IEEE Transactions on Consumer Electronics最新文献

{"title":"Primary-Ambient Extraction Using Ambient Phase Estimate Under Joint Sparsity and Independence Constraints for Stereo Signals","authors":"Xiyu Song;Teng Tian;Shiqi Wang;Fangzhi Yao;Hongbing Qiu;Mei Wang;Hongyan Jiang","doi":"10.1109/TCE.2025.3563989","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563989","url":null,"abstract":"Primary-ambient extraction (PAE) is a technique to enhance the user listening experience in spatial audio reproduction. This is achieved by extracting the primary and ambient components from the sound scene. The PAE approach of ambient phase estimation with a sparsity constraint (APES) leverages the magnitude consistency of ambient components and the sparsity of the primary components to refine the PAE performance. This approach demonstrates an improved extraction accuracy when the ambient component is relatively strong. However, APES suffers from severe extraction errors when the primary amplitudes are equal in two channels of a stereo signal, which is a common sound scene in stereo signals. In this paper, the limitations of APES are analyzed, and a novel ambient phase estimation method is proposed under the joint constraints of sparsity and independence, called APESI. This method uses the independence between the primary component and the ambient component to correct the ambient phase estimation condition. Both objective and subjective experimental results demonstrate that the proposed APESI outperforms the APES and other traditional approaches in terms of extraction accuracy and ambient spatial accuracy, especially when the primary amplitudes are equal.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2806-2813"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868252","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":"Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data","authors":"Saeed Iqbal;Xiaopin Zhong;Muhammad Attique Khan;Mohammad Shabaz;Zongze Wu;Dina Abdulaziz AlHammadi;Weixiang Liu;Shabbab Ali Algamdi;Yang Li","doi":"10.1109/TCE.2025.3563986","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563986","url":null,"abstract":"Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3391-3412"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868164","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":"Privacy-Preserving Interactive Semantic Codec Training for IoT-Based Holographic Counterparts","authors":"Jinpeng Xu;Liang Chen;Limei Lin;Xiaoding Wang;Yanze Huang;Li Xu;Md. Jalil Piran","doi":"10.1109/TCE.2025.3563921","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563921","url":null,"abstract":"The use of IoT-based semantic codecs to process complex contextual semantic information in holographic counterparts introduces significant privacy risks, as it may expose sensitive data, thereby increasing the likelihood of privacy disclosures. The diverse and dynamic nature of holographic counterparts in IoT environments exacerbates these challenges, making it more difficult for semantic codecs to effectively safeguard data privacy. This complexity further intensifies the need for privacy-preserving computation methods, as ensuring the confidentiality and security of the data processed by these codecs becomes a critical concern. However, current privacy protection strategy for multi-party training of semantic codecs relies heavily on the central server for gradient calculation, which may lead to gradient leakage issue. To address this issue, we propose PIMSeC (Privacy-Preserving Interactive Multi-Party Semantic Codec Training for IoT-Based Holographic Counterparts), a novel encryption-based technique that facilitates secure and efficient multi-party interactive training without the dependence on the central server, which enhances both data security and privacy resilience. PIMSeC not only proposes a full interactive secure multi-party deep learning model to protect data privacy during multi-party interactive training, but also, within the above deep learning model, establishes an encrypted additive gradient noise mechanism to ensure post-training semantic codec data privacy. Our theoretical analysis and experimental results demonstrate that PIMSeC promotes semantic codecs privacy protection effectively by interactive secure multi-party training. Compared to the state-of-art methods, PIMSeC achieves a 3% to 15% improvement in terms of accuracy, precision, F1-score, and recall at lower compression rates.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5287-5299"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867940","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":"DPSO-NAS: Wall Crack Detection Algorithm Based on Particle Swarm Optimization NAS","authors":"Zhao Xuejian;Chen Wenxin;Wang Enliang;Hu Yekai","doi":"10.1109/TCE.2025.3564011","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564011","url":null,"abstract":"As urbanization progresses, building surfaces increasingly suffer from degradation and structural damage due to prolonged environmental stress, raising significant safety concerns. Consumer-grade drones with embedded vision technology offer a promising approach for intelligent detection of architectural surface anomalies. However, reliance on manually designed network architectures limits their effectiveness, as these struggle to represent complex textures, reduce crack segmentation accuracy, and fail to efficiently leverage the heterogeneous computing resources of drones, hindering widespread adoption in building inspections. To address these issues, we propose a Neural Architecture Search framework based on Dynamic Particle Swarm Optimization (DPSO-NAS). This framework introduces a hardware-aware search space to dynamically adapt architectures to drone computational constraints, a dual-path feature fusion unit using anisotropic convolution to enhance crack feature extraction, and an automated evaluation mechanism to eliminate human bias and ensure optimal model convergence. Experiments show DPSO-NAS outperforms manually designed networks by 4.7–12.3 percentage points in classification accuracy on CIFAR and ImageNet16-120 datasets. In crack segmentation, it achieves a 77.4% mIoU and reduces edge localization errors by 38.6%. On mainstream drone platforms, it improves inference speed by 2.1 times and cuts power consumption by 57%, advancing efficient, scalable inspection solutions.