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Covert Communication in RSMA-Assisted Ambient Backscatter Communication Systems

IEEE Transactions on Wireless communications 2025年

作者： Zhang, Zhuo Yang, Liang Lei, Hongjiang Li, Xingwang Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering China Jishou University School of Communication and Electronic Engineering China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering The Chongqing Key Laboratory of Mobile Communications Technology Chongqing400065 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

In this paper, we investigate covert communication in rate-splitting multiple access (RSMA)-assisted ambient backscatter communication (AmBC) systems. Specifically, in the presence of illegitimate users, a base station applies RSMA to the downlink transmission of covert users and common users in the the primary system of ambient backscatter. To evaluate the covertness of the system, we derive closed-form expressions for the detection error probability (DEP), outage probability (OP), and covert rate (CR), where the DEP is applied to evaluate the ability of the warden to detect the presence or absence of communication behaviors between the base station and the covert user. Based on these expressions, we also conduct an asymptotic analysis of the outage probability, an upper bound analysis of the covert rate, and an analysis of the impact of the power allocation factor on system performance. Numerical results show that the RSMA scheme has a higher covert rate and a higher minimum DEP value compared to the non-orthogonal multiple access (NOMA) scheme, but it may reduce its outage performance. Additionally, an appropriate use of the power allocation factor can enhance the covertness of the system. Furthermore, we find that the co-channel interference between the primary and secondary systems in the ambient backscatter system can degrade the covert performance of the primary system. © 2002-2012 IEEE.

关键词： Backscattering

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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H-STEP: Heuristic Stable Edge Service Entity Placement for Mobile Virtual Reality Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chi, Xuejian Chen, Honglong Ni, Zhichen Sun, Haiyang Sun, Peng Yu, Dongxiao Shandong Provincial Engineering Research Center of Intelligent Sensing and Measurement and Control Technology College of Control Science and Engineering Qingdao266580 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Shandong University School of Computer Science and Technology Qingdao266237 China

Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user's quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system's economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP's performance closely approximates that of the optimal scheme and surpasses existing schemes. © 2002-2012 IEEE.

关键词： Virtual environments

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Self-Supervised Enhancement of Forward-Looking Sonar Images: Bridging Cross-Modal Degradation Gaps through Feature Space Transformation and Multi-Frame Fusion

arXiv

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arXiv 2025年

作者： Zhang, Zhisheng Zhang, Peng Wang, Fengxiang Ma, Liangli Sun, Fuchun College of Electronic Engineering Naval University of Engineering Wuhan430033 China College of Meteorology and Oceanography National University of Defense Technology Changsha410073 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Enhancing forward-looking sonar images is critical for accurate underwater target detection. Current deep learning methods mainly rely on supervised training with simulated data, but the difficulty in obtaining high-quality real-world paired data limits their practical use and generalization. Although self-supervised approaches from remote sensing partially alleviate data shortages, they neglect the cross-modal degradation gap between sonar and remote sensing images. Directly transferring pretrained weights often leads to overly smooth sonar images, detail loss, and insufficient brightness. To address this, we propose a feature-space transformation that maps sonar images from the pixel domain to a robust feature domain, effectively bridging the degradation gap. Additionally, our self-supervised multi-frame fusion strategy leverages complementary inter-frame information to naturally remove speckle noise and enhance target-region brightness. Experiments on three self-collected real-world forward-looking sonar datasets show that our method significantly outperforms existing approaches, effectively suppressing noise, preserving detailed edges, and substantially improving brightness, demonstrating strong potential for underwater target detection applications. © 2025, CC BY.

关键词： Underwater acoustics

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From fluid relaxations to double deep Q-network for Dynamic Multiplicity Flexible Job-Shop Scheduling

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Applied Soft Computing 2025年 177卷

