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AMTrans:Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution

清华大学学报自然科学版（英文版） 2025年第3期30卷 1329-1341页

作者： Lei Gao Liyuan Cui Shuwen Chen Lizhen Deng Xiaokang Wang Xiaohong Yan Hu Zhu College of Electronic and Optical Engineering & College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer Science and Technology Hainan UniversityHaikou 570228China

Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry *** improve the quality and reliability of infrared spectroscopy signals,deconvolution is a crucial preprocessing *** by the transformer model,we propose an Auto-correlation Multi-head attention Transformer(AMTrans)for infrared spectrum sequence *** auto-correlation attention model improves the scaled dot-product attention in the *** utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation *** auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the *** proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic *** comparing the experiments with other deconvolution techniques,the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.

关键词： spectroscopy spectral deconvolution transformer auto-correlation mechanism

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communication-Efficient Distributed Learning in Massive IoT: A Graph-Based Perspective

Communication-Efficient Distributed Learning in Massive IoT:...

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IEEE Conference on Wireless communications and networking

作者： Lindong Zhao Jingyue Tang Mingzhe Chen Liang Zhou Weihua Zhuang Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.

关键词： Performance evaluation Knowledge engineering computer aided instruction Costs Distance learning Scalability Graph neural networks Data models Internet of Things Interchannel interference

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Regularized Instance Weighting Multiview Clustering via Late Fusion Alignment

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IEEE Transactions on Neural networks and Learning Systems 2025年第5期36卷 8926-8938页

作者： Zhang, Yi Tian, Fengyu Ma, Chuan Li, Miaomiao Yang, Hengfu Liu, Zhe Zhu, En Liu, Xinwang National University of Defense Technology School of Computer Changsha410073 China Chongqing University School of Computer Science Chongqing400030 China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Changsha University College of Electronic Information and Electrical Engineering Hunan Changsha410073 China Hunan First Normal University School of Computer Science Changsha410205 China Zhejiang Lab Hangzhou311121 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

Multiview clustering has become a prominent research topic in data analysis, with wide-ranging applications across various fields. However, the existing late fusion multiview clustering (LFMVC) methods still exhibit some limitations, including variable importance and contributions and a heightened sensitivity to noise and outliers during the alignment process. To tackle these challenges, we propose a novel regularized instance weighting multiview clustering via late fusion alignment (R-IWLF-MVC), which considers the instance importance from various views, enabling information integration to be more effective. Specifically, we assign each sample an importance attribute to enable the learning process to focus more on the key sample nodes and avoid being influenced by noise or outliers, while laying the groundwork for the fusion of different views. In addition, we continue to employ late fusion alignment to integrate base clustering from various views and introduce a new regularization term with prior knowledge to ensure that the learning process does not deviate too much from the expected results. After that, we design a three-step alternating optimization strategy with proven convergence for the resultant problem. Our proposed approach has been extensively evaluated on multiple real-world datasets, demonstrating its superiority to state-of-the-art methods. © 2012 IEEE.

关键词： Fusion strategy late fusion alignment multiple kernel clustering multiview clustering

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Cross-Modal Haptic Generation for Emergency Rescue in Internet of Robotic Things

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IEEE Internet of Things Journal 2025年

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE68588 United States

Robots equipped with multi-modal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual-haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multi-modal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multi-modal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue. © 2025 IEEE.

关键词： Robots

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ScanDTM: A Novel Dual-Temporal Modulation Scanpath Prediction Model for Omnidirectional Images

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhu, Dandan Zhang, Kaiwei Min, Xiongkuo Zhai, Guangtao Yang, Xiaokang East China Normal University School of Computer Science and Technology Shanghai200333 China Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai200240 China

