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Energy Harvesting Jammer-Aided Covert Communications in Wireless Multi-Relay IoT Systems

IEEE Internet of Things Journal 2025年第11期12卷 17443-17455页

作者： Lv, Hao Yang, Bin Sun, Xiuwen Gao, Chan Gui, Bao Taleb, Tarik School of Computer Science and Technology of Anhui University Hefei230000 China Chuzhou University School of Computer and Information Engineering Chuzhou239000 China Xi'an University of Posts and Telecommunications National Engineering Research Center for Secured Wireless School of Cybersecurity Xi'an710121 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum44801 Germany

This paper investigates covert communications in a multi-relay Internet of Things (IoT) system with multiple energy harvesting jammers, where a transmitter (Alice) attempts to covertly transmit confidential messages to its destination (Bob) through relay forwarding, while a warden (Willie) detects the existence of Alice's transmission. Specifically, we employ a harvesting-then-jamming protocol with which the jammers first harvest energy from Alice and then send jamming signals to interfere with Willie's detection. We propose a relay and jammer selection strategy, namely quality of service (QoS)-aware selection, and use the random selection strategy as a comparison strategy. Under these two selection strategies, we derive the optimal detection threshold and minimum detection error probability at Willie, respectively. We then model the covert throughput performance and obtain the maximum covert throughput by jointly optimizing covert transmit power and jamming transmit power. Extensive numerical results are provided to illustrate the impacts of system parameters on covert throughput performance. © 2014 IEEE.

关键词： Jamming

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A robust visual information hiding framework based on HVS pixel adaptive alpha blending (HPAAB) technique

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Multimedia Tools and Applications 2025年第14期84卷 13959-13982页

作者： Panda, Bishwabara Nayak, Manas Ranjan Mallick, Pradeep Kumar Basu, Abhishek School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of ECE RCC Institute of Information Technology Kolkata India

The popularity of the Internet and digital consumer gadgets has fundamentally changed our society and daily lives by making digital data collection, transmission, and storage exceedingly easy and convenient. However, protecting these data and preventing unauthorized changes is an important task. This challenge has arisen in several domains, including copyright protection, content authentication, information concealment, and protected communications. Several academics have developed digital watermarking algorithms to solve this issue, aiming to implant hidden data (a watermark) in digital material to affix a mark on or seal the digital data content. The proposed approach builds a spatial domain image watermarking approach using entropy, a spectral residual pixel-based saliency map, and alpha blending. This method states that the value of entropy improves the concealing capacity. To increase imperceptibility, a spectral residual-based saliency map is used to discover less visible portions of the input images and progressively incorporate more information based on entropy data. Any modifications made other than those detectable regions will be less noticeable to viewers since they result from saliency, which decides which elements of an image are the most visible from a visual perspective. Lastly, utilizing the quality criteria, the mechanism's capability is analyzed for numerous existing frameworks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Entropy

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Network Traffic Matrix Estimation Based on Link Loads Sampling

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1524-1539页

作者： Sun, Wenyue Chen, Qian Song, Xuehua Bertino, Elisa Wang, Changda Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Yangzhou Polytechnic College School of Information Engineering Yangzhou225009 China Purdue University Department of Computer Science West LafayetteIN47907 United States

Traffic Matrix (TM) represents traffic between all Origin-Destination (OD) node pairs in a network, playing a crucial role in network management. While the methods for TM acquisition typically require scaling each link load in a network, measurement costs arise drastically as the number of links grows exponentially with the number of nodes. To address this issue while achieving TM with acceptable accuracy aligned with various network management requirements, the paper proposes two novel methods. The first is the ME-TMEIM (TM Estimation with Incomplete Measurement Based on Maximum Entropy) method, an efficient approach with yet acceptable accuracy. The second is the D-TMEIM (Dynamic TMEIM) method. Compared to the ME-TMEIM method, the D-TMEIM method trades the TM's acquisition accuracy for efficiency. It adds temporal constraints on network traffic to improve the precision of the obtained missing link loads, based on which the TM is generated using the CS-OMP (Compressed Sensing-Orthogonal Matching Pursuit) algorithm. Experimental results using publicly available Abilene and GÉANT networks demonstrate that the proposed methods not only enhance TM acquisition efficiency but also maintain nearly the same accuracy as the known methods that acquire TM through exhaustive links measurement. © 2013 IEEE.

