{"title":"IEEE Consumer Technology Society Board of Governors","authors":"","doi":"10.1109/TCE.2025.3561644","DOIUrl":"https://doi.org/10.1109/TCE.2025.3561644","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"C3-C3"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11038973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308204","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}
{"title":"Guest Editorial AI-Generated Content Empowered Healthcare Electronics","authors":"Gwanggil Jeon;Joel Rodrigues;Shiping Wen;Junxin Chen;Nan Ji;Abdellah Chehri","doi":"10.1109/TCE.2025.3553026","DOIUrl":"https://doi.org/10.1109/TCE.2025.3553026","url":null,"abstract":"The rapid advancements in AI-Generated Content (AIGC) have revolutionized various domains, including consumer electronics and healthcare technologies. AIGC’s ability to generate high-quality text, images, and videos within seconds has reshaped human-computer interactions, from intelligent customer service to immersive virtual experiences. More importantly, its application in Healthcare Electronics (HE) has opened new frontiers, facilitating automated diagnostics, medical data synthesis, and intelligent healthcare predictions.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1319-1321"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11038991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323198","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}
{"title":"IEEE Consumer Technology Society Officers and Committee Chairs","authors":"","doi":"10.1109/TCE.2025.3561646","DOIUrl":"https://doi.org/10.1109/TCE.2025.3561646","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"C4-C4"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11038963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308480","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}
{"title":"Enhancing Code Transformation in Large Language Models Through Retrieval-Augmented Fine-Tuning","authors":"Jing-Ming Guo;Po-Yang Liu;Yi-Chong Zeng;Ting-Ju Chen","doi":"10.1109/TCE.2025.3565294","DOIUrl":"https://doi.org/10.1109/TCE.2025.3565294","url":null,"abstract":"Large language models (LLMs) have made substantial advancements in knowledge reasoning and are increasingly utilized in specialized domains such as code completion, legal analysis, and medical transcription, where accuracy is paramount. In such applications, document-specific precision is more critical than general reasoning capabilities. This paper proposes a novel approach based on Retrieval-Augmented Fine-Tuning (RAFT) to enhance model-generated outputs, particularly in code transformation tasks. RAFT integrates domain-specific knowledge, optimizing in-domain retrieval-augmented generation by training the model to discern the relationship between prompts, retrieved documents, and target outputs. This enables the model to extract relevant information while mitigating the impact of noise. Experimental results demonstrate that the proposed method improves accuracy of 2.4% and CodeBLEU of 1.3% for VB-to-C# code conversion, highlighting its effectiveness in domain-specific applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2342-2346"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308333","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":"Autonomous Vehicle Forensics: Investigating Data Streams for Traffic Prediction and Incident Mitigation","authors":"Vivek Srivastava;Sumita Mishra;Nishu Gupta;Eid Albalawi;Shakila Basheer","doi":"10.1109/TCE.2025.3564924","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564924","url":null,"abstract":"The growing implementation of autonomous cars in intelligent transportation systems requires solid traffic forecasting and incident prevention mechanisms. Yet, there are difficulties in attaining system interoperability and user acceptability. In this research, a deep learning-based framework is suggested for traffic forecasting and prevention based on the use of a forensic method on autonomous car data. A restricted boltzmann machine derives deep, weighted features which are subsequently handled by an adaptive dilated long short-term memory model optimized by using the position updated osprey optimization algorithm. Forecasted traffic data are analyzed further to formulate mitigation strategies such as optimized path planning. Experimental results demonstrate better performance compared to the baseline methods based on various metrics, highlighting the effectiveness of the framework in improving future transportation systems and autonomous vehicle forensics.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1211-1218"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323128","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 1st-Order NS-SAR⋅ADC With DCMS Switching Scheme for Smart Consumer Electronics Device","authors":"Xin Xin;Yuchen Li;Shimei Zhao;Dingyu Sun;Xingyuan Tong","doi":"10.1109/TCE.2025.3559687","DOIUrl":"https://doi.org/10.1109/TCE.2025.3559687","url":null,"abstract":"A novel Dual-Capacitor Merge-and-Split (DCMS) capacitor switching scheme is proposed for a Noise-Shaping (NS) Successive-Approximation-Register Analog-to-Digital-Converter (SAR ADC) for smart consumer electronics device. In comparison to the <inline-formula> <tex-math>$V_{mathrm { cm}}$ </tex-math></inline-formula>-based method, the proposed DCMS scheme significantly decreases the average switching energy by 93.7%, while optimizing integral and differential non-linearity by factors of <inline-formula> <tex-math>$surd 3$ </tex-math></inline-formula>/<inline-formula> <tex-math>$4times $ </tex-math></inline-formula> and <inline-formula> <tex-math>$surd 1$ </tex-math></inline-formula>/<inline-formula> <tex-math>$2times $ </tex-math></inline-formula>, respectively. By employing only <inline-formula> <tex-math>$V_{mathrm { cm}}$ </tex-math></inline-formula> reference, the DCMS scheme can avoid the <inline-formula> <tex-math>$V_{ref}$ </tex-math></inline-formula> generation circuit and mismatch between the <inline-formula> <tex-math>$V_{ref}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$V_{mathrm { cm}}$ </tex-math></inline-formula> compared to the tri-level or multiple-level switching schemes. Moreover, it maintains a stable common-mode output voltage, simplifying comparator design by aiding in dynamic offset suppression. A <inline-formula> <tex-math>$1{^{text {st}}}$ </tex-math></inline-formula>-order NS-SAR ADC is implemented with the proposed DCMS scheme in a 0.18-<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m CMOS process. Measurement results indicate it can achieve a Signal-to-Noise-and-Distortion Ratio (SNDR) of 66.5 dB with a power consumption of <inline-formula> <tex-math>$19.22~mu $ </tex-math></inline-formula>W from a 1-V supply, at a sampling rate of 1 MS/s. This yields a Schreier Figure-of-Merit (FoMs) of 161.6 dB and a Walden Figure-of-Merit (FoMw) of 89.28 fJ/conversion-step.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1298-1306"},"PeriodicalIF":4.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322946","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}
Etikala Aruna;Arun Sahayadhas;Ponugoti Kalpana;Surbhi B. Khan;Mohammad Tabrez Quasim;Ahlam Almusharrf;Fatima Asiri
{"title":"A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices","authors":"Etikala Aruna;Arun Sahayadhas;Ponugoti Kalpana;Surbhi B. Khan;Mohammad Tabrez Quasim;Ahlam Almusharrf;Fatima Asiri","doi":"10.1109/TCE.2025.3553741","DOIUrl":"https://doi.org/10.1109/TCE.2025.3553741","url":null,"abstract":"Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0- based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1201-1210"},"PeriodicalIF":4.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323175","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}
Udara Jayasinghe;Prabhath Samarathunga;Nimesh Pollwaththage;Yasith Ganearachchi;Thanuj Fernando;Anil Fernando
{"title":"Quantum Communication for Video Transmission Over Error-Prone Channels","authors":"Udara Jayasinghe;Prabhath Samarathunga;Nimesh Pollwaththage;Yasith Ganearachchi;Thanuj Fernando;Anil Fernando","doi":"10.1109/TCE.2025.3552930","DOIUrl":"https://doi.org/10.1109/TCE.2025.3552930","url":null,"abstract":"Quantum communication offers transformative potential for media transmission by addressing the limitations of classical communication systems. To realize this potential, the study proposes a quantum communication framework for transmitting compressed videos over error-prone channels, leveraging quantum superposition. Two channel coding schemes are analyzed: quantum error correction (three-qubit, five-qubit, and seven-qubit codes) and classical error correction (1/3 rate polar code), all operating within the same bandwidth constraints. The proposed systems are benchmarked against a classical communication system using 1/3 rate polar codes. Results show that the three-qubit error correction-based quantum communication system, while simple and efficient, achieves significant performance gains over both classical error correction-based quantum and classical communication systems, with up to 41.42 dB in peak signal-to-noise ratio (PSNR), 0.9639 in structural similarity index measure (SSIM), and 94.4042 in video multimethod assessment fusion (VMAF). However, the five-qubit and seven-qubit systems outperform the three-qubit system, with the seven-qubit system surpassing all others in high noise environments, demonstrating its robustness across various group of pictures (GOP) formats. These findings highlight the trade-offs between simplicity and complexity, as the three-qubit system is practical and efficient, while the five-qubit and seven-qubit channel codes offer higher fidelity and resilience at the cost of increased complexity.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1148-1155"},"PeriodicalIF":4.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314730","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}
Zhenyu Wu;Yipeng Li;Tingkai Hu;Chuandong Li;Zhen Luo
{"title":"Resolution Enhancement for mmWave Radar With Matrix Pencil-Based Coherent Extension","authors":"Zhenyu Wu;Yipeng Li;Tingkai Hu;Chuandong Li;Zhen Luo","doi":"10.1109/TCE.2025.3552947","DOIUrl":"https://doi.org/10.1109/TCE.2025.3552947","url":null,"abstract":"In this letter, a super-resolution strategy for millimeter-wave (mmWave) radar is proposed. The matrix pencil method is employed to estimate the principal components of radar signals, and the phase differences between successive chirps are compensated by reconstructing inter-chirp signals based on the estimated parameters. By coherently combining successive chirps, the equivalent bandwidth of the radar signal is significantly increased. The resolution improvements over existing algorithms are verified through simulations in practical scenarios. The proposed method shows potential to improve performance in vital sign detection and other life-care radar applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2350-2352"},"PeriodicalIF":4.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308438","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}