IEEE Transactions on Consumer Electronics最新文献_第10页

Mitigating RC-Delay Induced Accuracy Loss in Analog In-Memory Computing: A Non-Compromising Approach 减轻模拟内存计算中 RC 延迟引起的精度损失：不妥协的方法

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-19 DOI: 10.1109/TCE.2024.3445341

Saike Zhu;Cimang Lu;Xiang Qiu;Shifan Gao;Xiang Ding;Youngseo Kim;Yi Zhao

{"title":"Mitigating RC-Delay Induced Accuracy Loss in Analog In-Memory Computing: A Non-Compromising Approach","authors":"Saike Zhu;Cimang Lu;Xiang Qiu;Shifan Gao;Xiang Ding;Youngseo Kim;Yi Zhao","doi":"10.1109/TCE.2024.3445341","DOIUrl":"10.1109/TCE.2024.3445341","url":null,"abstract":"The Internet of Things (IoT) has proliferated ubiquitous information exchange between the physical and cyber worlds through consumer electronics, with a focus on moving computing power to edge terminals. Computing-in-memory (CIM) technology has emerged as a competitive candidate for edge computing because of its low power consumption and high performance. In order to achieve accurate inference for neural network models, it is crucial to comprehend the source of errors in the CIM-based analog computing paradigm. In this work, we analyzed the impact of random noises and output stabling times on the Programmable Linear Random Access Memory (PLRAM)-based CIM chip. Experimental results show that the impact of random noise is negligible. The output stabling time can be treated as RC delay, which is related to the weight distribution. We proposed a weight reordering strategy to achieve better performance without sacrificing computation accuracy. Experiments with a commercial 11-keyword speech recognition model show a 74.4% runtime reduction while maintaining a 95.6% classification accuracy.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7544-7550"},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189674","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

CORES: COde REpresentation Summarization for Code Search CORES：用于代码搜索的 COde REpresentation 总结

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-16 DOI: 10.1109/TCE.2024.3445139

Xu Zhang;Xiaoyu Hu;Deyu Zhou

{"title":"CORES: COde REpresentation Summarization for Code Search","authors":"Xu Zhang;Xiaoyu Hu;Deyu Zhou","doi":"10.1109/TCE.2024.3445139","DOIUrl":"10.1109/TCE.2024.3445139","url":null,"abstract":"With the growth of the consumer electronics market, the software development industry is facing new opportunities and an increased focus on code retrieval techniques to improve efficiency and reduce costs. Code search aims to retrieve and reuse code from extensive repositories based on a search query with specific requirements. Recently, pre-trained model-based approaches have become popular because of grasping semantic representations of code snippets and search queries accurately. However, such approaches ignore the inconsistency between code and query statements due to the redundant tokens, such as definitions and punctuation marks in the code snippets, which hinder the matching accuracy. To tackle such disadvantage, in this paper, two strategies are proposed based on explicit or implicit code representation summarization. By summarizing the code representation, the redundancy in the code is removed and the inconsistency between code and query statements is alleviated. For the explicit code representation summarization-based strategy, different views of contextual information are obtained and summarized based on different scales of pyramidal dilated convolution. As to the implicit code representation summarization-based strategy, covariance is directly applied to constrain the code representation to ensure de-redundancy. Experimental results on six benchmark datasets show both strategies outperform the current State-Of-The-Art model CORES by 1.2% on average MRR scores.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 3","pages":"6095-6104"},"PeriodicalIF":4.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189728","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

Dynamic Anti-Jamming Strategy in SIoT: A Stackelberg-Matching Game Approach SIoT 中的动态抗干扰策略：堆栈伯格匹配博弈方法

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-16 DOI: 10.1109/tce.2024.3412166

Yunfan Zhang, Feihuang Chu, Luliang Jia, Miao Yu, Wenting Cao

引用次数: 0

Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning 确保零信任物联网数据隐私：区块链中使用联盟学习的差异化隐私保护

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-16 DOI: 10.1109/tce.2024.3444824

