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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Haoran Zhang, Zhibo Kang, Jiarong Jiang, Xun Gong, Xiaoli Zhang, Jin Sun, Zhe Cichocki, Andrzej College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Systems Research Institute Polish Academy of Science Warsaw Poland

ISBN: (纸本)9798350368741

data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN. © 2025 IEEE.

关键词： data argumentation frequency domain augmentation Gaussian kernel prediction time series

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Multiplex network infomax:Multiplex network embedding via information fusion

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Digital Communications and Networks 2023年第5期9卷 1157-1168页

作者： Qiang Wang Hao Jiang Ying Jiang Shuwen Yi Qi Nie Geng Zhang Electronic Information School Wuhan UniversityWuhan430072China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou510006China China Electric Power Research Institute Beijing100192China

For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and *** existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among ***,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex *** majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple *** this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding *** be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each *** the basis of this framework,we present an unsupervised network embedding method for attributed multiplex *** results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised.

关键词： Network embedding Multiplex network Mutual information maximization

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Mask R-CNN Network for Back Recognition on Adam's Bending Images 21

Mask R-CNN Network for Back Recognition on Adam's Bending Im...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuxin, Pu Hongbin, Cai Jipeng, Fan Xin, Zheng School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Chengdu Chengdian Goldisc HealthData Technology Co. Ltd. China School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China

ISBN: (纸本)9798331519254

Back recognition is critical for adolescent idiopathic scoliosis screening. Existing methods mainly rely on manual work which is time-consuming and laborious and may introduce errors and mismatching impacting the accuracy of subsequent screening outcomes. In this work, we propose to utilize the Mask R-CNN network as the framework for auto-extraction of the back region on Adam's bending images. However, due to the inherent limit receptive fields, initial Mask R-CNN can hardly learn long-range dependencies which are crucial for object detection and segmentation. To this end, we introduce the explicit visual center module into Mask R-CNN. In the EVC module, a lightweight MLP is devised to extract global long-range dependencies. To compensate for MLP's lack of ability to capture features of local corner regions, a learnable visual center is incorporated. By aggregating global long-range dependencies and discriminative feature representation of local key regions, our Mask R-CNN can achieve better performance on Adam's bending images. Moreover, we collect and annotate an Adam's bending image dataset for back recognition. Experimental results on our annotated dataset demonstrate that our improved Mask R-CNN considerably improves the average precision in detection and segmentation of Mask R-CNN from 78.6% to 83.6% and 85.8% to 87.0%, respectively. © 2024 IEEE.

关键词： Object detection

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RIS-Aided MIMO Downlink Transmission for Ultradense LEO Satellite-Terrestrial Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15304-15318页

作者： Zhang, Xin Qin, Xiaohan Zhang, Zitian Cai, Lin X. Zhou, Haibo Zhuang, Weihua Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms. © 2014 IEEE.

关键词： Integer programming

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TRUE: A Correlation Analysis Approach for Conducting Optimal Routing Metrics in VANETs

IEEE Networking Letters

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IEEE Networking Letters 2023年第1期5卷 55-58页

作者： Bi, Zhenguo Meng, Guiying Hawbani, Ammar Goudos, Sotirios K. Wan, Shaohua Zhao, Liang Shenyang Aerospace University School of Computer Science Shenyang110136 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Science and Technology of China School of Computer Science and Technology Hefei360022 China Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

In vehicular networks, especially in urban scenarios, road topology-based routing shows great advantages in dealing with the problems of broken links and long delays originating from sparse connections and signal attenuation. However, how road status affect data transmission performance on various routing metrics is still an intricate and vague problem. Concerned with this problem, we propose a correlation analysis approach between road network features and routing metrics (TRUE) to assist in the construction of optimal routing metrics in road-based hierarchical inter-vehicle routing. Simulation results show that TRUE has a strong ability to optimize intermediate road selection. © 2019 IEEE.

关键词： Vehicle routing

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Design and Implementation of a Power Grid Fault State Analysis System Based on Deep Learning 4

Design and Implementation of a Power Grid Fault State Analys...

