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IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

Chinese Journal of Electronics 2025年第1期34卷 322-337页

作者： Jingya Wang Yu Zhang Bin Zhang Jinxiang Xia Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University

This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling-important points sampling method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating method is proposed for feature extraction, improving the network's ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net(IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2 datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

关键词： Point cloud compression Representation learning Geometry Fuses Semantics Artificial neural networks Feature extraction Sampling methods Robustness Data mining

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5G Ultra-Dense Network Fingerprint Positioning Method Based on Matrix Completion

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China Communications 2023年第3期20卷 105-118页

作者： Yuexia Zhang Chong Liu Key Laboratory of Information and Communication Systems Ministry of Information IndustryBeijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching ***,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint ***,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint ***,a classification weighted K-nearest neighbor fingerprint matching algorithm is *** estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint *** simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.

关键词： indoor positioning fingerprint matching matrix completion 5G UDN RSSI

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GTE: learning code AST representation efficiently and effectively

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science China(information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Learning cross-modal interaction for RGB-T tracking

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science China(information sciences) 2023年第1期66卷 320-321页

作者： Chunyan XU Zhen CUI Chaoqun WANG Chuanwei ZHOU Jian YANG Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology

Dear editor,Visual object tracking, which has attracted increasing attention in the field of general visual understanding, aims to track each temporally changing object in a video sequence, with the target specified only in the first *** most tracking algorithms have facilitated significant advances in RGB video sequences, object tracking using only RGB information is unreliable under extreme lighting conditions(e.g., dark night, rain, and foggy).

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 40-53页

作者： Qinglai Wei Shanshan Jiao Qi Dong Fei-Yue Wang IEEE the Institute of Systems Engineering Macau University of Science and Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences China Academy of Electronics and Information Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems the School of Artificial Intelligence University of Chinese Academy of Sciences

This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

关键词： Adaptive dynamic programming (ADP) critic neural network (NN) event-triggered control optimal consensus control robust control

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Recent Advances of Boron-Containing Chiral Luminescent Materials

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Chinese Journal of Chemistry 2025年第2期43卷 219-238页

作者： Jiaqi Di Shuran Han Pangkuan Chen Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering of the Ministry of Industry and Information TechnologySchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing102488 China

As a class of organic dyes,boron-containing compounds play an important role in organic luminescent *** have attracted considerable attention due to their unique photophysical *** luminescent systems have a wide range of practical applications in biological imaging,optoelectronic devices,information storage and 3D ***-containing chiral luminescent materials can not only effectively improve the luminescent properties of CPL materials,but also bring unique properties to the system,which enables them to be used as favorable CPL emitting materials for an expanded range of ***,we review the research progress of boron-containing chiral luminescent materials by the detailed discuss according to different chiral skeletons,such as point chirality,1,1’-binaphthyl,[n]helicenes,[2,2]paracyclophane and pillar[5]arenes.

关键词： Organoboron Chirality Circularly dichroism Circularly polarized luminescence Luminescent materials

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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

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Computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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Adversarial Graph Convolutional Network for Skeleton-Based Early Action Prediction

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Journal of Computer science & technology 2024年第6期39卷 1269-1280页

作者： Xian-Shan Li Neng Zhang Bin-Quan Cai Jing-Wen Kang Feng-Da Zhao School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China School of Information Science and Engineering Xinjiang University of Science and TechnologyKorla 841000China Key Laboratory for Software Engineering of Hebei Province Yanshan UniversityQinhuangdao 066004China

This paper proposes a novel method for early action prediction based on 3D skeleton data. Our method combines the advantages of graph convolutional networks (GCNs) and adversarial learning to avoid the problems of insufficient spatio-temporal feature extraction and difficulty in predicting actions in the early execution stage of actions. In our method, GCNs, which have outstanding performance in the field of action recognition, are used to extract the spatio-temporal features of the skeleton. The model learns how to optimize the feature distribution of partial videos from the features of full videos through adversarial learning. Experiments on two challenging action prediction datasets show that our method performs well on skeleton-based early action prediction. State-of-the-art performance is reported in some observation ratios.

关键词： graph convolutional network adversarial learning skeleton-based action prediction

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Optical Coupling in Atomic Waveguide for Vertically Integrated Photonics

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Research 2025年第1期2024卷 15-22页

作者： Yue Wang Junzhuan Wang Ruijuan Tian Jiapeng Zheng Lei Shao Bo Liu Fengqiu Wang Xuetao Gan Yi Shi Xiaomu Wang School of Electronic Science and Engineering Nanjing UniversityNanjing 210093China Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technologyand Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi'an 710129China Department of Physics The Chinese University of Hong KongHong Kong SARChina State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510275China Institute of Optics and Electronics Nanjing University of Information Science and TechnologyNanjing 210044China

Integrated 2-dimensional(2D)photonic devices such as monolayer waveguide has generated exceptional interest because of their ultimate *** particular,they potentially permit stereo photonic architecture through bond-free van der Waals ***,little is known about the coupling and controlling of the single-atom guided wave to its photonic environment,which governs the design and application of integrated ***,we report the optical coupling of atomically guided waves to other photonic *** directly probe the mode beating between evanescent waves in a monolayer 2D waveguide and a silicon photonic waveguide,which constitutes a vertically integrated *** mode-coupling measures the dispersion relation of the guided wave inside the atomic waveguide and unveils it strongly modifies matter's electronic states,manifesting by the formation of a propagating *** also demonstrated light modulating and spectral detecting in this compact nonplanar *** findings provide a generalizable and versatile platform toward monolithic 3-dimensional integrated photonics.

关键词： integrated systemherewe stereo photonic architecture photonic modes mode beating vertically integrated photonics atomically guided waves optical coupling atomic waveguide

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Observation of memristive behavior in PDMS-glass nanofluidic chip

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Nano Research 2025年第2期18卷 725-732页

作者： Weiling Sun Yike Xiao Pingyuan Yan Fei Sun Xuan Zhang Chuanxiang Sheng Qi Wang Yefeng Yu School of Microelectronics Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of New Membrane Materials Ministry of Industry and Information TechnologySchool of Environmental and Biological EngineeringNanjing University of Science and TechnologyNanjing 210094China Department of Optical Science and Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China

Nanofluidic memristors,which use ions in electrolyte solutions as carriers,have been developed rapidly and brought new opportunities for the development of neuromorphic *** the transport and accumulation of ions in nanochannels to process information is an endeavor to realize the nanofluidic *** this study,we report a new nanofluidic memristor,which is a polydimethylsiloxane(PDMS)-glass chip with two platinum(Pt)electrodes and well-aligned multi-nanochannels within PDMS for ion enrichment and *** device not only exhibits typical bipolar memristive behavior and ion current rectification(ICR)but also demonstrates excellent endurance,maintaining stable performance after 100 sweep *** systematically investigate the key factors affecting ion transport behavior in this *** results show that the ICR ratio of the current-voltage(I-V)hysteresis curves decreases with increasing scan rate and solution *** potential measurements are introduced to reveal that the PDMS surface carries more negative charges in higher pH solutions,resulting in more pronounced memristive and ICR ***,our memristor can simulate short-term synaptic plasticity,such as paired-pulse facilitation(PPF)and paired-pulse depression(PPD),with a relatively low energy consumption of 12 pJ per spike per ***,the inherent accessibility and robustness of our nanofluidic memristors facilitate the optimization of device structure and *** important observations and investigations lay a foundation for advancing energy-saving and efficient neuromorphic computing.

关键词： polydimethylsiloxane(PDMS)-glass chip nanofluidic memristor nanochannel short-term synaptic plasticity(STP)

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