Pub Date : 2025-08-22DOI: 10.1109/TCE.2025.3601582
Jiacui Huang;Hongtao Zhang;Mingbo Zhao;Zhou Wu;Yuping Liu
Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for generalizing code for guiding the robot navigation. However, these methods face limitations in instance-level and attribute-level navigation tasks as they cannot distinguish different instances of the same object. To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird’s-in-eye view. Such indexing is instance-level and attribute-level. In particular, when integrated with a large language model, IVLMap demonstrates the capability to i) transform natural language into navigation targets with instance and attribute information, enabling precise localization, and ii) accomplish zero-shot end-to-end navigation tasks based on natural language commands. Extensive navigation experiments are conducted. Simulation results illustrate that our method can achieve an average improvement of 14.4% in navigation accuracy. Code and demo are released at https://ivlmap.github.io/.
{"title":"Instance-Aware Visual Language Grounding for Consumer Robot Navigation","authors":"Jiacui Huang;Hongtao Zhang;Mingbo Zhao;Zhou Wu;Yuping Liu","doi":"10.1109/TCE.2025.3601582","DOIUrl":"https://doi.org/10.1109/TCE.2025.3601582","url":null,"abstract":"Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for generalizing code for guiding the robot navigation. However, these methods face limitations in instance-level and attribute-level navigation tasks as they cannot distinguish different instances of the same object. To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird’s-in-eye view. Such indexing is instance-level and attribute-level. In particular, when integrated with a large language model, IVLMap demonstrates the capability to i) transform natural language into navigation targets with instance and attribute information, enabling precise localization, and ii) accomplish zero-shot end-to-end navigation tasks based on natural language commands. Extensive navigation experiments are conducted. Simulation results illustrate that our method can achieve an average improvement of 14.4% in navigation accuracy. Code and demo are released at <uri>https://ivlmap.github.io/</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12519-12526"},"PeriodicalIF":10.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778152","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}
Pub Date : 2025-08-22DOI: 10.1109/TCE.2025.3601605
Omar H. Khater;Abdul Jabbar Siddiqui;M. Shamim Hossain;Aiman El-Maleh
Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds compete for essential resources with crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The adoption of automated computer vision technologies and ground agricultural consumer electronic vehicles in precision agriculture offers sustainable, low-carbon solutions. However, prior works suffer from issues such as low accuracy and precision, as well as high computational expense. This work proposes EcoWeedNet, a novel model that enhances weed detection performance without introducing significant computational complexity, aligning with the goals of low-carbon agricultural practices. The effectiveness of the proposed model is demonstrated through comprehensive experiments on the CottonWeedDet12 benchmark dataset, which reflects real-world scenarios. EcoWeedNet achieves performance comparable to that of large models (mAP@$0.5{=}95.2$ %), yet with significantly fewer parameters (approximately $textbf {4.21}%$ of the parameters of YOLOv4), lower computational complexity and better computational efficiency ($textbf {6.59}%$ of the GFLOPs of YOLOv4). These key findings indicate EcoWeedNet’s deployability on low-power consumer hardware, lower energy consumption, and hence reduced carbon footprint, thereby emphasizing the application prospects of EcoWeedNet in next-generation sustainable agriculture. These findings provide the way forward for increased application of environmentally-friendly agricultural consumer technologies.