关键词： Efficiency

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BACFuse: Towards Noise-Resistant BAC Detection Based on Multi-Modal Fusion on Smartphone

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IEEE Internet of Things Journal 2025年第11期12卷 16410-16423页

作者： Wu, Yuan Zhao, Gaorong Yang, Zheng Feng, Yong Wang, Yongmei Ding, Lei Chen, Jia Zhang, Jian Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China Wuhan University School of Computer Science Wuhan230036 China Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users' active participation, making it impractical to monitor Blood Alcohol Content (BAC) during driving. This paper introduces BACFuse, a device-free, contactless, and non-invasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multi-modal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse's performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with a MAE of 2.13mg/dl, showing promise for future in-car driving management paradigms. © 2014 IEEE.

关键词： Ultrasonic devices

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Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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JCN: Joint Constraint-Based Human Pose Refinement Networks

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IEEE Access 2025年 13卷 68841-68856页

作者： Zhang, Yuru Zhao, Jiayuan Su, Xiaodong Xu, Hongyan Jin, Meijian Harbin University of Commerce School of Computer and Information Engineering Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China

2D human pose estimation has essential applications in traffic prediction and human-computer interaction. We propose a pose refinement network for refining human pose features to improve human pose detection accuracy. We propose our approach to two critical problems in pose estimation, occlusion and background interference, for which the model often needs local information for inference. In contrast, for the background interference problem, the model needs more non-local semantic information to guide the model to make semantic judgments. Specifically, we propose Joint Constraint-Based Human Pose Refinement Networks, which explicitly and implicitly model the relationship between critical points through graph neural networks and self-attention mechanisms to capture local and non-local information of the pose features and refine the keypoints responses. In order to avoid mutual conflicts between explicit and implicit modeling, this paper chooses a parallel structure. It selects an appropriate feature fusion strategy to fuse parallel branch features to improve model robustness. Meanwhile, to make the model pay more attention to the hard-to-identify vital points, this paper adopts the focal loss function to optimize the model and improve the data's long-tailed distribution. We validate our work on the publicly available dataset MPII with the MSCOCO dataset. The results show that our proposed method has the effect of refining the keypoints response, with a PCKh@0.5 of 92.51% on the official MPII validation dataset, and achieved a result of 78% AP on the official MSCOCO validation dataset, which is comparable to the performance of existing state-of-the-art models. © 2013 IEEE.

关键词： Semantics

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DiffuSAR: Frequency Domain-Aware Diffusion Model for SAR Image Generation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11851-11866页

作者： Ying, Zilu Ke, Wenyu Zhai, Yikui Long, Zhihao Zhou, Jianhong Zhu, Hufei Chen, C. L. Philip Wuyi University School of Electronics and Information Engineering Guangdong Jiangmen529020 China South China University of Technology School of Electronics and Information Engineering Guangzhou510000 China South China University of Technology Faculty of Computer Science and Engineering Guangdong Guangzhou510000 China

In recent years, deep learning-based Synthetic Aperture Radar (SAR) image detection, recognition, and segmentation models achieve remarkable accuracy when trained on large amounts of SAR image samples. However, the acquisition of SAR images tends to be costly. A practical approach to address this issue involves the use of artificially generated training samples to supplement the dataset. Current SAR image generation methods based on Generative Adversarial Networks have issues with generation fidelity and are challenge to converge. Moreover, most of these methods only consider the spatial domain of SAR images without exploring the characteristics of their frequency domain. To tackle the aforementioned problems, we proposed a lightweight frequency domain-aware SAR image generation model based on the Denoising Diffusion Probabilistic Model. The proposed generative model is capable of producing highly realistic artificial SAR image samples while converging stably. Meanwhile, our research reveals that high-frequency components of SAR images play a crucial role in the generation process, and a frequency adjustment module based on the Fast Fourier Transform is implemented in our generation model to prevent interference of high-frequency components by low-frequency components. Tests on the MSTAR dataset highlight our model's advantages in both generation quality and parameter efficiency compared to other cutting-edge image generation models. An additional ablation study also confirms the effectiveness of our proposed frequency adjustment module, which reduces the Fréchet Inception Distance by 2.85%, confirming our hypothesis that high-frequency components are more crucial to SAR image generation. Code is available at https://***/yikuizhai/DiffuSAR. © 2025 IEEE.

