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A Complex Hermitian Positive Definite Manifold Embedding Transformer Network for Time-Varying Direction of Arrival Tracking

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Teng, Liping Wang, Qing Wang, Yanhui Guo, Qinghua Li, Wanqing Tianjin University School of Electrical and Information Engineering Tianjin300072 China Nantong University School of Information Science and Technology Nantong226019 China Science and Technology on Space Physics Laboratory Beijing100076 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

In this work, we address the challenge of stable and robust time-varying direction-of-arrival (DOA) tracking, where both the DOA trajectory and the number of sources change over time across different scenarios. We formulate the problem as a multi-label classification task and propose a novel solution leveraging Riemannian manifold geometry and advanced neural networks for sequential data processing. Specifically, we introduce the Complex Hermitian Positive Definite embedding Transformer (C-HPD-T) network, which learns statistical representations of covariance matrices. The C-HPD-T consists of an HPD module and a Transformer module. The HPD module learns an appropriate statistical representation and the complex Transformer module models the data temporally. In particular, in the C-HPD-T network, we build a complex singular value decomposition (SVD) module to ensure that complex structure is preserved during the calculation of each layer. Meanwhile, we design a Transformer-based sequential network to capture the temporal relationship of signals and for the time-varying DOAs. Through training the network with varying numbers of moving targets featuring diverse motion trajectories, the network demonstrates excellent performance and robustness against array imperfections. Extensive simulation results demonstrate its superior performance gains compared to existing methods. © 2015 IEEE.

关键词： Covariance matrix

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Continuous Bijection Supervised Pyramid Diffeomorphic Deformation for Learning Tooth Meshes From CBCT Images

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zechu Peng, Weilong Wen, Jinyu Tang, Keke Fang, Meie Feng, David Dagan Li, Ping Guangzhou University Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou511400 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China The University of Sydney Biomedical and Multimedia Information Technology Research Group School of Computer Science SydneyNSW2006 Australia The Hong Kong Polytechnic University Department of Computing School of Design and Research Institute for Sports Science and Technology Hong Kong Hong Kong

Accurate and high-quality tooth mesh generation from cone-beam computerized tomography (CBCT) is an essential computer-aided technology for digital dentistry. However, existing segmentation-based methods require complicated post-processing and significant manual correction to generate regular tooth meshes. In this paper, we propose a method of continuous bijection supervised pyramid diffeomorphic deformation (PDD) for learning tooth meshes, which could be used to directly generate high-quality tooth meshes from CBCT Images. Overall, we adopt a classic two-stage framework. In the first stage, we devise an enhanced detector to accurately locate and crop every tooth. In the second stage, a PDD network is designed to deform a sphere mesh from low resolution to high one according to pyramid flows based on diffeomorphic mesh deformations, so that the generated mesh approximates the ground truth infinitely and efficiently. To achieve that, a novel continuous bijection distance loss on the diffeomorphic sphere is also designed to supervise the deformation learning, which overcomes the shortcoming of loss based on nearest-neighbour mapping and improves the fitting precision. Experiments show that our method outperforms the state-of-the-art methods in terms of both different evaluation metrics and the geometry quality of reconstructed tooth surfaces. © 1999-2012 IEEE.

关键词： Mapping

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

arXiv

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arXiv 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Information Systems Technology and Design Singapore University of Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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Graph Prompt Clustering

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Guangdong University of Technology School of Information Engineering Guangzhou China South China Technology Commercialization Center Guangzhou China

Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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Lattice mismatches caused by intrinsic stacking faults promote cracks initiation in Ni–Cu alloys

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Journal of Materials Research and technology 2025年 36卷 9504-9517页

作者： Yu Zhou Yongchao Liang Yuanwei Pu Lili Zhou Zean Tian Qian Chen Yunfei Mo Institute of Advanced Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang 550025 China Department of Information Engineering Gannan Medical University Ganzhou 341000 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Electronic Information and Electrical Engineering Changsha University Changsha 410003 China

Metals are prone to cracking during processing, and the existence of cracks reduces the mechanical properties and service life. Therefore, molecular dynamics simulation method in this study is used to explore the initiation and propagation mechanism of Ni–Cu alloy cracks, so as to improve their mechanical properties. The results show that the intrinsic stacking faults (ISFs) were generated at the torsional end, resulting in the emission of dislocations and the initiation of shear transition zones (STZs). In the yielding stage, the rapidly growing ISFs led to the initiation of a large number of dislocations. At this time, STZs formed shear bands which caused the model to yield. The ISFs led severe lattice mismatch, increased dislocation density, and formed a large number of dislocation entanglements, thereby promoting the formation of cracks. The number and area of cracks rapidly increased during the plastic deformation stage, which eventually led to irrecoverable fracture of the material. The number and area of cracks are the largest at a high twisting rate. High Ni content can increase the yield strength and reduce the number of cracks. The study employed the construct surface mesh method to establish the relationship between shear bands, ISFs, dislocations, and cracks. The study investigates the mechanisms of crack initiation and propagation, providing a theoretical basis for optimizing the preparation process of Ni–Cu alloy and enhancing its mechanical properties.

