IEEE Transactions on Consumer Electronics最新文献_第4页

Implicit Multi-Scale Swin Transformer Network for Image Denoising 隐式多尺度Swin变压器网络图像去噪

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565390

Qi Zhang;Yuwei Ding;Weiqi Zhang;Yian Zhu;Bob Zhang;Jerry Chun-Wei Lin

{"title":"Implicit Multi-Scale Swin Transformer Network for Image Denoising","authors":"Qi Zhang;Yuwei Ding;Weiqi Zhang;Yian Zhu;Bob Zhang;Jerry Chun-Wei Lin","doi":"10.1109/TCE.2025.3565390","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565390","url":null,"abstract":"Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5584-5594"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867641","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}

引用次数: 0

Intelligent Decision Support Systems for Energy-Efficient Autonomous Systems in Consumer Electronics 消费类电子产品节能自主系统的智能决策支持系统

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565596

Guangwei Zhang;Zhenyu Wu

{"title":"Intelligent Decision Support Systems for Energy-Efficient Autonomous Systems in Consumer Electronics","authors":"Guangwei Zhang;Zhenyu Wu","doi":"10.1109/TCE.2025.3565596","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565596","url":null,"abstract":"This paper proposes an AI-based decision support system for enhancing energy efficiency in autonomous systems within consumer electronics. The primary objective is to optimize energy consumption by leveraging advanced Recurrent Neural Networks (RNNs) combined with Adaptive Moment Estimation (Adam). The proposed intelligent framework incorporates real-time data processing, prediction, and adaptive decision-making to reduce energy usage across a variety of devices, such as smart thermostats, robotic vacuum cleaners, and IoT-enabled lighting systems. Key methodologies include the integration of RNNs to model temporal energy consumption patterns and the Adam optimizer for efficient model training. The results demonstrate significant energy savings with improvements in prediction accuracy compared to traditional approaches. The findings suggest that the AI-based system can provide substantial reductions in energy consumption while maintaining or improving device performance. The research has important implications for the future of energy-efficient consumer electronics, particularly as the demand for smart devices grows.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4714-4720"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867642","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}

引用次数: 0

Machine Learning-Based Depth Estimation for Energy Optimization in Smart Camera Systems 基于机器学习的智能相机系统能量优化深度估计

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565287

Tong Su;Yanan Jiang;Cuihua Hu

引用次数: 0

Lightweight Width-Depth Scalable Implicit Neural Representation for Progressive Image Compression 渐进式图像压缩的轻量级宽度-深度可扩展隐式神经网络表示

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565495

Qingyu Mao;Wenming Wang;Yongsheng Liang;Chenhu Xiao;Fanyang Meng;Gwanggil Jeon

{"title":"Lightweight Width-Depth Scalable Implicit Neural Representation for Progressive Image Compression","authors":"Qingyu Mao;Wenming Wang;Yongsheng Liang;Chenhu Xiao;Fanyang Meng;Gwanggil Jeon","doi":"10.1109/TCE.2025.3565495","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565495","url":null,"abstract":"Image compression approaches using implicit neural representation (INR) have recently gained attention for their lightweight nature, compactness, and fast decoding, showing promise for edge computing in consumer devices. Specifically, INR-based image compression methods implicitly store each image within a lightweight neural network, which serves as a compact representation of the image. However, most existing methods are limited to representing single-quality images with fixed-size models, which necessitates training separate models independently for images at varying quality levels, leading to additional training and storage costs. To tackle this problem, we propose a progressive image compression method based on Width-Depth Scalable Implicit Neural Representation (WDS-INR), which are composed of executable sub-networks of varying scales. By adjusting the scale of the sub-networks, WDS-INR can represent images at different quality levels while supporting progressive transmission. The scalable architecture of WDS-INR makes it well-suited for deployment on mobile and IoTs devices. Furthermore, we propose a band-limited initialization scheme that enhances both the representation capabilities and training stability of the WDS-INR. Finally, we introduce a meta-learning approach to the base sub-network to accelerate encoding <inline-formula> <tex-math>$(4 times text { faster})$ </tex-math></inline-formula>. Experimental results demonstrate that the proposed method outperforms the baseline in rate-distortion performance <inline-formula> <tex-math>$(+ 0.28~dB {~text {PSNR}})$ </tex-math></inline-formula>, while enabling scalable bit-rates with progressive decoding.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5666-5675"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867991","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}