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4th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2023

作者： Wen, Xuan Sun, Haimeng Xiamen Institute of Technology School of Data and Computer Science Xiamen China Chengyi College Jimei University Electronic Information Engineering Xiamen China

ISBN: (纸本)9798350308082

The aim of this paper is to propose an intelligent solution to power equipment fault. The emphasis is to improve the feature extraction for different types of power equipment fault Based on the deep learning technology, a method of acquiring feature map and constructing knowledge map for Power Equipment Fault Operation Detection is proposed. This method can analyze and diagnose the running state of power equipment, quickly identify and locate equipment faults, and provide corresponding maintenance schemes, so as to improve the ability of fault treatment and upgrade the level of equipment operation and maintenance services. In addition, this research also designed a kind of immersive full-color hybrid AR virtual environment electrical equipment fault teaching drill and Training Mode method, it enables operators to master the operation principle of power equipment and the skills of handling power equipment faults, so as to improve their practical operation ability and the accuracy of fault handling. © 2023 IEEE.

关键词： Neural network models

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Clustering Model-based Analysis of Sales in Vegetable Categories for Supermarkets 2

Clustering Model-based Analysis of Sales in Vegetable Catego...

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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Liao, Jiana Chen, Jiajun Zheng, Wenxu Ren, Yazhou Guangdong University of Technology School of Automation Guangzhou China University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798331515669

The development of effective vegetable sales strategies is crucial for enhancing supermarket profitability, and these strategies are heavily reliant on a nuanced understanding of the sales volume distribution patterns across different vegetable categories. However, most existing studies fail to thoroughly analyze these distribution patterns, often neglecting factors such as sales volume magnitude and fluctuations. To address this gap, we first propose a comprehensive framework based on a K-means++ clustering model and Spearman correlation coefficient to analyze the sales data. Firstly, we normalize the data to eliminate the impact of differing dimensions across vegetable categories. Next, we analyze the sales data from both quarterly and daily perspectives. data visualization is performed utilizing line charts, followed by a descriptive analysis of the sales volume distribution considering both magnitude and fluctuation. Furthermore, we utilize a K-means++ clustering model and the Spearman correlation coefficient to further explore the distribution patterns and interrelationships among different vegetable categories. The results of the experiments reveal the daily and quarterly sales distribution patterns of various vegetable categories, providing valuable insights to support the development of effective vegetable sales strategies for supermarkets. © 2024 IEEE.

关键词： Sales

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Improved Deep Matrix Factorization for Signal Recovery in Random Frequency Diversity Array Radar 2

Improved Deep Matrix Factorization for Signal Recovery in Ra...

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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Guo, Dayan Lin, Zijian Huang, Tianyao Qi, Yue He, Jie Zhang, Xiao-Ping Wang, Xueqian Li, Gang University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua Shenzhen International Graduate School Institute of Data and Information Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Motivated by the success of recent deep learning researches on radar, we consider a deep learning based matrix factorization method for suppressing the high range-angle side-lobes in random frequency diversity array (FDA) radars. While the existing deep matrix factorization approach is designed for real-valued situations and signals of FDA radars are complex-valued cases, we propose two methods to extend the existing approach into complex-valued framework: 1) adjusting the neural network structures and activation functions for complex-valued operations, and 2) constructing a real-valued representation of the original complex-valued input. Simulation results demonstrate that the proposed methods outperform traditional ones in multi-target scenarios. © 2024 IEEE.

关键词： Matrix factorization

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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A Multimodal Fusion Network for Student Emotion Recognition Based on Transformer and Tensor Product 2

A Multimodal Fusion Network for Student Emotion Recognition ...

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2nd IEEE International Conference on Sensors, electronics and computer engineering, ICSECE 2024

作者： Xiang, Ao Qi, Zongqing Wang, Han Yang, Qin Ma, Danqing School of Computer Science & Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Computer Science Stevens Institute of Technology HobokenNJ United States Financial Mathematics University of Southern California LA United States School of Integrated Circult Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798350373646

This paper introduces a new multi-modal model based on the Transformer architecture and tensor product fusion strategy, combining BERT's text vectors and ViT's image vectors to classify students psychological conditions, with an accuracy of 93.65%. The purpose of the study is to accurately analyze the mental health status of students from various data sources. This paper discusses modal fusion methods, including early, late and intermediate fusion, to overcome the challenges of integrating multi-modal information. Ablation studies compare the performance of different models and fusion techniques, showing that the proposed model outperforms existing methods such as CLIP and ViLBERT in terms of accuracy and inference speed. Conclusions indicate that while this model has significant advantages in emotion recognition, its potential to incorporate other data modalities provides areas for future research. © 2024 IEEE.

关键词： Tensors

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