Altaf Hussain, Wajahat Akbar, Tariq Hussain, Ali Kashif Bashir, Maryam M. Al Dabel, Farman Ali, Bailin Yang

引用次数: 0

An Innovative Secure and Privacy-Preserving Federated Learning Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks 基于联合学习的创新型安全和隐私保护混合深度学习模型，用于互联网支持的无线传感器网络中的入侵检测

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-14 DOI: 10.1109/tce.2024.3442015

Soumya Ranjan Jena, Mohammad Zia Ur Rahman, Deepak K. Sinha, P. Rajendra kumar, Vrince Vimal, Kamred Udham Singh, Thalakola Syamsundararao, J.N.V.R. Swarup Kumar, Balajee J

引用次数: 0

Empowering Consumer Electric Vehicle Mobile Charging Services With Secure Profit Optimization 通过安全的利润优化为消费者提供电动汽车移动充电服务

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-13 DOI: 10.1109/tce.2024.3442932

Zeinab Teimoori, Abdulsalam Yassine, M. Shamim Hossain

引用次数: 0

Joint Optimization of Service Caching and Task Offloading for Customer Application in MEC: A Hybrid SAC Scheme MEC 中客户应用服务缓存和任务卸载的联合优化：混合 SAC 方案

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-13 DOI: 10.1109/tce.2024.3443168

Yang Xu, Ziyu Peng, Nanxi Song, Yu Qiu, Cheng Zhang, Yaoxue Zhang

引用次数: 0

XAI-Empowered MRI Analysis for Consumer Electronic Health XAI 为消费者电子健康提供磁共振成像分析功能

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-13 DOI: 10.1109/tce.2024.3443203

Al Amin, Kamrul Hasan, M. Shamim Hossain

引用次数: 0

A Semantic and Syntactic Enhanced Neuromorphic Computing System and its Application in Consumer Sentiment Analysis 语义和句法增强型神经形态计算系统及其在消费者情感分析中的应用

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-13 DOI: 10.1109/tce.2024.3442882

Xiaoyue Ji, Liyan Zhu, Chenhao Hu, Yifeng Han, Donglian Qi

引用次数: 0

FedHLC: A Novel Federated Learning Algorithm Targeting Heterogeneous and Long-Tailed Data for Efficient Image Classification in Consumer Electronics FedHLC：针对异构和长尾数据的新型联合学习算法，用于消费电子产品中的高效图像分类

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-13 DOI: 10.1109/TCE.2024.3443022

Zhiguo Qu;Zhiwei Liang

{"title":"FedHLC: A Novel Federated Learning Algorithm Targeting Heterogeneous and Long-Tailed Data for Efficient Image Classification in Consumer Electronics","authors":"Zhiguo Qu;Zhiwei Liang","doi":"10.1109/TCE.2024.3443022","DOIUrl":"10.1109/TCE.2024.3443022","url":null,"abstract":"Federated learning (FL) is an effective technique for image classification in consumer electronics. This paper proposes a new FL algorithm called FedHLC to address heterogeneous and long-tailed data. Its architecture comprises a feature extractor and a classifier. The training process of FedHLC is divided into two distinct stages. In the first stage, it focuses on training feature extractors on the client side and conducts feature representation learning. This approach develops a robust and generalizable representation for digital image data. The second stage involves retraining the classifier on the server side with generated virtual features. This step not only safeguards client privacy but also effectively mitigates model bias towards tail categories. In addition, FedHLC incorporates a novel balancing factor that dynamically adjusts the influence of regularization term. It allows a flexible focus shift between global objectives and local objectives. The simulation experiments on benchmark datasets demonstrate that FedHLC outperforms the baseline algorithms including CReFF, FedAvg, FedProx and FedNova in terms of accuracy when dealing with heterogeneous and long-tailed data. Furthermore, FedHLC can not only achieve good convergence but also attain an accuracy peak of 89.24%, marking a substantial advancement in the field of FL for image classification in consumer electronics. The code is available at \u0000<uri>https://github.com/Kiritoliang/FedHLC</uri>\u0000.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7266-7278"},"PeriodicalIF":4.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189682","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