引用次数: 0

A Dynamic Transformer Network for Vehicle Detection 一种车辆检测的动态变压器网络

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565318

Chunwei Tian;Kai Liu;Bob Zhang;Zhixiang Huang;Chia-Wen Lin;David Zhang

{"title":"A Dynamic Transformer Network for Vehicle Detection","authors":"Chunwei Tian;Kai Liu;Bob Zhang;Zhixiang Huang;Chia-Wen Lin;David Zhang","doi":"10.1109/TCE.2025.3565318","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565318","url":null,"abstract":"Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at <uri>https://github.com/hellloxiaotian/DTNet</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2387-2394"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868235","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}

引用次数: 0

Artificial Intelligence-Enabled Image Processing for Energy Optimization in Computer Vision Systems 基于人工智能的图像处理在计算机视觉系统中的能量优化

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565308

Jingnan Duan;Jun Li

{"title":"Artificial Intelligence-Enabled Image Processing for Energy Optimization in Computer Vision Systems","authors":"Jingnan Duan;Jun Li","doi":"10.1109/TCE.2025.3565308","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565308","url":null,"abstract":"This research investigates the integration of Deep Q-Networks (DQN) and Ant Colony Optimization (ACO) in developing a hybrid framework aimed at optimizing energy consumption in computer vision systems, while ensuring performance in image processing tasks. With the increasing computational demands of modern applications, energy efficiency has become critical. The proposed methodology combines DQN’s ability to enhance decision-making processes with ACO’s exploration and exploitation strategies, resulting in efficient energy management. Key findings indicate that the DQN-ACO approach reduces energy consumption by 30% compared to baseline methods, while simultaneously improving accuracy by 8.24% and processing speed by 20%. Moreover, the framework exhibits strong adaptability in dynamic environments, leading to improvements in throughput and overall system performance. The outcomes of this research have significant implications for industries such as autonomous driving, surveillance, and mobile computing, where energy efficiency is paramount. By advancing both artificial intelligence and computer vision, this study contributes to the development of sustainable technology solutions, laying the foundation for future innovations in energy-efficient intelligent systems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4684-4691"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868228","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}

引用次数: 0

Application of Remote Sensing Technologies in Monitoring and Managing Renewable Energy Sources 遥感技术在可再生能源监测与管理中的应用

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565573

Yong Han;Xiaoliang Zhang;Jie Liu;Guangchun Liu;Weitao Yan

{"title":"Application of Remote Sensing Technologies in Monitoring and Managing Renewable Energy Sources","authors":"Yong Han;Xiaoliang Zhang;Jie Liu;Guangchun Liu;Weitao Yan","doi":"10.1109/TCE.2025.3565573","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565573","url":null,"abstract":"This paper develops a novel hybrid model based on Generative Adversarial Networks (GANs) and Differential Evolution (DE) to enhance remote sensing data and optimize resource assessment models for renewable energy management. GANs were employed to improve the resolution and quality of satellite imagery, addressing the challenges of low-resolution data and incomplete information. Quantitative evaluations, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), demonstrated significant improvements in image quality, facilitating more accurate site assessments and predictive modeling. DE was applied to optimize key parameters such as sensor configurations and image enhancement algorithms, leading to enhanced accuracy in resource maps and reduced operational costs. The hybridization of GANs and DE created a comprehensive workflow that allowed for improved decision-making and efficient deployment. The proposed hybrid framework was shown to achieve higher prediction accuracy, exemplified by performance metrics such as Mean Absolute Error and R-squared values. Simulation results on case studies highlighted successful applications in renewable energy projects, emphasizing the potential of this integrated approach to drive cost-effective and scalable solutions.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4729-4735"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868334","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}

引用次数: 0

An Intelligent Framework for Optimal Consumer Electronics’ Management in Smart Pocket Parks for Stimulating the Vitality of the Elderly 激发老年人活力的智能口袋公园中消费电子产品优化管理的智能框架