关键词： Ni–Cu alloy Molecular dynamics simulation Intrinsic stacking faults Crack initiation Lattice mismatch

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Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Singapore University of Technology and Design Singapore Singapore

Diffusion models have been extensively utilized in AI-generated content (AIGC) in recent years, thanks to the superior generation capabilities. Combining with semantic communications, diffusion models are used for tasks such as denoising, data reconstruction, and content generation. However, existing diffusion-based generative models do not consider the stringent bandwidth limitation, which limits its application in wireless communication. This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model. Our designed architecture utilizes the diffusion model, where the signal transmission process through the wireless channel acts as the forward process in diffusion. To reduce bandwidth requirements, we incorporate a downsampling module and a paired upsampling module based on a variational auto-encoder with reparameterization at the receiver to ensure that the recovered features conform to the Gaussian distribution. Furthermore, we derive the loss function for our proposed system and evaluate its performance through comprehensive experiments. Our experimental results demonstrate significant improvements in pixel-level metrics such as peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS). These enhancements are more profound regarding the compression rates and SNR compared to deep joint source-channel coding (DJSCC). © 2002-2012 IEEE.

关键词： Bandwidth

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

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IEEE Control Systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage

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PeerJ computer science 2025年 11卷

作者： Parthasarathi, P. Alshahrani, Haya Mesfer Venkatachalam, K. Cho, Jaehyuk Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Saudi Arabia Department of Software Engineering Jeonbuk National University Jeonju Korea Republic of Department of Software Engineering Division of Electronics Information Engineering Jeonbuk National University Jeonju Korea Republic of

The two leading causes of chronic kidney disease (CKD) are excessive blood pressure and diabetes. Researchers worldwide utilize the rate of globular filtration and kidney inflammation biomarkers to identify chronic kidney disease that gradually reduces renal function. The mortality rate for CKD is high, and thus, a person with this illness is more likely to pass away at a younger age. Healthcare professionals must diagnose the various illnesses connected to this deadly disease as promptly as possible to lighten the impact of CKD. A quantum machine learning (QML) based technique is presented in this research to help with the early diagnosis and prognosis of CKD. The proposed research comprises four phases: data pre-processing, data augmentation, feature selection, and classification. In the first phase, Kalman filter and data normalization techniques are applied to handle the missing and noisy data. In the second phase, data augmentation uses sparse autoencoders to balance the data for smaller classes. In the third phase, LASSO shrinkage is used to select the significant features in the dataset. Variational Quantum classifiers, a supervised QML technique, are employed in the classification phase to classify chronic kidney diseases. The proposed system has been evaluated on the UCI dataset, which comprises 400 CKD patients in the early stages with 25 attributes. The suggested system was assessed using F1-score, precision, recall, and accuracy as evaluation metrics. With a 99.2% classification accuracy, it was found that this model performed better than the other traditional classifiers used for chronic kidney disease classification. Copyright 2025 Parthasarathi et al. Distributed under Creative Commons CC-BY 4.0

关键词： Blood pressure

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An Improved Adaptive Particle Swarm Optimization Based on Belief Rule Base

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International Journal of Fuzzy Systems 2025年 1-20页

作者： Zhang, Xuelin Xu, Xiaobin Sun, Song Zhong, Qinghua He, Hong Brunauer, Georg Steyskal, Felix School of Computer and Information Technology Xinyang Normal University Xinyang 464000 China China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing Hangzhou Dianzi University Hangzhou 310018 China School of Automation Hangzhou Dianzi University Zhejiang Hangzhou 310018 China School of Computer Science Hangzhou Dianzi University Zhejiang Hangzhou 310018 China Institute for Energy Systems and Thermodynamics TU Wien Getreidemarkt 9 Vienna 1060 Austria Maschinen-Umwelttechnik-Transportanlagen Gmbh Schießstattgasse 49 Stockerau 2000 Austria

In particle swarm optimization (PSO), inertia weight plays a crucial role in balancing global and local search. However, fixed or simplistic adjustment strategies often struggle to effectively address complex optimization problems. To address this, an improved adaptive particle swarm optimization based on belief rule base (BRB-PSO) is proposed. Firstly, a universal BRB-based adaptive adjustment strategy for inertia weight is designed, enabling a dynamic balance between global and local search capabilities while ensuring adaptability to diverse optimization problems. Then, the evidential reasoning algorithm is integrated into the BRB to estimate the inertia weight increment and its current value accurately, which significantly improves the flexibility and generalization ability of rule inference. Furthermore, to prevent the algorithm from falling into local optima, the optimal values of each particle and the population are dynamically updated. Finally, the superiority of BRB-PSO is demonstrated by comparing the experimental results with other typical optimization algorithms, and its practical effectiveness is demonstrated in the parameter identification of the wind turbine pitch control system and ship steering machine. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2025.

关键词： Belief rule base Evidential reasoning Inertia weight Particle swarm optimization

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