关键词： Fast Fourier transforms

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TGCM: Cross-Domain Few-Shot Semantic Segmentation via One-Shot Target Guided CutMix 17th

TGCM: Cross-Domain Few-Shot Semantic Segmentation via One-S...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Wei, HaiTao Liu, JianMing Chen, Tong Qiu, WenLong School of Computer Information Engineering Jiangxi Normal University NanChang China

ISBN: (纸本)9789819609628

The goal of few-shot semantic segmentation is to build a model using a small amount of annotated data to generalize to a new object class. When there are significant differences between the target domain and the source domain, segmentation performance usually deteriorates significantly. Existing methods mainly rely on annotated data from the source domain to achieve domain-independent feature extraction through feature transformation. However, bridging the huge domain gap without guidance from the target domain data remains highly challenging. To effectively address the domain shift problem in cross-domain few-shot semantic segmentation (CD-FSS), this paper introduces a new method called Target-guided Cross-domain Few-shot Semantic Segmentation (TGCM), which introduces a labeled sample from the target domain to guide model learning during training. Specifically, TGCM utilizes one-shot image and mask from the target domain as auxiliary data and employs the CutMix method for data augmentation on the source domain training data. Subsequently, the task-adaptive feature transformer module (TAFT) and domain channel alignment (DCA) module are introduced to translate the features of fused images into the feature space related to the target domain, reducing the domain drift effect caused by cross-domain discrepancies. Finally, we present a Dynamic Prediction (DP) strategy to help the model gradually improve segmentation performance. Experimental results show that our model achieves significant improvement in CD-FSS, with average accuracies higher by 4.71% and 2.95% on 1-shot and 5-shot, respectively, compared to the baseline methods in CD-FSS. The code and dataset are available on https://***/08-401/TGCM_ACCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Gloss-driven Conditional Diffusion Models for Sign Language Production

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷 1-17页

作者： Tang, Shengeng Xue, Feng Wu, Jingjing Wang, Shuo Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China School of Data Science School of Information Science and Technology University of Science and Technology of China Hefei China

Sign Language Production (SLP) aims to convert text or audio sentences into sign language videos corresponding to their semantics, which is challenging due to the diversity and complexity of sign languages, and cross-modal semantic mapping issues. In this work, we propose a Gloss-driven Conditional Diffusion Model (GCDM) for SLP. The core of the GCDM is a diffusion model architecture, in which the sign gloss sequence is encoded by a Transformer-based encoder and input into the diffusion model as a semantic prior condition. In the process of sign pose generation, the textual semantic priors carried in the encoded gloss features are integrated into the embedded Gaussian noise via cross-attention. Subsequently, the model converts the fused features into sign language pose sequences through T-round denoising steps. During the training process, the model uses the ground-truth labels of sign poses as the starting point, generates Gaussian noise through T rounds of noise, and then performs T rounds of denoising to approximate the real sign language gestures. The entire process is constrained by the MAE loss function to ensure that the generated sign language gestures are as close as possible to the real labels. In the inference phase, the model directly randomly samples a set of Gaussian noise, generates multiple sign language gesture sequence hypotheses under the guidance of the gloss sequence, and outputs a high-confidence sign language gesture video by averaging multiple hypotheses. Experimental results on the Phoenix2014T dataset show that the proposed GCDM method achieves competitiveness in both quantitative performance and qualitative visualization. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Forming

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