{"title":"EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics","authors":"Omar H. Khater;Abdul Jabbar Siddiqui;M. Shamim Hossain;Aiman El-Maleh","doi":"10.1109/TCE.2025.3601605","DOIUrl":"https://doi.org/10.1109/TCE.2025.3601605","url":null,"abstract":"Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds compete for essential resources with crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The adoption of automated computer vision technologies and ground agricultural consumer electronic vehicles in precision agriculture offers sustainable, low-carbon solutions. However, prior works suffer from issues such as low accuracy and precision, as well as high computational expense. This work proposes EcoWeedNet, a novel model that enhances weed detection performance without introducing significant computational complexity, aligning with the goals of low-carbon agricultural practices. The effectiveness of the proposed model is demonstrated through comprehensive experiments on the CottonWeedDet12 benchmark dataset, which reflects real-world scenarios. EcoWeedNet achieves performance comparable to that of large models (mAP@<inline-formula> <tex-math>$0.5{=}95.2$ </tex-math></inline-formula>%), yet with significantly fewer parameters (approximately <inline-formula> <tex-math>$textbf {4.21}%$ </tex-math></inline-formula> of the parameters of YOLOv4), lower computational complexity and better computational efficiency (<inline-formula> <tex-math>$textbf {6.59}%$ </tex-math></inline-formula> of the GFLOPs of YOLOv4). These key findings indicate EcoWeedNet’s deployability on low-power consumer hardware, lower energy consumption, and hence reduced carbon footprint, thereby emphasizing the application prospects of EcoWeedNet in next-generation sustainable agriculture. These findings provide the way forward for increased application of environmentally-friendly agricultural consumer technologies.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12386-12397"},"PeriodicalIF":10.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778175","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}
Pub Date : 2025-08-19DOI: 10.1109/TCE.2025.3600307
Rajesh Kumar Chaudhary;Ravinder Kumar;Jaroslav Frnda;Niyaz Ahmad Wani;Muhammad Shahid Anwar;Jalel Ben-Othman
The extensive utilization of consumer electronic devices such as smartphones, smart wearables, and smart home technology has resulted in significant surge in data production. Due to storage and data transfer limitations, traditional machine learning techniques are sometimes impracticable for these distributed systems and can cause serious privacy problems. Federated Learning mitigates these issues by maintaining data on local devices. It updates models by consolidating locally trained outcomes on central server, which can be crucial in case of consumer electronic devices. Thus, to tackle these issues, this article presents new method named Dynamic inertia weight-based federated advanced particle swarm optimization (DIW-FedAPSO). It uses dynamic inertia weight strategy in advanced particle swarm optimization to select inertia weight dynamically for providing optimal velocity to consumer electronic devices and transmitting obtained optimal score after performing the local training instead of sending and averaging weights of devices as traditional federated learning method does. The experimental evaluations on different datasets (CelebA, FFHQ) under different non-iid data heterogeneity settings shows that proposed method attains better accuracy, while maintaining data privacy and enhances communication efficiency while minimizing number of communication rounds required to attain targeted accuracy over all datasets than other currently existing methods.
{"title":"Ameliorating Federated Learning Using Dynamic Inertia Weight-Based Advanced Particle Swarm Optimization for Consumer Electronic Devices","authors":"Rajesh Kumar Chaudhary;Ravinder Kumar;Jaroslav Frnda;Niyaz Ahmad Wani;Muhammad Shahid Anwar;Jalel Ben-Othman","doi":"10.1109/TCE.2025.3600307","DOIUrl":"https://doi.org/10.1109/TCE.2025.3600307","url":null,"abstract":"The extensive utilization of consumer electronic devices such as smartphones, smart wearables, and smart home technology has resulted in significant surge in data production. Due to storage and data transfer limitations, traditional machine learning techniques are sometimes impracticable for these distributed systems and can cause serious privacy problems. Federated Learning mitigates these issues by maintaining data on local devices. It updates models by consolidating locally trained outcomes on central server, which can be crucial in case of consumer electronic devices. Thus, to tackle these issues, this article presents new method named Dynamic inertia weight-based federated advanced particle swarm optimization <monospace>(DIW-FedAPSO)</monospace>. It uses dynamic inertia weight strategy in advanced particle swarm optimization to select inertia weight dynamically for providing optimal velocity to consumer electronic devices and transmitting obtained optimal score after performing the local training instead of sending and averaging weights of devices as traditional federated learning method does. The experimental evaluations on different datasets (CelebA, FFHQ) under different non-iid data heterogeneity settings shows that proposed method attains better accuracy, while maintaining data privacy and enhances communication efficiency while minimizing number of communication rounds required to attain targeted accuracy over all datasets than other currently existing methods.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12345-12357"},"PeriodicalIF":10.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778236","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}
Pub Date : 2025-08-18DOI: 10.1109/TCE.2024.3519018
Ahmed Farouk;Jingbo Wang;Rafael Sotelo
Quantum computers leverage the principles of quantum mechanics, including superposition and entanglement, allowing them to execute specific computations significantly faster than classical computers. The gate model is a common way to implement quantum algorithms, where the algorithms are broken down into a sequence of simple gates that operate on one or more quantum bits. This manipulation of a quantum computer involves a succession of unitary transformations that affect the different components of the superposition simultaneously, enabling significant parallel data processing and reducing the time of execution. As a result of these capabilities, quantum technology is expected to provide abilities and performance that are currently unattainable by classical methods. However, quantum hardware is under development and is prone to errors, which can negatively impact the performance of quantum methods. To address this challenge, error mitigation techniques are developed to reduce the impact of errors on the final outcomes. By leveraging the speedup offered by quantum hardware and using effective error mitigation techniques, quantum computing holds the promise of outperforming classical methods in various consumer applications (CA).
