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LAAS-KM: Lightweight authentication with aggregate signature verification and key management protocol for VANETs LAAS-KM：用于VANETs的具有聚合签名验证和密钥管理协议的轻量级身份验证

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-02-09 DOI: 10.1016/j.pmcj.2026.102183

A. Anshima , Jegadeesan Subramani , Arun Sekar Rajasekaran

Vehicular Ad Hoc Networks (VANETs) are a significant component of upcoming intelligent transportation systems. VANETs improve road safety by sending danger alerts to drivers; therefore, their messages must be secure and unaltered. Digital signatures are used to verify the integrity and authenticity of transmitted messages; however, existing digital signature-based schemes require a high computational time owing to the repeated use of mathematical operations. To address this issue, a novel signature aggregation and key management (LAAS-KM) scheme is proposed in this paper to reduce the computational cost without compromising security. First, the LAAS-KM allows roadside infrastructure to cluster multiple vehicle signatures into a compact signature to reduce the large computational overhead during the verification process. Moreover, LAAS-KM supports group communication with novel key management to update keys as vehicles move and network topologies change dynamically in VANETs. Moreover, the security analysis section indicates that the LAAS-KM can prevent various security attacks, including impersonation and replay attacks. Furthermore, a formal security analysis is performed using the Scyther tool to validate the critical security properties of LAAS-KM. Performance evaluations show that LAAS-KM outperforms traditional schemes in terms of communication and computation overheads. Finally, a practical simulation is performed using MATLAB, and the performance metrics are analyzed.

车辆自组织网络（vanet）是未来智能交通系统的重要组成部分。VANETs通过向驾驶员发送危险警报来改善道路安全；因此，他们的消息必须是安全且未被更改的。数字签名用于验证传输消息的完整性和真实性；然而，现有的基于数字签名的方案由于重复使用数学运算，需要大量的计算时间。为了解决这一问题，本文提出了一种新的签名聚合和密钥管理（LAAS-KM）方案，在不影响安全性的前提下降低了计算成本。首先，LAAS-KM允许路边基础设施将多个车辆签名聚类为一个紧凑的签名，以减少验证过程中的大量计算开销。此外，LAAS-KM支持群组通信，采用新颖的密钥管理，在vanet中随着车辆移动和网络拓扑动态变化而更新密钥。此外，安全分析部分指出，LAAS-KM可以防止各种安全攻击，包括模拟攻击和重放攻击。此外，使用Scyther工具执行了正式的安全性分析，以验证LAAS-KM的关键安全属性。性能评估表明，LAAS-KM在通信和计算开销方面优于传统方案。最后，利用MATLAB进行了实际仿真，并对性能指标进行了分析。

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引用次数: 0

Hierarchical Information Embeddings with Neural ODEs for Personalized Federated Learning 基于神经ode的个性化联邦学习层次信息嵌入

IF 23.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Pattern Analysis and Machine Intelligence

Pub Date : 2026-02-09 DOI: 10.1109/tpami.2026.3662990

Rui She, Sijie Wang, Qiyu Kang, Kai Zhao, Tianyu Geng, Yanan Zhao, Wenfei Liang, Wee Peng Tay

引用次数: 0

Robust Transceiver Design for RIS Enhanced Dual-Functional Radar-Communication With Movable Antenna RIS增强双功能可移动天线雷达通信鲁棒收发器设计

IF 6.8 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Vehicular Technology

Pub Date : 2026-02-09 DOI: 10.1109/tvt.2026.3662466

Ran Yang, Zheng Dong, Yue Xiu, Guangyi Liu, Wanting Lyu, Xiangxin Meng, Yan Li, Ning Wei

引用次数: 0

A MW/MMW Shared-Aperture Antenna with Enhanced Dual-Band Performance Based on Structural Reutilization for Vehicular Applications 一种基于结构重复利用的增强双频性能的毫瓦/毫米瓦共用孔径车载天线

IF 6.8 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Vehicular Technology

Pub Date : 2026-02-09 DOI: 10.1109/tvt.2026.3662697

Xin Geng, Bi-Tian Chai, Wen-Wen Yang, Wei Qin, Lei Guo, Jian-Xin Chen

引用次数: 0

IEEE Collabratec IEEE Collabratec

IF 11.2 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Communications Magazine

Pub Date : 2026-02-09 DOI: 10.1109/mcom.2026.11373804

引用次数: 0

From Local to Global: Semantic Communication-Driven Remote 3D Scene Reconstruction Using Low-Altitude Platforms 从局部到全局：语义通信驱动的低空平台远程三维场景重建

IF 8.6 1区计算机科学 Q1 TELECOMMUNICATIONS

IEEE Transactions on Cognitive Communications and Networking

Pub Date : 2026-02-09 DOI: 10.1109/tccn.2026.3662333

Tianle Mai, Haipeng Yao, Gepeng Zhu, Chenlang Jin, Xiangjun Xin

引用次数: 0

Generalized Decoupled Control and Capacitor Voltage Balancing for Current Scalable Modular Multilevel Converter 电流可扩展模块化多电平变换器的广义解耦控制与电容电压平衡

IF 7.7 1区工程技术 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Industrial Electronics

Pub Date : 2026-02-09 DOI: 10.1109/tie.2026.3651339

Diego Verdugo, Félix Rojas, Javier Pereda, Jonathan Lillo, Alan Watson

引用次数: 0

Ninth IEEE RADIO International Conference, 27–30 October 2025, Mauritius [AP-S Committees & Activities] 第九届IEEE无线电国际会议，2025年10月27-30日，毛里求斯[AP-S委员会和活动]

