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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South University School of Artificial Intelligence Guilin University of Electronic Technology The Department of Computer Science Universidad de Chile

In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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AR-Light: Enabling Fast and Lightweight Multi-User Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology HoustonTX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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Enhancing patient-independent detection of freezing of gait in Parkinson’s disease with deep adversarial network

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Neural Computing and Applications 2025年 1-21页

作者： Fahad, Md Shah Ranjan, Ashish Kumar, Gautam Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science and Engineering C. V. Raman Global University Odisha Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India

Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, traumas, and psychological repercussions, significantly impacting the patient’s quality of life. The use of accelerometer data derived from sensors put on a patient’s body to detect FoG has previously been proposed using convolutional neural network (CNN)-based deep learning algorithms. Here, we combine a CNN + Long short-term Memory (LSTM)-attention model to detect FoG episodes—a first for the detection of FoG using the accelerometer data. CNN facilitates automatic feature extraction from the accelerometer data itself. The output from the CNN is fed to the LSTM network, which is known for capturing sequential information. Further, the attention mechanism introduces relative focus on individual sub-sequences during the training of the LSTM network. The proposed model is made patient independent using adversarial training. The proposed model achieved improvements of +7.60% (without adversarial training) and + 8.36% (with adversarial training) in the sensitivity values over state of the art. This is obtained with little or no compromise in specificity values. The corresponding improvements in accuracy values are +3.81 and +2.23%, respectively. These findings imply that the proposed method can detect FoG gaits satisfactorily and can be effective in achieving accurate monitoring and gait assistance for Parkinson’s disease patients during daily life and rehabilitation therapy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Accelerometers

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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science China(Information sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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Error-Correcting Graph Codes 16

Error-Correcting Graph Codes

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16th Innovations in Theoretical computer science Conference, ITCS 2025

作者： Kopparty, Swastik Potukuchi, Aditya Sha, Harry Department of Mathematics Department of Computer Science University of Toronto Canada Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9783959773614

In this paper, we construct Error-Correcting Graph Codes. An error-correcting graph code of distance δ is a family C of graphs, on a common vertex set of size n, such that if we start with any graph in C, we would have to modify the neighborhoods of at least δn vertices in order to obtain some other graph in C. This is a natural graph generalization of the standard Hamming distance error-correcting codes for binary strings. Yohananov and Yaakobi were the first to construct codes in this metric. We extend their work by showing 1. Combinatorial results determining the optimal rate vs distance trade-off nonconstructively. 2. Graph code analogues of Reed-Solomon codes and code concatenation, leading to positive distance codes for all rates and positive rate codes for all distances. 3. Graph code analogues of dual-BCH codes, yielding large codes with distance δ = 1 - o(1). This gives an explicit "graph code of Ramsey graphs". Several recent works, starting with the paper of Alon, Gujgiczer, Körner, Milojević, and Simonyi, have studied more general graph codes;where the symmetric difference between any two graphs in the code is required to have some desired property. Error-correcting graph codes are a particularly interesting instantiation of this concept. © Swastik Kopparty, Aditya Potukuchi, and Harry Sha.

关键词： Reed-Solomon codes

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Neural acceleration of incomplete factorization preconditioning

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Neural Computing and Applications 2025年第2期37卷 1009-1026页

作者： Booth, Joshua Dennis Sun, Hongyang Garnett, Trevor Computer Science University of Alabama in Huntsville HuntsvilleAL35899 United States Electrical Engineering & Computer Science University of Kansas LawrenceKS66046 United States

The solution of a sparse system of linear equations is ubiquitous in scientific applications. Iterative methods, such as the preconditioned conjugate gradient (PCG) method and the generalized minimal residuals (GMRES) method, are normally chosen over direct methods due to memory and computational complexity constraints. However, the efficiency of these methods depends largely on the preconditioner utilized. The development of a preconditioner normally requires some insight into the sparse linear system and the desired trade-off between generating the preconditioner and the reduction in the number of iterations. Incomplete factorization is a popular black box method to generate these preconditioners. However, it may fail for several reasons, including numerical issues that require searching for adequate scaling, shifting, and fill-in while utilizing a difficult-to-parallelize algorithm. With a move toward heterogeneous computing, many sparse applications find GPUs that are optimized for dense tensor applications like training neural networks being underutilized. In this work, we demonstrate that a simple artificial neural network trained either at compile time or in parallel to the running application on a GPU can provide an incomplete LLT factorization that can be used as a preconditioner. This generated preconditioner is as good as or better than the ones found using multiple preconditioning techniques such as scaling and shifting in terms of reduction in number of iterations. Moreover, the generated method also works and never fails to produce a preconditioner that does not reduce the iteration count. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Conjugate gradient method

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Accurate Representation of Distribution System Dynamics With DERs for Bulk System Studies

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IEEE Transactions on Industry Applications 2025年第3期61卷 5187-5197页