{"title":"Guest Editorial Quantum in Consumer Technology: Opportunities and Challenges","authors":"Ahmed Farouk;Jingbo Wang;Rafael Sotelo","doi":"10.1109/TCE.2024.3519018","DOIUrl":"https://doi.org/10.1109/TCE.2024.3519018","url":null,"abstract":"Quantum computers leverage the principles of quantum mechanics, including superposition and entanglement, allowing them to execute specific computations significantly faster than classical computers. The gate model is a common way to implement quantum algorithms, where the algorithms are broken down into a sequence of simple gates that operate on one or more quantum bits. This manipulation of a quantum computer involves a succession of unitary transformations that affect the different components of the superposition simultaneously, enabling significant parallel data processing and reducing the time of execution. As a result of these capabilities, quantum technology is expected to provide abilities and performance that are currently unattainable by classical methods. However, quantum hardware is under development and is prone to errors, which can negatively impact the performance of quantum methods. To address this challenge, error mitigation techniques are developed to reduce the impact of errors on the final outcomes. By leveraging the speedup offered by quantum hardware and using effective error mitigation techniques, quantum computing holds the promise of outperforming classical methods in various consumer applications (CA).","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6131-6133"},"PeriodicalIF":10.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867975","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}
Pub Date : 2025-08-18DOI: 10.1109/TCE.2024.3487864
Kai Liu;Song Guo;Penglin Dai;Rui Tan;Shan Du
{"title":"Guest Editorial of the Special section on Edge Intelligence and Its Applications to Consumer Electronics","authors":"Kai Liu;Song Guo;Penglin Dai;Rui Tan;Shan Du","doi":"10.1109/TCE.2024.3487864","DOIUrl":"https://doi.org/10.1109/TCE.2024.3487864","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4656-4659"},"PeriodicalIF":10.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-08-18DOI: 10.1109/TCE.2025.3584412
{"title":"IEEE Consumer Technology Society Officers and Committee Chairs","authors":"","doi":"10.1109/TCE.2025.3584412","DOIUrl":"https://doi.org/10.1109/TCE.2025.3584412","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"C4-C4"},"PeriodicalIF":10.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-08-18DOI: 10.1109/TCE.2024.3519759
Gaurav Dhiman;Atulya K. Nagar;Wattana Viriyasitavat
{"title":"Guest Editorial Blockchain-Assisted 6G Communication in Consumer Electronics: Applications, Challenges, and Opportunities","authors":"Gaurav Dhiman;Atulya K. Nagar;Wattana Viriyasitavat","doi":"10.1109/TCE.2024.3519759","DOIUrl":"https://doi.org/10.1109/TCE.2024.3519759","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3862-3864"},"PeriodicalIF":10.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-08-18DOI: 10.1109/TCE.2025.3563143
Honghao Gao;Walayat Hussain;Ramón J. Durán Barroso;Mohammad S. Obaidat
{"title":"Guest Editorial Special Section on “User Behavior Modeling for Trustworthy Recommendation over Consumer Electronics Products”","authors":"Honghao Gao;Walayat Hussain;Ramón J. Durán Barroso;Mohammad S. Obaidat","doi":"10.1109/TCE.2025.3563143","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563143","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"7211-7212"},"PeriodicalIF":10.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
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