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arXiv 2022年

作者： Huang, Yihui Wang, Zi Zhang, Xinlin Cao, Jian Tu, Zhangren Lin, Meijin Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud Research and Development Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China The College of Physics and Information Engineering Fuzhou University Fuzhou China The Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University China The School of Computer and Information Engineering Xiamen University of Technology Xiamen China

Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimposed exponential functions, deep learning has achieved fast and high-fidelity signal reconstruction by training a mapping from the undersampled exponentials to the fully sampled ones. However, the mismatch, such as the sampling rate of undersampling, the organ and the contrast of imaging, between the training and target data will heavily compromise the reconstruction. To address this issue, we propose Alternating Deep Low-Rank (ADLR), which combines deep learning solvers and classic optimization solvers. Experiments on the reconstruction of synthetic and realistic biomedical magnetic resonance signals demonstrate that ADLR can effectively mitigate the mismatch issue and achieve lower reconstruction errors than state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Exponential functions

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Incoherent mode division multiplexing for high-security information encryption

arXiv

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arXiv 2023年

作者： Liu, Xin Ponomarenko, Sergey A. Wang, Fei Cai, Yangjian Liang, Chunhao Shandong Provincial Engineering and Technical Center of Light Manipulation Shandong Provincial Key Laboratory of Optics and Photonic Devices School of Physics and Electronics Shandong Normal University Jinan250014 China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal University Jinan250358 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 2X4 Canada Department of Physics and Atmospheric Science Dalhousie University HalifaxNSB3H 4R2 Canada School of Physical Science and Technology Soochow University Suzhou215006 China

In the age of information explosion, the conventional optical communication protocols are rapidly reaching the limits of their capacity, as almost all available degrees of freedom (e.g., wavelength, polarization) for division multiplexing have been explored to date. Recent advances in coherent mode division multiplexing have greatly facilitated high-speed optical communications and secure, high-capacity information storage and transfer. However, coherent mode division multiplexing is quite vulnerable to even minute environmental disturbances which can cause significant information loss. Here, we propose and experimentally demonstrate a paradigm shift to incoherent mode division multiplexing for high-security optical information encryption by harnessing the degree of coherence of structured random light beams. In contrast to the conventional techniques, our approach does not require mode orthogonality to circumnavigate unwanted mode crosstalk. In addition, our protocol has, in principle, no upper bound on its capacity. Thanks to the extreme robustness of structured random light to external perturbations, we are able to achieve highly accurate information encryption and decryption in the adverse environment. The proposed protocol opens new horizons in an array of fields, such as optical imaging, optical communications, and cryptography, and it can be relevant for information processing with acoustical, seismic, matter as well as other types of waves. © 2023, CC BY.

关键词： Cryptography

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Anomalydae: Dual autoencoder for anomaly detection onattributed networks

arXiv

引用

arXiv 2020年

作者： Fan, Haoyi Zhang, Fengbin Li, Zuoyong School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed *** Codes 68T30 Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks

Anomalydae: Dual Autoencoder for Anomaly Detection on Attrib...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Haoyi Fan Fengbin Zhang Zuoyong Li School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

关键词：

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Robust Face Recognition via Multi-scale Contextual information Ensemble Learning

Robust Face Recognition via Multi-scale Contextual Informati...

引用

Chinese Automation Congress (CAC)

作者： Wan Zhang Guangwei Gao Fei Wu Songsong Wu College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The study of robust face recognition has always gained much attention and has been widely applied to many social and public safety protection fields. Image patch based methods have achieved much more attractive performance. Especially, multi-scale patch based methods take the impact of different scales of the image patch into consideration. However, these methods ignore a fact that different parts of the face image contain different contextual semantic information which may be useful for the recognition task. To this end, in this study, we presented a robust face recognition approach by fully utilizing the multi-scale contextual information. Different from previous patch based methods, in our method, we select several patches around the test patch with a certain step in each window. Then the concatenated patch set is linearly represented over the corresponding patch sets on the training samples. By this consideration, the contextual topology can provide complementary contributions to the recognition, especially when the test faces have occlusions. Also, the multi-scale ensemble learning scheme is exploited to further enhance the recognition performance. Our extensive experiments have validated the superiority of our proposed approach over some state-of-the-art patch based robust face recognition approaches.

关键词： Face recognition Training Image recognition Telecommunications Nuclear magnetic resonance Indexes Dictionaries

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Negative information filtering algorithm based on text content in multimedia networks

引用

International Journal of Performability Engineering 2019年第11期15卷 3061-3071页

作者： Chen, Wenqing Fu, Weina College of Applied Technology China University of Labor Relations Beijing100000 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010012 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In the multimedia network environment, it is necessary to effectively filter negative information in the multimedia network and enhance the ability to mine and identify valid data. This paper presents a new algorithm of negative information filtering based on text content in multimedia networks. The principal component features of negative information in multimedia networks are extracted, and matched filters are designed to filter the negative information reasonably. All text contents and negative information are normalized and sorted. They are transformed into the same text format for classification and processing, and the filtering and detection of negative information are realized. Finally, based on the semantic features of text content, the support vector machine algorithm is used to extract negative information features from data. Experimental results show that the algorithm improves the filtering accuracy and performance for negative information in multimedia networks, and it has good application value. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： information filtering

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An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

引用

computers, Materials & Continua 2019年第3期58卷 711-725页

作者： Jin Wang Yu Gao Wei Liu Wenbing Wu Se-Jung Lim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China College of Information Engineering Yangzhou UniversityYangzhou225000China Department of Computer Engineering Chonnam National UniversityYeosu596597Korea.

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many *** provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of *** clustering process takes sensors’location and residual energy into consideration to elect suitable cluster ***,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to *** experimental results display that the presented method can avoid long distance communicate between sensor ***,this algorithm reduces energy consumption effectively and improves package delivery rate.

关键词： Internet of things wireless sensor networks clustering mobile data collection timer.

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Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

arXiv

引用

arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening

Heterogeneous two-Stream Network with Hierarchical Feature P...

引用

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Dong Wang Yunpeng Bai Bendu Bai Chanyue Wu Ying Li National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728176055;9781728176062

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Satellites Three-dimensional displays Fuses Streaming media Signal processing information retrieval Visual effects

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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