Scanpath prediction for omnidirectional images aims to effectively simulate the human visual perception mechanism to generate dynamic realistic fixation trajectories. However, the majority of scanpath prediction methods for omnidirectional images are still in their infancy as they fail to accurately capture the time-dependency of viewing behavior and suffer from sub-optimal performance along with limited generalization capability. A desirable solution should achieve a better trade-off between prediction performance and generalization ability. To this end, we propose a novel dual-temporal modulation scanpath prediction (ScanDTM) model for omnidirectional images. Such a model is designed to effectively capture long-range time-dependencies between various fixation regions across both internal and external time dimensions, thereby generating more realistic scanpaths. In particular, we design a Dual Graph Convolutional network (Dual-GCN) module comprising a semantic-level GCN and an image-level GCN. This module servers as a robust visual encoder that captures spatial relationships among various object regions within an image and fully utilizes similar images as complementary information to capture similarity relations across relevant images. Notably, the proposed Dual-GCN focuses on modeling temporal correlations from both local and global perspectives within the internal time dimension. Furthermore, drawing inspiration from the promising generalization capabilities of diffusion models across various generative tasks, we introduce a novel diffusion-guided saliency module. This module formulates the prediction issue as a conditional generative process for the saliency map, utilizing extracted semantic-level and image-level visual features as conditions. With the well-designed diffusion-guided saliency module, our proposed ScanDTM model acting as an external temporal modulator, we can progressively refine the generated scanpath from the noisy map. We conduct extensive expe

关键词： Prediction models

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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Science China(Information Sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Analysis and improvement of a provable secure fuzzy identity-based signature scheme

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Science China(Information Sciences) 2014年第9期57卷 156-160页

作者： XIONG Hu CHEN YaNan ZHU GuoBin QIN ZhiGuang School of Computer Science and Engineering University of Electronic Science and Technology of China Chongqing Key Lab of Computer Network and Communication Technology Key Lab of Network Security and Cryptology Fujian Normal University School of Traffic and Transportation Beijing Jiaotong University

To enjoy the property of error tolerance in the biometric identities extraction,the fuzzy identitybased signature has been initialized to issue a signature on behalf of a descriptive attributes setωinstead of a single *** attributes setω′can be used to verify the validity of the signature in case the distance betweenωandω′is less than a pre-defined ***,Wang re-formalized the notion of fuzzy identitybased signature and proposed a concrete pairing-based construction along with the efficiency analysis and formal security ***,by giving concrete attack,we indicate that Wang’s scheme is not secure against forgery *** also present an improved scheme to prevent this attack.

关键词： identity-based cryptosystem fuzzy identity-based signature forgery attack cryptanalysis bilinear pairings

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A new application with algebraic multigrid method

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Journal of Information and Computational Science 2012年第6期9卷 1447-1454页

作者： Huang, Ying Xie, Mei Li, Weisheng Lai, Jun Wang, Kuangsheng School of Electronic Engineering University of Electronic Science and Technology of China Chengdu 610054 China Chongqing Key Lab. of Computer Network and Communication Technology Chongqing 400065 China Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China

Algebraic Multi-grid (AMG) method is analyzed and is combined with the graph cut method to extract the texture feature of the image. AMG method can extract more information about the singularities in the image. The data in the coarse grid is utilized to reconstruct the image and the reconstruct result approximates to the original image well. On the basis of the analysis on the AMG method, an energy function is constructed for the texture feature and minimized using max-flow method. Experimental results show that the proposed method can extract more texture details accurately. © 2012 by Binary Information Press.

关键词： Image reconstruction

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Research on K-means median filter

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Journal of Information and Computational Science 2011年第6期8卷 961-968页

作者： Huang, Ying Xie, Mei Li, Weisheng Zhou, Lifang School of Electronic Engineering University of Electronic Science and Technology of China Chengdu 610054 China Chongqing Key Lab. of Computer Network and Communication Technology Chongqing 400065 China Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China

K-means median filter algorithm and recursive K-means median filter algorithm are designed to solve the defects of standard median filter. At first, the sample composed by the current pixel and its neighbors are divided into two classes, and the median of the class which includes the current pixel is computed as the result. The parameters TL and TH are used for the consideration of speed and accuracy, and they are analyzed to reach an optimal result. The standard K-means algorithm is a time-consuming procedure, so the optimization is proposed in this paper. And through the experimental analysis, the algorithm is suitable to the images with obvious foreground and background, especially to binary image. Some examples are given to prove the new algorithm can get better noise reduction effect. © 2011 Binary Information Press.

关键词： Median filters

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE communications Letters 2025年

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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