{"title":"Deadbeat Predictive Algorithm-Based Back EMF Observer for Sensorless Control of PMSM Drives","authors":"Quntao An, Mengji Zhao, Teng Ma, Youtong Wu","doi":"10.1109/tie.2025.3544220","DOIUrl":"https://doi.org/10.1109/tie.2025.3544220","url":null,"abstract":"","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"30 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bursty Mixed Gaussian-impulsive Noise Model and Parameter Estimation","authors":"Tianfu Qi, Jing Zhang, Jun Wang, Yichao Zhu","doi":"10.1109/tcomm.2025.3547782","DOIUrl":"https://doi.org/10.1109/tcomm.2025.3547782","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"86 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546723","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}
Xiaotong Zhao, Hanlin Zhang, Jie Lin, Fan Liang, Fanyu Kong, Hansong Xu, Kun Hua
{"title":"Privacy-Preserving Edge-Aided Eigenvalue Decomposition in Internet of Things","authors":"Xiaotong Zhao, Hanlin Zhang, Jie Lin, Fan Liang, Fanyu Kong, Hansong Xu, Kun Hua","doi":"10.1109/jiot.2025.3544245","DOIUrl":"https://doi.org/10.1109/jiot.2025.3544245","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"28 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546739","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}
{"title":"Enhancing geometric modeling in convolutional neural networks: limit deformable convolution","authors":"Wei Wang, Yuanze Meng, Han Li, Guiyong Chang, Shun Li, Chenghong Zhang","doi":"10.1007/s40747-025-01799-8","DOIUrl":"https://doi.org/10.1007/s40747-025-01799-8","url":null,"abstract":"<p>Convolutional neural networks (CNNs) are constrained in their capacity to model geometric transformations due to their fixed geometric structure. To overcome this problem, researchers introduce deformable convolution, which allows the convolution kernel to be deformable on the feature map. However, deformable convolution may introduce irrelevant contextual information during the learning process and thus affect the model performance. DCNv2 introduces a modulation mechanism to control the diffusion of the sampling points to control the degree of contribution of offsets through weights, but we find that such problems still exist in practical use. Therefore, we propose a new limit deformable convolution to address this problem, which enhances the model ability by adding adaptive limiting units to constrain the offsets and adjusts the weight constraints on the offsets to enhance the image-focusing ability. In the subsequent work, we perform lightweight work on the limit deformable convolution and design three kinds of LDBottleneck to adapt to different scenarios. The limit deformable network, equipped with the optimal LDBottleneck, demonstrated an improvement in mAP75 of 1.4% compared to DCNv1 and 1.1% compared to DCNv2 on the VOC2012+2007 dataset. Furthermore, on the CoCo2017 dataset, different backbones equipped with our limit deformable module achieved satisfactory results. The source code for this work is publicly available at https://github.com/1977245719/LDCN.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538782","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}
{"title":"Stress Severity Detection in College Students Using Emotional Pulse Signals and Deep Learning","authors":"Mi Li, Junzhe Li, Yanbo Chen, Bin Hu","doi":"10.1109/taffc.2025.3547753","DOIUrl":"https://doi.org/10.1109/taffc.2025.3547753","url":null,"abstract":"","PeriodicalId":13131,"journal":{"name":"IEEE Transactions on Affective Computing","volume":"67 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546412","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}
{"title":"Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey","authors":"Vu Tuan Truong, Luan Ba Dang, Long Bao Le","doi":"10.1145/3721479","DOIUrl":"https://doi.org/10.1145/3721479","url":null,"abstract":"Diffusion models (DMs) have achieved state-of-the-art performance on various generative tasks such as image synthesis, text-to-image, and text-guided image-to-image generation. However, the more powerful the DMs, the more harmful they can potentially be. Recent studies have shown that DMs are prone to a wide range of attacks, including adversarial attacks, membership inference attacks, backdoor injection, and various multi-modal threats. Since numerous pre-trained DMs are published widely on the Internet, potential threats from these attacks are especially detrimental to the society, making DM-related security a topic worthy of investigation. Therefore, in this paper, we conduct a comprehensive survey on the security aspect of DMs, focusing on various attack and defense methods for DMs. First, we present crucial knowledge of DMs with five main types of DMs, including denoising diffusion probabilistic models, denoising diffusion implicit models, noise conditioned score networks, stochastic differential equations, and multi-modal conditional DMs. We provide a comprehensive survey of recent works investigating different types of attacks that exploit the vulnerabilities of DMs. Then, we thoroughly review potential countermeasures to mitigate each of the presented threats. Finally, we discuss open challenges of DM-related security and describe potential research directions for this topic.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546525","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}