作者： Challa, Kiran Kumar Matavalam, Amarsagar Reddy Ramapuram Venkatraman, Ramakrishnan Ajjarapu, Venkataramana Iowa State University Department of Electrical and Computer Engineering Ames50011 United States Arizona State University School of Electrical Computer and Energy Engineering Tempe85282 United States

While there has beensignificant progress in recent years to incorporate the dynamics of distribution systems and industrial loads into the transmission using aggregated composite load models (CLM);there is no study that evaluates the accuracy of a well-tuned CLM model versus the true dynamics of a distribution system (DS) with 3-φ, 1-φ motors, ZIP loads, and distributed energy resources (DERs) connected to a transmission system (TS). This paper aims to address this gap by analyzing the limitations of the CLM and proposing physics-based approaches to address these drawbacks. We show that a single CLM is not flexible enough to represent the aggregated DS dynamics in situations when only a few motors in the DS stall or a few DERs trip. To address the drawback of the single CLM, we propose a DS structure preserving reduced order model (DS-ROM) that clusters nodes in the DS and represents each cluster using a CLM. We then present a Python-based automated approach to determine the parameters of the DS-ROM. Results on IEEE-14-bus TS connected to IEEE-37-node DS and a practical 240-node DS demonstrate that DS-ROM can capture the DS dynamics for various kinds of TS faults with an accuracy of ∼98%. © 2025 IEEE.

关键词： Problem oriented languages

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Enhancing Voice Phishing Detection Using Multilingual Back-Translation and SMOTE: An Empirical Study

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IEEE Access 2025年 13卷 37946-37965页

作者： Boussougou, Milandu Keith Moussavou Hamandawana, Prince Park, Dong-Joo Soongsil University Department of Computer Science and Engineering Seoul06978 Korea Republic of Ajou University Department of Software and Computer Engineering Suwon16499 Korea Republic of Soongsil University School of Computer Science and Engineering Seoul06978 Korea Republic of

With the widespread global trend of voice phishing or vishing attacks, the development of effective detection models using artificial intelligence (AI) has been hindered by the lack of high-quality and large volumes of data. This lack of data reflecting a real vishing scenario often leads to imbalanced datasets and biased detection models. Therefore, we present in this paper a data augmentation (DA) method for expanding the imbalanced Korean call content vishing (KorCCVi) dataset to address the existing data asymmetry problem and enhance the performance of Korean vishing detection. The proposed approach for DA involves using the back-translation (BT) method with three different intermediate languages: English, Chinese, and Japanese. The proposed method offers several advantages over the traditional synthetic minority oversampling technique (SMOTE), which is the main technique used to compare with our multilingual BT approach. Using these two DA techniques, several machine learning (ML) and deep learning (DL) models were trained on the original imbalanced dataset, the dataset balanced with SMOTE and its variants, and the dataset augmented with our method. We analyzed the impact of these DA methods on the performance of the models, demonstrated the benefits of each approach, and suggested the most suitable approach. The performance of the trained models was evaluated using the accuracy, precision, recall, and F1-score metrics. The experimental results demonstrated that the proposed multilingual BT method effectively expands the dataset while preserving its contextual and linguistic characteristics. The average performance of the models revealed that those trained on the augmented dataset outperformed the other models. They achieved F1-scores of 98.91% for the back-translated data, 98.14% for the original data, and 97.23% for SMOTE. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecomm

关键词： Phishing

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Isomorphic Classification of First Degree Cellular Automata

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Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

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Community Detection in Multi-Aspect Functional Brain Networks: Robust Tensor Decomposition Approach

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IEEE Access 2025年 13卷 82043-82064页

作者： Al-Sharoa, Esraa Al-Wardat, Mohammad Aviyente, Selin Jordan University of Science and Technology College of Engineering Department of Electrical Engineering Irbid22110 Jordan Michigan State University College of Engineering Department of Electrical and Computer Engineering East LansingMI48824 United States

Functional organization of the brain can be characterized as a network of interconnected regions. Study of brain networks has offered new insights on human behavior and neurodegenerative diseases. Recent studies show that the functional connectivity networks are dynamic with the topology and community structure evolving with time during task performance and rest. Most of the current work on analyzing the community structure of dynamic networks either focus on single subjects or extract the group level community structure. In this paper, we present a framework for community detection in dynamic functional connectivity networks across multiple subjects and at the individual level, simultaneously. The proposed approach is based on a structured robust tensor decomposition with spectral clustering, temporal smoothness and co-clustering regularization terms to extract both the group and individual level community structures. Moreover, the temporal evolution of the community structure within the group is tracked to identify change points that may correspond to events of interest. The proposed framework is applied to dynamic functional connectivity networks constructed from task-based electroencephalogram (EEG) data. © 2013 IEEE.

关键词： Tensors Hidden Markov models Brain modeling Organizations Nonhomogeneous media Matrix decomposition Diseases Mathematical models Feature extraction Time series analysis

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