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UltraADV: An Unsupervised Deep Learning Lightweight Framework for Anomaly Detection in V2X

IEEE Internet of Things Journal 2025年第9期12卷 12735-12747页

作者： Huang, Weixian Xu, Hongyun Gong, Yujing Liu, Zhikang Li, Feng Lin, Zhihui Hu, Bin-Jie South China University of Technology School of Computer Science and Engineering Guangzhou510006 China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China

Designing anomaly detection systems for vehicle-to-everything (V2X) is a challenge. Deep learning has shown strong advantages in anomaly detection. However, labeling anomalies is often difficult and expensive, and deep neural networks often prioritize performances by incorporating a large number of parameters. Therefore, there is an urgent need for a lightweight anomaly detection framework which can be built with unsupervised learning methods. In this article, we propose a framework, the UltraADV, which is suitable for anomaly detection systems in V2X. In the framework, the autoencoder facilitates an unsupervised setting, while the knowledge distillation transfers the knowledge of a larger network to a smaller network. Ultimately, the compressed model can be deployed onto the resource-constrained devices for real-world applications in V2X. We evaluate the performances of multiple architectures of UltraADV on two publicly available datasets. Our best performing architecture demonstrably surpasses the benchmark in terms of performance metrics, parameter efficiency, and prediction speed. © 2014 IEEE.

关键词： Contrastive Learning

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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

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computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises

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IEEE Transactions on Wireless Communications 2025年第5期24卷 4055-4072页

作者： Pei, Jianhua Feng, Cheng Wang, Ping Tabassum, Hina Shi, Dongyuan Huazhong University of Science and Technology School of Electrical and Electronic Engineering Hubei Wuhan China Cornell University Energy Systems Engineering System Engineering IthacaNY United States York University Lassonde School of Engineering Department of Electrical Engineering and Computer Science TorontoON Canada

Deep learning (DL)-based Semantic Communications (SemCom) is becoming critical to maximize overall efficiency of communication networks. Nevertheless, SemCom is sensitive to wireless channel uncertainties, source outliers, and suffer from poor generalization bottlenecks. To address the mentioned challenges, this paper develops a latent diffusion model-enabled SemCom system with three key contributions, i.e., i) to handle potential outliers in the source data, semantic errors obtained by projected gradient descent based on the vulnerabilities of DL models, are utilized to update the parameters and obtain an outlier-robust encoder, ii) a lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter and is placed before the decoder at the receiver, enabling adaptation for out-of-distribution data and enhancing human-perceptual quality, and iii) an end-to-end consistency distillation (EECD) strategy is used to distill the diffusion models trained in latent space, enabling deterministic single or few-step low-latency denoising in various noisy channels while maintaining high semantic quality. Extensive numerical experiments across different datasets demonstrate the superiority of the proposed SemCom system, consistently proving its robustness to outliers, the capability to transmit data with unknown distributions, and the ability to perform real-time channel denoising tasks while preserving high human perceptual quality, outperforming the existing denoising approaches in semantic metrics such as multiscale structural similarity index measure (MS-SSIM) and learned perceptual image path similarity (LPIPS). © 2002-2012 IEEE.

关键词： Image enhancement

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Blockchain-enabled IoT access control model for sharing electronic healthcare data

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Multimedia Tools and Applications 2025年第10期84卷 8127-8148页

作者： Ilyas, Benkhaddra Kumar, Abhishek Ali, Setitra Mohamed Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Chandigarh University Gharuan Mohali Punjab140413 India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Deploying the Internet of Things (IoT) in the transfer of enormous medical data often promotes challenges with the security, confidentiality, and privacy of the user’s sensitive data. In addition, the access control for prohibiting unauthorized access has been facing challenges with single-point-of-failure, collusion, and other security issues. To address these challenges associated with IoT access control, this research proposes a Blockchain-enabled IoT access control for sharing electronic health records (EHR). The proposed authentic access control scheme transfers the data to the authenticated user without discrepancies. The proposed technique utilizes the hyperledger composer fabric blockchain that improves the transparency and traceability of the network, and the intelligent contract keeps track of the information, which reduces the possibility of unauthorized access. The proposed access control scheme validates the request effectively and transmits the data efficiently. The effectiveness of the proposed access control scheme is evaluated by analyzing the blocks in terms of metrics such as responsiveness of 185.47 secs, 95% Genuine User Detection (GUD), 96.50% Privacy, and 3.50% Information loss, for 100 blocks and proposed access control scheme works more efficiently in transmitting the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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Deep Joint Source-Channel Coding for Wireless Image Transmission: One-Model-to-Many

