng Zhang, Bin Xu, Yi Li, Zhaohui Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information Technology Sun Yat-sen University Guangzhou510275 China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou510275 China Zhuhai519000 China Guangdong Provincial Key Laboratory of Information Photonics Technology Advanced Institute of Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou510006 China

We realized a chalcogenide glass (ChG) photonic crystal supporting bound states in the continuum (BIC) with a Q-factor of ca. 105. With large photosensitivity of ChG, a non-volatile and high precision resonant-wavelen... 详细信息

ISBN: (纸本)9781557528209

关键词： Q factor measurement

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Development of Hardware Module for Collecting Parameters of Microhydroelectric Power Plant Operation in Mountainous Conditions

Development of Hardware Module for Collecting Parameters of ...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Stefan V. Onishchenko Aliy R. Mamiy Konstantin A. Yurkaev Laboratory of Renewable Energy Sources Adyghe State University Maykop Russian Federation Department of Automated Systems of Processing Information and Control Adyghe State University Maykop Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

Hydropower plants play a major role in the global energy sector, generating up to 17% of all energy generated. Despite this, the spread of small hydropower is not as extensive, although it has an extremely large potential and is a competitive renewable energy source, as small hydropower plants have high efficiencies of up to 80%. The implementation of such energy complexes requires a preliminary assessment of their efficiency, for which atlases and data from existing GIS systems are most often used, but they may not have information on the potential of small rivers and streams, and do not allow for seasonal changes in the water landscape, which is particularly pronounced in mountainous terrain. Therefore, this paper proposes to examine the process of developing a small generating station suitable for use on small streams in mountainous terrain, which can be used as a means to collect data on the energy production profile of small mountain streams, which will help to establish seasonal dependencies and balance the energy system, achieving sustainable energy production through the involvement of other generation sources.

关键词： Microhydro power Temperature Voltage Production Turning Generators Rivers

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Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller 14

Design and Implementation of Telemedicine System Using Light...

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14th International Conference on information Science and Technology, ICIST 2024

作者： Ezilarasan, M.R. Leung, Man-Fai Dai, Xiangguang Vel Tech Rangarajan Dr. Sagunthala R and D Instituteof Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Key Laboratory of Intelligent Chongqing Wanzhou404100 China

ISBN: (纸本)9798350353334

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications;however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted. © 2024 IEEE.

关键词： Liquid crystal displays

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A Sandwich control System with Dual Stochastic Impulses

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 741-744页

作者： Chuanlin Liao Dan Tu Yuming Feng Wei Zhang Zitao Wang B.O.Onasanya the School of Computer Science and Engineering Chongqing Three Gorges University the School of Physical Education and Health Chongqing Three Gorges University the School of Three Gorges Artificial Intelligence Chongqing Three Gorges University the Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University the Department of Mathematics University of Ibadan

Dear editor,Along with the progress of science and technology and the development of social civilization,control system brings an increasingly significant function in daily *** application field of control system is very wide,for instance,in mobile technology[1],artificial earth satellite[2],pest control[3],***[4]first put forward the concept of random pulse in *** present,impulsive control is used in networked control[5],secure communication[6],*** the 21st century,the impulsive control has been used in synchronization of coupled system,intelligent fault identification,image encryption.

关键词： system impulsive instance

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A Probabilistic Topology Inference Method for Networked Dynamical System via Single Excitation

A Probabilistic Topology Inference Method for Networked Dyna...

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IEEE Conference on Decision and control

作者： Qing Jiao Yushan Li Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method.

关键词： Lower bound Accuracy Network topology Noise Probabilistic logic Numerical simulation Topology Noise measurement Reliability Dynamical systems

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