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6055-6068"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868389","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":"Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning","authors":"Saeed Iqbal;Xiaopin Zhong;Muhammad Attique Khan;Zongze Wu;Dina Abdulaziz AlHammadi;Weixiang Liu;Imran Arshad Choudhry","doi":"10.1109/TCE.2025.3563909","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563909","url":null,"abstract":"Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models - Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) - to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5025-5035"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867974","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":"A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms","authors":"Hazrat Bilal;Muhammad Shamrooz Aslam;Yibin Tian;Inam Ullah;Sarra Ayouni;Athanasios V. Vasilakos","doi":"10.1109/TCE.2025.3563993","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563993","url":null,"abstract":"The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-<inline-formula> <tex-math>$H^{infty }$ </tex-math></inline-formula>-SMO (Takagi-Sugeno Fuzzy-based <inline-formula> <tex-math>$H^{infty }$ </tex-math></inline-formula> Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-<inline-formula> <tex-math>$H^{infty }$ </tex-math></inline-formula>-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-<inline-formula> <tex-math>$H^{infty }$ </tex-math></inline-formula>-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6910-6923"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868333","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":"Self-Attention Policy Optimization for Task Offloading and Resource Allocation in Low-Carbon Agricultural Consumer Electronic Devices","authors":"Yi Huang;Jisong Zeng;Yanting Wei;Miaojiang Chen;Wenjing Xiao;Yang Yang;Zhiquan Liu;Ahmed Farouk;Houbing Herbert Song","doi":"10.1109/TCE.2025.3563421","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563421","url":null,"abstract":"In recent years, the widespread use of edge agricultural consumer electronics has greatly contributed to the level of intelligence in agricultural production, bringing higher efficiency and quality. However, offloading all tasks to the cloud incurs significant latency and resource waste, while relying solely on edge computing fails to meet the computational demands of the entire system. To solve the above problems, we introduce the device-edge-cloud (DEC) three-layer architecture, where agri-consumer electronics devices can partially offload tasks to the edge, and the edge can partially offload tasks to the cloud, i.e., agri-consumer electronics can realize device-edge-cloud collaborative computation. Second, we model the joint computation offloading and resource allocation optimization problem as a non-convex optimization and propose a novel Self-Attention Policy Optimization (SAPO) algorithm to solve it. Experiments show that the joint optimization performance of the proposed SAPO exceeds the baseline, and it is suitable for many different models. Compared with fully connected networks, it has better convergence and robustness, with a convergence speed 50% faster than the fully connected networks. The proposed SAPO algorithm has good scalability and adaptability, and has the potential to be extended to smart agricultural computing scenarios with non-convex optimization.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6969-6980"},"PeriodicalIF":10.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868309","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":"MAPSM: Mobility-Aware Proactive Service Migration Framework for Mobile-Edge Computing in Consumer Internet of Vehicles","authors":"Xuhui Zhao;Yan Shi;Shanzhi Chen;Jianghui Liu;Baofeng Ji;Shahid Mumtaz","doi":"10.1109/TCE.2025.3563627","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563627","url":null,"abstract":"Mobile edge computing is considered as a key technology for consumer Internet of Vehicles networks, which provides low-latency, high-reliability network services for end-users. Service migration need to address where to migrate and how to implement service migration procedure based on user mobility. The existing reactive migrating solutions lead to overlong service migration time and end to end latency. The proactive service migration method obtains the target server in advance through mobility prediction, and the service migration procedure starts before communication handover. Based on the above observation, a Mobility Aware Proactive edge Service Migration framework (MAPSM) is proposed in this paper. MAPSM includes the key aspects: 1) predicting the next location of an end user based on ensemble learning method combining recurrent neural networks and geographical embedding Markov chain predictors; 2) using the mobility prediction result to determine the target edge server of service migration, a proactive migration-handover coordinated method is proposed by performing container pre-migration, memory state migration and communication handover. The time planning scheme in the procedure is also designed. Experimental results demonstrate that MAPSM can greatly improve migration performance, effectively reduce end-to-end latency and significantly reduce service migration time. MAPSM outperforms other baseline service migration approaches.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3753-3766"},"PeriodicalIF":10.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867616","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":"Neural Fictitious-Self Play-Based Cyber-Layer Defense for Frequency Control in Microgrids Against FDI Attacks","authors":"Yang Li;Shichao Liu;Li Zhu;Hongwei Wang","doi":"10.1109/TCE.2025.3563674","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563674","url":null,"abstract":"Securing secondary frequency control against increasing false data injection (FDI) attacks is crucial in microgrid systems. Although various detection systems (DSs) have been proposed for microgrids, false positives (FPs) and false negatives (FNs) in DSs introduce imperfect observations to the cyber defense system. Improper defense actions may reduce the system performance due to additional time delay and/or resource utilization. This paper designs a decentralized optimal decision-making scheme for cyber-layer defense to secure microgrid secondary frequency control against rational FDI attacks. Besides the capability of tackling imperfect observations from DSs, the proposed optimal defense decision-making scheme can maximize the long-term reward rather than a one-shot reward in response to FDI attacks. A multi-stage security game model is formulated, and cyber-physical states and controllability Gramians are jointly considered in the payoff function. The strategy realization-equivalent rule and Nash equilibrium (NE) are introduced to derive the optimal defense policy. A neural fictitious self-play (NFSP) is introduced to learn the optimal defense strategy. Simulation results show that the proposed method increases the successful defense ratio by 21.29% compared with the stochastic game solution when imperfect observations of DSs are considered.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6109-6119"},"PeriodicalIF":10.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868117","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":"CMRM: Collaborative Multi-Agent Reinforcement Learning for Multi-Objective Traffic Signal Control","authors":"Lei Nie;Dandan Qi;Bingyi Liu;Peng Li;Haizhou Bao;Heng He","doi":"10.1109/TCE.2025.3563723","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563723","url":null,"abstract":"Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2793-2805"},"PeriodicalIF":10.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868329","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}