Pub Date : 2026-02-09DOI: 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。
{"title":"Ninth IEEE RADIO International Conference, 27–30 October 2025, Mauritius [AP-S Committees & Activities]","authors":"Vikass Monebhurrun","doi":"10.1109/MAP.2025.3638524","DOIUrl":"https://doi.org/10.1109/MAP.2025.3638524","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13090,"journal":{"name":"IEEE Antennas and Propagation Magazine","volume":"68 1","pages":"114-115"},"PeriodicalIF":5.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11385831","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 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.
{"title":"Advancing Dysarthric Speech-to-Text Recognition with LATTE: A Low-Latency Acoustic Modeling Approach for Real-Time Communication.","authors":"Qurat Ul Ain, Hammad Afzal, Fazli Subhan, Mazliham Mohd Suud, Younhyun Jung","doi":"10.1177/2167647X251411174","DOIUrl":"https://doi.org/10.1177/2167647X251411174","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X251411174"},"PeriodicalIF":2.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 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.
{"title":"SKDAN: A Signal Knowledge-enhanced Domain Adaptation Network for remaining useful life prediction and uncertainty quantification of rolling bearings","authors":"Bin Liu, Changfeng Yan, Ming Lv, Yuan Huang, Lixiao Wu","doi":"10.1016/j.compind.2026.104447","DOIUrl":"https://doi.org/10.1016/j.compind.2026.104447","url":null,"abstract":"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.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"24 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/tnnls.2026.3656889
Mengyun Wang, Yifeng Niu, Bo Wang, Wei Zhang, Chang Wang
{"title":"A Survey on Learning Motion Planning and Control for Mobile Robots: Toward Embodied Intelligence","authors":"Mengyun Wang, Yifeng Niu, Bo Wang, Wei Zhang, Chang Wang","doi":"10.1109/tnnls.2026.3656889","DOIUrl":"https://doi.org/10.1109/tnnls.2026.3656889","url":null,"abstract":"","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"35 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/jiot.2026.3662407
Runkai Song, Fan Qin, Wenchi Cheng, Steven Gao
{"title":"Flexible Wearable Filtering Antenna With Stable Performance for IoT Devices","authors":"Runkai Song, Fan Qin, Wenchi Cheng, Steven Gao","doi":"10.1109/jiot.2026.3662407","DOIUrl":"https://doi.org/10.1109/jiot.2026.3662407","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"60 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146145973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/tac.2026.3662563
Alexandre Didier, Melanie N. Zeilinger
{"title":"Approximate Predictive Control Barrier Function for Discrete-Time Systems","authors":"Alexandre Didier, Melanie N. Zeilinger","doi":"10.1109/tac.2026.3662563","DOIUrl":"https://doi.org/10.1109/tac.2026.3662563","url":null,"abstract":"","PeriodicalId":13201,"journal":{"name":"IEEE Transactions on Automatic Control","volume":"133 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1016/j.jnca.2026.104438
Zainab Alwaisi, Tanesh Kumar, Simone Soderi
Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.
{"title":"Robust and energy-aware detection of Mirai botnet for future 6G-enabled IoT networks","authors":"Zainab Alwaisi, Tanesh Kumar, Simone Soderi","doi":"10.1016/j.jnca.2026.104438","DOIUrl":"https://doi.org/10.1016/j.jnca.2026.104438","url":null,"abstract":"Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"1 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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