作者： Yang, Xiaoyu Han, Yuyan Wang, Yuting Li, Huan Gao, Kaizhou Liu, Yiping School of Computer Science Liaocheng University Liaocheng252059 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Due to the random arrival of orders during production, companies may face challenges that result in the execution of a scheduling plan that is not optimal. The real-time dynamic arrival of orders is a critical issue. In view of this, this paper proposes a multi-policy double deep Q-network (MPDDQN) to optimize a Dynamic Multiplicity Flexible Job-Shop Scheduling Problem (DMFJSP), in which the dynamic arrival of job orders and multiple types of jobs are considered simultaneously. Specifically, firstly, a mathematical model for DMFJSP is established, and its validity is verified by the Gurobi solver. Secondly, we propose a modified fluid model that transforms discrete scheduling into a continuous flow to simplify the complexity of the DMFJSP. Based on this fluid model, we further develop a multi-metric selection strategy to assist in production scheduling decisions. Third, a Markov Decision Process (MDP) model is constructed, we extracted 19 features related to jobs and machines as references for state features and designed 20 composite rules as candidate actions. Subsequently, the MPDDQN algorithm achieves an effective balance between exploration and exploitation across various complex scenarios by combining prioritized experience replay, a soft update mechanism, and an adaptive action selection strategy. Finally, by conducting a comparative analysis with 10 well-known heuristic rules and 3 existing deep reinforcement learning-based scheduling approaches across 81 test cases, the proposed algorithm demonstrates superior generalization, robustness, and optimization capabilities in DMFJSP across scenarios with varying levels of complexity. © 2025 Elsevier B.V.

关键词： Deep reinforcement learning

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A Secure and Efficient Privacy Data Aggregation Mechanism 18th

A Secure and Efficient Privacy Data Aggregation Mechanism

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ma, Wenshuo Liu, Xinru Yu, Kan Luo, Chuanwen Wang, Guopeng Liu, Xiaowu School of Artificial Intelligence and Big Data Zibo Vocational Institute Zibo China Weifang Engineering Vocational College Weifang China School of Computer Science and Engineering Macau University of Science and Technology Taipa China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information Science and Technology Beijing Forestry University Beijing China School of Computer Science Qufu Normal University Rizhao China Zhejiang Ocean University Zhoushan China The Open University of China Beijing China

ISBN: (纸本)9783031714665

Data aggregation (DA) plays an important role in conserving the limited resources and extending the network lifetime of Wireless Sensor Networks (WSNs). Privacy preservation has garnered considerable attention and is considered one of the most viable schemes to address issues such as communication eavesdropping, information leakage, and unauthorized access in WSNs employing DA. In this paper, we propose an improved Secure Privacy-preserving Data Aggregation (iSECPDA) mechanism. It dynamically selects cluster heads through an enhanced Stable Election Protocol (SEP), which considers the residual energy of sensor nodes to enhance network lifetime. In our proposed SEPPDA, we integrate interference information into the sensing data and ensure data transmission privacy through a proposed slicing mechanism. Theoretical analysis and simulation experiments demonstrate that iSECPDA exhibits significant performance improvements in terms of communication overhead and privacy preservation levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data aggregation

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An Incentive Framework for Task Offloading in Edge Computing Marketplaces under Price Competition

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Liantao Sun, Peng Wang, Zhibo Pang, Xiaoyi Hu, Jiahui Chen, Honglong Yang, Yang Shanghai200062 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China College of Control Science and Engineering Qingdao266580 China IoT Thrust Guangzhou510000 China

To efficiently execute tasks, computation resource requesters (CRRs) with limited resources can offload their tasks to nearby computation resource providers (CRPs) with spare computing capacity. These CRPs require appropriate incentives to compensate for their incurred costs when helping process the offloaded tasks. Although several mechanisms have been designed to incentivize CRPs, none of them have investigated the incentive mechanism considering price-setting and price-taking CRPs simultaneously. In this work, we propose an incentive framework for task offloading in the edge computing marketplace that includes both price-setting and price-taking CRPs. We model the CRR's interactions with both types of CRPs as a three-stage Stackelberg game to maximize the profit for both the CRR and CRPs. We prove the existence of a unique subgame perfect equilibrium (SPE) of the formulated game and further develop iterative algorithms for the CRR and price-setting CRPs to achieve the equilibrium. Through the designed algorithms, each CRP does not require complete information about the CRR and other CRPs. Extensive simulations demonstrate that offloading tasks to both price-setting and price-taking CRPs achieves higher profits for the CRR and price-setting CRPs compared to offloading tasks solely to price-setting CRPs. Additionally, the obtained SPE can achieve near-optimal social welfare. © 2002-2012 IEEE.

