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Preamble slice orderly queue access scheme in cell-free dense communication systems

Digital Communications and Networks 2025年第1期11卷 126-135页

作者： Jun Sun Mengzhu Guo Jian Liu School of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type Communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)*** this scheme,the preamble arrangement is combined with the access *** preamble arrangement is realized by preamble slices which is from the virtual preamble *** access devices learn to queue orderly by deep reinforcement *** orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access *** orderly queue is determined by interaction information among multiple *** the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of ***,the access performance of PSOQA is compared with other random contention schemes in different load *** results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

关键词： Machine type communication Random access Reinforcement learning Access success probability Federated learning

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Quantized Control for Input-to-State Stabilization of Discrete-Time Markov Jump Systems with Coding and Decoding Procedures

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IAENG International Journal of Applied Mathematics 2025年第1期55卷 26-33页

作者： Gao, Xiaohui Su, Yue Han, Chengyi Han, Jing Chen, Yebin School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Electrical and Information Engineering Wanjiang University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China

This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in the system state before and after encoding and decoding, the quantization range, and the packet length is established. A criterion for inputto- state stability of the quantized closed-loop Markov jump system is obtained using a Lyapunov function and the Schur complement. The gains of the required quantized controller can be derived from a feasible solution to linear matrix inequalities. Finally, the proposed control scheme is validated using an operational amplifier circuit system. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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Convolutional Neural Network Image Classification Based on Different Color Spaces

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Tsinghua science and technology 2025年第1期30卷 402-417页

作者： Zixiang Xian Rubing Huang Dave Towey Chuan Yue School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is *** paper explores the impact of different color spaces on image classification using *** compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color *** find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than *** color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,*** leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network *** experiments show that our proposed model can outperform the single-color-space models on most *** also find that our method is simple,flexible,and compatible with any CNN and image dataset.

关键词： color space Convolutional Neural Network(CNN) image classification pseudo-Siamese network

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Comprehensive analysis of various imputation and forecasting models for predicting PM2.5 pollutant in Delhi

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Neural Computing and Applications 2025年 1-18页

作者： Karnati, Hemanth Soma, Anuraag Alam, Adnan Kalaavathi, B. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control Board, detailing air pollutant concentrations at the ITO (Income Tax Office), Delhi station, from 2017 to 2023. Our focus on pollutants such as PM2.5, PM10, NO2, NH3, SO2, ozone, and CO highlights the challenges posed by missing data in environmental studies. Through the application of several imputation models, including KNN, linear regression, forward fill + backward fill, Fourier + KNN, linear interpolation, and statistical methods, we identified combination of forward fill and backward fill as the most effective method for addressing data gaps, specifically for ozone measurements. Building on this imputed dataset, we employ various forecasting models to predict PM2.5 levels, a critical pollutant. Our exploration includes time series and deep learning approaches, such as LSTM, Bi-LSTM, CNN-LSTM, GRU, Deep AR, WaveNet, TCN, ARIMA, SARIMA, and Prophet. The performance of these models is evaluated using daily data, with Bi-LSTM and LSTM with attention emerging as the top performers due to their accuracy in predicting PM2.5 concentrations. The implications of our findings extend beyond academic interest, offering practical insights for environmental policy and health advisories in Delhi. By demonstrating the efficacy of specific imputation and forecasting techniques, our research contributes to the broader effort of improving air quality monitoring and prediction. The success of Bi-LSTM and LSTM with attention models, in particular, suggests a promising avenue for future studies aiming to enhance the precision of environmental forecasts. © The Author(s) 2025.

关键词： Time series

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Real-Time Cross-Domain Gesture and User Identification via COTS WiFi

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5124-5137页

作者： Cao, Chenhong Ding, Yue Dai, Miaoling Gong, Wei Zhao, Xibin University of Science and Technology of China School of Computer Science and Technology Langfang101127 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Tsinghua University School of Software Beijing100190 China

WiFi-based gesture recognition has emerged as a promising alternative to computer vision, enabling seamless integration and enhanced interaction in human-computer interaction systems. Simultaneously identifying users during gesture recognition is vital for improving security and personalization. However, existing WiFi-based dual-task recognition approaches often rely on handcrafted features, which hinder precision and introduce delays in cross-domain scenarios. To address these challenges, we propose WiDual, a real-time system for cross-domain gesture recognition and user identification using WiFi signals. By integrating spatial and channel attention mechanisms, WiDual adaptively extracts crucial features for dual-task recognition. The system employs Channel State Information (CSI) visualization to convert WiFi signals into images, facilitating efficient feature extraction and minimizing information loss and latency. Furthermore, a collaborative module fuses gesture and user identity features, enhancing recognition performance. Experimental evaluations on a public dataset with six gestures and six users across diverse environments demonstrate WiDual's effectiveness. It achieves 96% accuracy in cross-domain gesture recognition and 91.27% in user identification. Compared to state-of-the-art methods, WiDual improves user identification accuracy by 26%, gesture recognition by 8%, and reduces processing time sixfold, showcasing its potential for real-time applications. © 2002-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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GET3DGS: Generate 3D Gaussians Based on Points Deformation Fields

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4437-4449页

作者： Yu, Haochen Gong, Weixi Chen, Jiansheng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