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565411

Lingling Zhu;Noor Aimran Samsudin;Zuhra Junaida Mohamad Husny Hamid;Haipeng Xu

{"title":"An Intelligent Framework for Optimal Consumer Electronics’ Management in Smart Pocket Parks for Stimulating the Vitality of the Elderly","authors":"Lingling Zhu;Noor Aimran Samsudin;Zuhra Junaida Mohamad Husny Hamid;Haipeng Xu","doi":"10.1109/TCE.2025.3565411","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565411","url":null,"abstract":"This study focuses on optimal energy management of smart pocket parks in smart city for promoting elderly vitality based on consumer electronic devices. Recognizing the challenges elderly individuals face in maintaining an active lifestyle, the proposed system integrates innovative smart energy management system to provide energy demand of personalized sports activities compatible with users’ needs. In the proposed Deep Q-Learning (DQL) smart model, the system continuously learns from user interactions and health data to tailor recommendations, ensuring optimal energy consumption of devices and improvement in fitness levels. A key feature of this approach is the emphasis on energy management optimization for the devices used in the park. Through smart energy solutions, including solar-powered devices and efficient energy usage models, the system minimizes the environmental impact while ensuring consistent performance. Simulated Annealing (SA) is employed to fine-tune system parameters and avoid energy wastage, ensuring that devices operate at peak efficiency. The combination of DQL and SA allows for a synergistic system that not only promotes physical activity but also ensures sustainability and energy efficiency in smart cities. This methodology sets the stage for future advancements in urban health solutions for the elderly.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4670-4676"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868121","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}

引用次数: 0

SMDFusion: A Self-Supervised Fusion for Infrared and Visible Images via Cross-Modal Random Noise Masked Encoding and Difference Perception SMDFusion：一种基于交叉模态随机噪声掩蔽编码和差异感知的自监督红外和可见光图像融合

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565680

Mingchuan Tan;Rencan Nie;Jinde Cao;Ying Zhang

{"title":"SMDFusion: A Self-Supervised Fusion for Infrared and Visible Images via Cross-Modal Random Noise Masked Encoding and Difference Perception","authors":"Mingchuan Tan;Rencan Nie;Jinde Cao;Ying Zhang","doi":"10.1109/TCE.2025.3565680","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565680","url":null,"abstract":"Infrared and visible image fusion (IVIF) aims to merge images from both modalities of the same scene into a single image, enabling comprehensive information display and better support for visual computing tasks. Nevertheless, existing methods often overlook pixel-level relationships and struggle to effectively eliminate redundant information. To this end, we propose SMDFusion, a novel framework for fusing infrared and visible images using cross-modal noise-masked encoding and cross-modal differential perception information coupling. The framework consists of a self-supervised learning network (SSLN) and an unsupervised fusion network (UFN). Regarding the SSLN, the noise random masked encoder learns pixel-level relationships by employing a grid structure for multi-scale feature mapping that facilitates information exchange among different scales. The network is optimized with a self-supervision strategy for better representation learning. As for the UFN, symmetrical grid structures and multi-scale attention mechanisms are utilized to integrate intra-modal features while the cross-modal difference perception (CDP) module eliminates redundant information between modalities and conditionally captures complementary perception. The fusion image is synthesized by computing the modality-specific contribution estimation. Qualitative and quantitative experimental results demonstrate that SMDFusion outperforms representative methods in the task of multi-modal information fusion as well as supporting downstream tasks. The code is available at:<uri>https://github.com/rcnie/IVIF-SMDFusion</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2579-2591"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868391","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}

引用次数: 0

Big Data-Intelligence Analytics for Energy Optimization in IoT-Enabled Smart Home Devices 面向物联网智能家居设备能源优化的大数据智能分析

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-29 DOI: 10.1109/TCE.2025.3565590

Yihong Li;Qiang Song

{"title":"Big Data-Intelligence Analytics for Energy Optimization in IoT-Enabled Smart Home Devices","authors":"Yihong Li;Qiang Song","doi":"10.1109/TCE.2025.3565590","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565590","url":null,"abstract":"This article explores the integration of Artificial Intelligence (AI) and Big Data Analytics to optimize energy consumption in IoT-enabled smart home devices. It presents a robust analytical framework that leverages Variational Autoencoders (VAEs) for feature extraction and Differential Evolution (DE) for optimizing energy management parameters. Data was gathered from various IoT devices, including energy usage patterns, occupancy data, and environmental conditions. The results show a notable 40% reduction in energy consumption, leading to annual cost savings of up to <inline-formula> <tex-math>${$}300$ </tex-math></inline-formula> per household. Moreover, user satisfaction increased by 25%, with participants reporting heightened awareness and engagement in energy conservation. The study highlights how the proposed framework efficiently identifies common usage patterns and optimizes energy distribution while preserving user comfort. These findings reinforce the potential of AI-driven analytics in improving energy efficiency in smart homes, demonstrating that advanced algorithms not only support energy conservation but also promote active user participation in sustainability efforts.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4721-4728"},"PeriodicalIF":10.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868016","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}

引用次数: 0