关键词： Computation offloading

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fMRI2GES: Co-speech Gesture Reconstruction from fMRI Signal with Dual Brain Decoding Alignment

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhu, Chunzheng Shao, Jialin Lin, Jianxin Wang, Yijun Wang, Jing Tang, Jinhui Li, Kenli Hunan University College of Information Science and Engineering Changsha China Tsinghua University Department of Automation Beijing100084 China Nanjing University of Science and Technology School of Computer Science and Technology Nanjing210094 China

Understanding how the brain responds to external stimuli and decoding this process has been a significant challenge in neuroscience. While previous studies typically concentrated on brain-to-image and brain-to-language reconstruction, our work strives to reconstruct gestures associated with speech stimuli perceived by brain. Unfortunately, the lack of paired {brain, speech, gesture} data hinders the deployment of deep learning models for this purpose. In this paper, we introduce a novel approach, fMRI2GES, that allows training of fMRI-to-gesture reconstruction networks on unpaired data using Dual Brain Decoding Alignment. This method relies on two key components: (i) observed texts that elicit brain responses, and (ii) textual descriptions associated with the gestures. Then, instead of training models in a completely supervised manner to find a mapping relationship among the three modalities, we harness an fMRIto- text model, a text-to-gesture model with paired data and an fMRI-to-gesture model with unpaired data, establishing dual fMRI-to-gesture reconstruction patterns. Afterward, we explicitly align two outputs and train our model in a self-supervision way. We show that our proposed method can reconstruct expressive gestures directly from fMRI recordings. We also investigate fMRI signals from different ROIs in the cortex and how they affect generation results. Overall, we provide new insights into decoding co-speech gestures, thereby advancing our understanding of neuroscience and cognitive science. © 1991-2012 IEEE.

关键词： Deep learning

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MAP-SIM: A DNN-Specific Mapping Optimization Framework for Shared-Memory CPU-Systolic Array Architectures

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Yuhang Wen, Mei Shen, Junzhong Chen, Zhaoyun Shi, Yang Wang, Tianyu Shao, Zili National University of Defense Technology College of Computer Science and Technology Hunan Changsha410073 China Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

As performance demands continue to rise, Shared-Memory Heterogeneous Systems (SMHSs) have been widely adopted for their ability to enable efficient communication and data sharing between different heterogeneous cores. However, existing SMHS face challenges in uneven workload distribution among heterogeneous cores and suboptimal mapping schemes, preventing them from fully leveraging their architectural advantages. To address these issues, this paper proposes a mapping-aware framework for modeling SMHSs called MAP-SIM. By performing performance modeling for CPUs and Systolic Arrays (SAs), and considering rational schemes for the partition and mapping of computational tasks, MAP-SIM aims to evaluate and optimize the computational performance of heterogeneous multicore architectures. The experimental results show that compared to previous work, MAP-SIM can increase simulation speed by 14 to 67 times and can also enhance the computational performance of SMHS by 1.4 to 4.4 times. © 1982-2012 IEEE.

关键词： Photomapping

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Study on rheological performance and mechanism of SMA grafted TPU modified asphalt based on in-situ synthesis method 2

Study on rheological performance and mechanism of SMA grafte...

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2nd International Conference on New Materials for Environment and Energy, NMEE 2024

作者： Wang, Shuang Zhu, Xiaobo Chen, Ping Wang, Zhichen Wu, Enze School of Computer Science and Engineering Hunan University of Information Technology Hunan Changsha410151 China Intelligent Engineering Technology Research Center Harbin Cambridge University Heilongjiang Harbin150069 China College of Transportation Engineering Dalian Maritime University Liaoning Dalian116026 China Heilongjiang Engineering Quality Road Bridge Testing Center Co. Ltd. Heilongjiang Harbin150016 China

This study aimed to enhance the high- and low-temperature rheological properties of thermoplastic polyurethane (TPU)-modified asphalt through an innovative in-situ synthesis process. Styrene grafted maleic anhydride (SMA) was employed for the graft copolymerization of the TPU modifier. The asphalt's temperature-dependent properties and the modification mechanism were comprehensively analyzed using dynamic shear rheometry (DSR), binder bending beam rheometer (BBR), Fourier-transform infrared spectroscopy (FTIR), environmental scanning electron microscopy (ESEM), and fluorescence microscopy (FM) tests. The findings revealed that the in-situ synthesized SMA-grafted TPU-modified asphalt exhibited superior rheological properties at both high and low temperatures compared to the conventional native polymerization method. The incorporation of SMA with TPU notably enhanced the asphalt's resistance to high-temperature deformation and its temperature sensitivity while preserving its low-temperature toughness and crack resistance. Microscopic analyses indicated that TPU is synthesized internally within the asphalt matrix, ensuring excellent compatibility. The SMA grafts onto the TPU, creating a flexible network structure with rigid nodes. Optimal results were achieved with a 5% TPU modifier dosage and a TPU to SMA mass ratio of 60:40. This research contributes to the development of high-performance asphalt materials with improved thermal stability and mechanical performance. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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