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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Yupu Deng, Liwei Zhao, Yan Chen, Jin Xie, Jiandong Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Department of Computer Science Aalborg University Aalborg Denmark Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

The pervasiveness of GPS-equipped smart devices and the accompanying deployment of sensing technologies generates increasingly massive amounts of trajectory data that cover applications such as personalized location recommendation and urban transportation planning. Trajectory recovery is of utmost importance for incomplete trajectories (resulted from the constraints of devices and environment) to enable their completeness and reliability. To achieve effective trajectory recovery, we propose a novel trajectory recovery framework, namely Deep Trajectory Recovery with enhanced trajectory Similarity (SimiDTR), which is capable of contending with the complex mobility regularity found in trajectories in continuous space. In particular, we design a rule-based information extractor to extract the spatial information related to an incomplete trajectory, which is then fed into a deep model based on attention mechanism to generate a tailored similar trajectory for the incomplete trajectory. Finally, we use a deep neural network model to recover the incomplete trajectory with the blessing of its similar trajectory. An extensive empirical study with real data offers evidence that the framework is able to advance the state of the art in terms of effectiveness for trajectory recovery, especially in scenes with sparse trajectory data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recovery

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Low-Rank Representation Based Model for Sar Image Denoising and Edge-Preserving

Low-Rank Representation Based Model for Sar Image Denoising ...

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2023 SAR in Big data Era, BIGSARdata 2023

作者： Zhang, Tianyi Tian, Sirui Abhadiomhen, Stanley Ebhohimhen Xu, Zhiyong Shen, Xiang-Jun Wang, Jing Wang, Chao School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China University of Nigeria Department of Computer Science Nsukka410001 Nigeria Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Digital Earth Science Beijing100094 China

ISBN: (纸本)9798350326239

Synthetic aperture radar imaging is a powerful remote sensing technology, but its images are often degraded by speckle noise, reducing quality. In recent years, speckle reduction has become a hot topic in SAR research, with various methods emerging. However, traditional low-rank SAR denoising causes edge blurring and loss of important features. To address this, we propose a new approach using prior knowledge to distinguish noise pixels from edges. By separating low-rank residuals and effectively extracting edge information, our method outperforms others, generating noise-suppressed SAR images while retaining features. Experiments demonstrate the effectiveness of our approach. © 2023 IEEE.

关键词： Synthetic aperture radar

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A Highly Selective Wideband Filtering Stacked DDPA 4

A Highly Selective Wideband Filtering Stacked DDPA

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Tian, Kang Zhang, JiaSheng La, Dongsheng Northeastern University at Qinhuangdao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Qinhuangdao066000 China An Hui Ming Bian Electronic Science & Technology Co. Ltd. China

ISBN: (纸本)9798350363586

In this paper, a compact and highly selective stacked filtering dense dielectric patch (DDP) antenna (DDPA) is proposed. By placing a pair of thin DDP with high dielectric constant along the y axis on the DDP with low dielectric constant, the resonant frequency of the TE12 mode is adjusted to approach low frequency and form a passband with the TE11 mode. By loading a U-shaped microstrip feeding line on the microstrip feeding line, the bandwidth can be further broadened and the TE11, TE11 + δ and TE12 modes can be successfully excited in the passband. The radiation nulls are generated by the U-shaped microstrip line and non-radiative TE12 mode, respectively. By adjusting the length of the U-shaped microstrip line and the size of DDP with high dielectric constant, the independent adjustment of radiation nulls can be achieved. Finally, the proposed filtering antenna achieves a relative bandwidth of 11.66 % (4.04-4.54 GHz). The average realized gain and peak gain are 6.89 dBi and 7.1 dBi, respectively. The realized gain in the passband is very flat, and the fluctuation is less than 0.7 dBi. The proposed antenna has the advantages of compactness, high selectivity and flat gain, and it can be applied to modern satellite communication and wireless communication systems. © 2024 IEEE.

关键词： Bandwidth

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CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing 26

CHA-Sens: An End-to-End Comprehensive Residual Convolution F...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhou, Fujia Zhang, Wei Zhu, Guangxu Li, Hang Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Science and Technology on Electronic Information Control Laboratory Chengdu China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350331684

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks. © 2023 IEEE.

关键词： Channel state information

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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Community Search Based on Containment Control of Multi-Agent System with Opinion Leaders 43

Community Search Based on Containment Control of Multi-Agent...

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43rd Chinese Control Conference, CCC 2024

作者： Liu, Siyang Li, Tong Shao, Jinliang Zheng, Wei Xing University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9789887581581

Community search is the problem of identifying the community in which a given node resides. Different from traditional community search methods based on specific topological structures, this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community formation. Firstly, the Breadth-First Search method is used to locally sample the network. Secondly, containment control of multi-agent system is applied to obtain the final state of each node for local community partitioning. Finally, grounded in practical considerations, an effective leadership transfer mechanism is proposed to update the results and enhance search precision. Through experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms, the effectiveness and rationality of our algorithm are demonstrated. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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Reed-Solomon Codes over Ring with Lower Computational Complexity

Reed-Solomon Codes over Ring with Lower Computational Comple...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Tan, Shicheng Hou, Hanxu Yu, Leilei Dongguan University of Technology School of Computer Science & Technology Dongguan China Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798350324457

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters. © 2023 IEEE.

关键词： cyclic ring cyclic-shift encoding/decoding complexity Reed-Muller transform Reed-Solomon codes

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CLISC: Bridging Clip and SAM by Enhanced CAM for Unsupervised Brain Tumor Segmentation 22

CLISC: Bridging Clip and SAM by Enhanced CAM for Unsupervise...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ma, Xiaochuan Fu, Jia Liao, Wenjun Zhang, Shichuan Wang, Guotai School of Computer Science and Engineering United States Sichuan Cancer Hospital & Institute Sichuan Cancer Center School of Medicine University of Electronic Science and Technology of China Department of Radiation Oncology Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798331520526

Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs. Unsupervised segmentation is promising to avoid human annotations while the performance is often limited. In this study, we present a novel unsupervised segmentation approach that leverages the capabilities of foundation models, and it consists of three main steps: (1) A vision-language model (i.e., CLIP) is employed to obtain image-level pseudo-labels for training a classification network. Class Activation Mapping (CAM) is then employed to extract Regions of Interest (ROIs), where an adaptive masking-based data augmentation is used to enhance ROI identification. (2) The ROIs are used to generate bounding box and point prompts for the Segment Anything Model (SAM) to obtain segmentation pseudo-labels. (3) A 3D segmentation network is trained with the SAM-derived pseudo-labels, where low-quality pseudo-labels are filtered out in a self-learning process based on the similarity between the SAM's output and the network's prediction. Evaluation on the BraTS2020 dataset demonstrates that our approach obtained an average Dice Similarity Score (DSC) of 85.60%, outperforming five state-of-the-art unsupervised segmentation methods by more than 10 percentage points. Besides, our approach outperforms directly using SAM for zero-shot inference, and its performance is close to fully supervised learning. © 2025 IEEE.

关键词： Visual languages

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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