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IEEE Wireless Communications Letters 2025年第5期14卷 1501-1505页

作者： Wang, Feng Chen, Xuechen Deng, Xiaoheng Lin, Siyu Central South University School of Computer Science and Engineering Changsha410075 China Central South University School of Electronic Information Changsha410075 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100091 China

In recent years, researches on deep joint source-channel coding (DeepJSCC) for wireless communications achieve great success owing to the employment of deep learning (DL). However, all existing DeepJSCC schemes only work with a single bandwidth, ignoring the fact that different users have different bandwidths. Under additive Gaussian white noise (AWGN) channel, we present a DeepJSCC scheme utilizing auto-regressive model and transformer block for wireless image transmission, where a single model is trained across multiple compression ratios (CRs) through masking, enabling it to adapt to various bandwidth CRs. Specifically, at the transmitter, our encoder first combines convolutional kernels with transformer block to effectively capture both global and local features of an image. Then we transmit a portion of the encoded image by masking. At the receiver, based on the masking strategy, we use auto-regressive model that fully utilizes the known prior information to restore the masked portions of the encoded image. Extensive experiments show that our scheme achieves the adaptation of a single model to various CRs and better image reconstruction quality than the separately models trained under fixed CRs. © 2025 IEEE.

关键词： White noise

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Spectrum-Energy Efficient Sink-AAV-LEO Backbone Networks to Ensure Ubiquitous Data Collection Timeliness

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IEEE Internet of Things Journal 2025年第9期12卷 12761-12776页

作者： Liu, Hanjian Gui, Jinsong Central South University School of Computer Science and Engineering Changsha410083 China Central South University School of Electronic Information Changsha410075 China

The rise of low Earth orbit (LEO) satellites and autonomous aerial vehicles (AAVs) has facilitated the integration of space-air–terrestrial networks, enabling ubiquitous data collection in Internet of Remote Things (IoRT) networks. However, limited battery capacity of sinks/AAVs and scarce spectrum in these networks make it challenging to build spectrum-energy efficient backbone networks for timely data collection. In this article, we explore the resource allocation problem in Sink-AAV-LEO-based data collection scenarios, where sink nodes gather data from sensors, and AAVs relay it to LEO satellites. To ensure data collection timeliness while optimizing energy and spectrum use, we focus on optimizing the three-hop network structure, power control for sink nodes, path selection and power control for AAVs, and bandwidth allocation for AAV-LEO links. Although these tasks should theoretically be modeled as a global optimization problem, solving them within tolerable time complexity is difficult. Therefore, we first establish a three-hop network using our proposed optimization mechanism. Then, we describe the Sink-AAV and AAV-LEO resource allocation problems, and propose the corresponding solutions via utilizing our improved deep reinforcement learning algorithm, namely ISS-proximal policy optimization. Finally, we solve the global problem based on the solutions to the above two local problems. Besides, our simulation results are compared with various benchmark schemes, validating the effectiveness and superiority of our proposed solutions across different perspectives. © 2014 IEEE.

关键词： Global optimization

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Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5584-5597页

作者： Zeng, Zhihao Zhang, Xiaoning Zhao, Yangming Zoha, Ahmed Imran, Muhammad Ali Zhang, Yan University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China University of Science and Technology of China School of Computer Science and Technology Hefei China University of Glasgow School of Engineering United Kingdom University of Oslo Department of Informatics Norway

The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×. © 2002-2012 IEEE.

关键词： Economic and social effects

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