IF 5.7 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Antennas and Propagation Magazine

Pub Date : 2026-02-09 DOI: 10.1109/MAP.2025.3638524

Vikass Monebhurrun

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。

引用次数: 0

Advancing Dysarthric Speech-to-Text Recognition with LATTE: A Low-Latency Acoustic Modeling Approach for Real-Time Communication. 用LATTE推进困难语音到文本识别：用于实时通信的低延迟声学建模方法。

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data

Pub Date : 2026-02-09 DOI: 10.1177/2167647X251411174

Qurat Ul Ain, Hammad Afzal, Fazli Subhan, Mazliham Mohd Suud, Younhyun Jung

Dysarthria, a motor speech disorder characterized by slurred and often unintelligible speech, presents substantial challenges for effective communication. Conventional automatic speech recognition systems frequently underperform on dysarthric speech, particularly in severe cases. To address this gap, we introduce low-latency acoustic transcription and textual encoding (LATTE), an advanced framework designed for real-time dysarthric speech recognition. LATTE integrates preprocessing, acoustic processing, and transcription mapping into a unified pipeline, with its core powered by a hybrid architecture that combines convolutional layers for acoustic feature extraction with bidirectional temporal layers for modeling temporal dependencies. Evaluated on the UA-Speech dataset, LATTE achieves a word error rate of 12.5%, phoneme error rate of 8.3%, and a character error rate of 1%. By enabling accurate, low-latency transcription of impaired speech, LATTE provides a robust foundation for enhancing communication and accessibility in both digital applications and real-time interactive environments.

构音障碍是一种运动语言障碍，其特征是说话含糊不清，常常难以理解，对有效的沟通提出了重大挑战。传统的自动语音识别系统经常表现不佳，特别是在严重的情况下。为了解决这一差距，我们引入了低延迟声学转录和文本编码（LATTE），这是一种专为实时困难语音识别而设计的高级框架。LATTE将预处理、声学处理和转录映射集成到一个统一的管道中，其核心由混合架构提供动力，该架构结合了用于声学特征提取的卷积层和用于建模时间依赖性的双向时间层。在UA-Speech数据集上进行评估，LATTE的单词错误率为12.5%，音素错误率为8.3%，字符错误率为1%。通过实现对受损语言的准确、低延迟转录，LATTE为增强数字应用程序和实时交互环境中的通信和可访问性提供了坚实的基础。

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引用次数: 0

SKDAN: A Signal Knowledge-enhanced Domain Adaptation Network for remaining useful life prediction and uncertainty quantification of rolling bearings SKDAN：一种用于滚动轴承剩余使用寿命预测和不确定性量化的信号知识增强域自适应网络

IF 1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-09 DOI: 10.1016/j.compind.2026.104447

Bin Liu, Changfeng Yan, Ming Lv, Yuan Huang, Lixiao Wu

Domain adaptation-based methods are extensively applied to predict the Remaining Useful Life (RUL) of rolling bearings under complex operating conditions. However, the nonlinear degradation process of bearings gives rise to markedly non-stationary characteristics in vibration signals throughout the full life cycle. Although significant differences in fault features arise across different degradation stages, clearly identifying the critical degradation information remains a challenge. In this paper, a Signal Knowledge-enhanced Domain Adaptation Network (SKDAN) is proposed to learn domain-invariant features from non-stationary degradation processes, thereby improving cross-domain RUL prediction. Specifically, an adaptive short-time Fourier transform layer with a variable window is introduced to analyze the raw vibration signals in the time domain. This differentiable layer extracts time–frequency physical information with high energy concentration, which enhances the representation of degradation features. Subsequently, a novel discrepancy metric, termed Multi-Stage Maximum Mean Discrepancy (MSMMD), is proposed to replace the global average discrepancy with multiple local discrepancies. The MSMMD metric effectively increases the inter-class distance between cluster centers, which enables cross-domain feature alignment. Finally, an uncertainty measurement mechanism is constructed via a step-by-step training strategy, with the objective of quantifying the uncertainty in RUL results by calculating confidence intervals for prediction points. Comparative tests with other methods are conducted on two different bearing datasets, and the results demonstrate that SKDAN achieves superior performance and reliability in cross-domain RUL prediction.

基于域自适应的方法被广泛应用于复杂工况下滚动轴承剩余使用寿命的预测。然而，轴承的非线性退化过程导致振动信号在整个生命周期中具有明显的非平稳特征。尽管不同退化阶段的断层特征存在显著差异，但清晰识别关键退化信息仍然是一个挑战。本文提出了一种信号知识增强的域自适应网络（SKDAN），从非平稳退化过程中学习域不变特征，从而提高了跨域RUL预测能力。具体来说，引入了一种带可变窗口的自适应短时傅里叶变换层，对原始振动信号进行时域分析。该可微层提取能量浓度高的时频物理信息，增强了退化特征的表征。随后，提出了一种新的差异度量，称为多阶段最大平均差异（MSMMD），用多个局部差异代替全球平均差异。MSMMD度量有效地增加了聚类中心之间的类间距离，从而实现了跨域特征对齐。最后，通过逐步训练策略构建不确定性度量机制，通过计算预测点的置信区间来量化规则学习结果中的不确定性。在两个不同的轴承数据集上与其他方法进行了对比测试，结果表明，SKDAN在跨域RUL预测中取得了优异的性能和可靠性。

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