The 3D Gaussian Splatting method has recently shown significant advancements in rendering speed and scene composition quality, enhancing its industrial applications and boosting the demand for 3D Gaussian asset generation. However, existing mature 3D generation technologies predominantly rely on implicit representations, which often struggle to balance geometric quality with editability. The production of 3D Gaussian assets generally involves diffusion models that require a dual-stage process of reconstruction and generation, resulting in substantial training and inference costs. To overcome these challenges, we introduce GET3DGS, an innovative approach that combines 3D-aware GANs with 3D Gaussian Splatting representations. This method facilitates the manipulation of the physical attributes of 3D Gaussians, such as geometry and texture, via point deformation fields. Offering faster inference speeds and end-to-end training capabilities, our model outperforms existing diffusion model-based methods. By deriving high-quality Gaussian point cloud geometric representations from 2D images, our approach reduces material accumulation costs and produces data compatible with 3D Gaussian rendering engines. We have evaluated the generative performance of our model on ShapeNet and OmniObject3D and demonstrate competitive results in terms of image and geometric quality relative to previous methods. © 1991-2012 IEEE.

关键词： Rendering (computer graphics)

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Object Detection Model for Remote Sensing Images Based on YOLOv9

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 840-847页

作者： Hou, Donghao Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenges faced by traditional object detection methods in remote sensing images, such as the large number of targets and complex backgrounds, by proposing a novel network based on YOLOv9. The network innovatively introduces the C3_CD_CGA module, an enhanced module based on Cascaded Group Attention, designed to reduce computational redundancy and increase attention diversity, and enhances the processing capability of multi-scale information through the CD module. The C3 module employs deep asymmetric convolution to mitigate information loss and increase the receptive field. Additionally, the network integrates DSConv with the RepNCSPELAN4 module to adaptively focus on and precisely capture the features of elongated and curved local structures, such as vehicles. The introduction of the CARAFE module further improves the spatial resolution of the feature maps, significantly enhancing performance across various visual tasks. Experimental results show that the improved YOLOv9 achieves a mean average precision (mAP) of 88% on the SIMD dataset, which is an improvement of 1.6% compared to the baseline YOLOv9 model and 1.5% higher than the state-of-the-art YOLO-SE model. This model not only achieves more effective multi-target recognition in complex backgrounds but also strikes a good balance between accuracy and efficiency. © (2025), (International Association of Engineers). All rights reserved.

关键词： Urban planning

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Revolutionizing signature scheme: the enhanced Edward Elgamal extreme performance accumulate signature approach for IoT and blockchain applications

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Soft Computing 2025年第3期29卷 1473-1496页

作者： Anusha, R. Saravanan, R. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Digital signatures, essential for establishing trust in the digital realm, have evolved in their application and importance alongside emerging technologies such as the Internet of Things (IoT), Blockchain, and cryptocurrency. These advancements necessitate improvements in performance, security, and efficiency. This article examines and compares the Elliptic Curve Digital Signature Algorithm with the Hyper Elliptic Curve Digital Signature Algorithm and the Edwards Curve Digital Signature Algorithm. We highlight its superior capabilities for blockchain and IoT applications and advocate for its potential to deliver immediate enhancements in security and performance. Our study introduces a novel digital signature scheme specifically designed to enhance non-repudiation in blockchain ecosystems. Utilizing the Optimized Extreme Performance Edwards Curve Accumulated Signature scheme, our approach significantly reduces signing and verification times by 10% and 13%, respectively, compared to traditional signatures. Additionally, it offers a 10% boost in transaction throughput and block validation efficiency. Experiments conducted within various blockchain-integrated IoT setups demonstrate the scheme's effectiveness, consistently achieving improvements across diverse IoT sensor data. This highlights the innovative contribution of our scheme to the efficiency and security of blockchain technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Blockchain Edward curve Elliptic curve Hyperelliptic curve IoT Optimization

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A Lightweight Feature Enhancement Model for Infrared Small Target Detection

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IEEE Sensors Journal 2025年第9期25卷 15224-15234页

作者： Cheng, Kuanhong Ma, Teng Fei, Rong Li, Junhuai Xi’an University of Technology School of Computer Science and Engineering Shaanxi Xi’an710048 China

Infrared small target detection (IRSTD) is a critical yet challenging task due to low target-background contrast, minimal target texture, and high noise levels. While data-driven methods have significantly advanced performance, persistent challenges remain. Traditional pooling schemes in multiscale networks often sacrifice crucial details necessary for detecting small targets, and existing attention mechanisms struggle to balance performance with computational efficiency. To address these challenges, we propose the hybrid feature mining network (HFMNet), a lightweight and efficient model designed to enhance feature representation for IRSTD. First, to reduce detailed loss caused by conventional pooling—which can amplify noise (e.g., max pooling) or suppress contrast (e.g., average pooling)—we introduce the cross-pooling shallow enhancement module (CSEM). By integrating diverse pooling strategies across parallel shallow streams, CSEM preserves fine details, improves localization, and prevents target loss in deeper layers. Second, for efficient local-global context modeling, we propose the hybrid feature mining module (HFMM). This module combines local and global attention by decomposing large 2-D convolutions into coupled 1-D convolutions and incorporating dilated convolutions, broadening the receptive field while reducing complexity. Coupled with the vision state-space module (VSSM) for global context modeling, HFMM effectively integrates local and global information to enhance detection performance. The effectiveness of our HFMNet has been evaluated on four benchmark datasets. In comparison to other state-of-the-art (SOTA) methods, the Pd reaches 92.52% on the IRSTD-1K dataset and 99.08% on the NUAA-SIRST dataset, with only 6.09M parameters, making it a promising lightweight solution for IRSTD applications. © 2001-2012 IEEE.

关键词： Benchmarking

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