IET Computers and Digital Techniques最新文献

{"title":"Novel Fault- Detection Implementation of FIR Filters Based on Multilevel Current-Mode Berger Codes","authors":"Reza Omidi","doi":"10.1049/cdt2/8823169","DOIUrl":"https://doi.org/10.1049/cdt2/8823169","url":null,"abstract":"<p>Soft errors pose a significant threat to the reliability of digital circuits, especially in harsh environments. While traditional error detection and correction techniques, such as Hamming codes and dual modular redundancy (DMR), have been widely employed, they often incur significant hardware overhead. This article presents a novel approach to enhancing the fault tolerance of finite impulse response (FIR) filters using multivalued logic (MVL). By leveraging the inherent redundancy of MVL, we propose several fault detection schemes based on current-mode MVL (CMMVL). These proposed architectures offer a significant reduction in hardware overhead compared to state-of-the-art techniques, often achieving at least a 60% decrease in area overhead. However, due to the power consumption characteristics of CMMVL, the proposed methods may introduce a higher power overhead. Among the proposed schemes, the time-division multiplexed checker (TDMC), technique demonstrates on average a 30% reduction in power overhead compared to the best previous method. Our proposed architectures achieve an area overhead of less than 40%, a significant reduction compared to the 110% overhead of conventional DMR. Among these, the TDMC architecture also reduces power overhead to 77%, outperforming DMR’s 108% power overhead. However, due to the high delay of the checker circuit, our methods do not show a significant improvement in the power-delay product (PDP). In the best-case prelayout scenario, the TDMC overhead is similar to DMR. Unfortunately, in the post-layout simulation, this overhead increases by as much as 110%.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/8823169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148078988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148076972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Artistic Image Style Transfer Based on Fusion Generative Adversarial Networks and VGG16","authors":"Nan Chen,&nbsp;Qingyong Meng,&nbsp;Lei Chen","doi":"10.1049/cdt2/3970805","DOIUrl":"https://doi.org/10.1049/cdt2/3970805","url":null,"abstract":"<p>Artistic image style transfer enables the synthesis of visually creative images by blending the structure of a content image with the textures of an artistic reference. However, existing models struggle to simultaneously preserve structural content and achieve coherent multi-style fusion, often producing distorted outputs with limited controllability. To address these limitations, we propose Fusion GAN, a dual-branch generative framework that integrates a content–style encoder, a multi-style adaptive fusion block (MSAFB), and perceptual constraints derived from VGG16. The architecture enables controlled blending of multiple style statistics while maintaining spatial consistency. Unlike standard GAN-based style transfer approaches, the proposed model incorporates a hybrid loss formulation combining perceptual content loss, Gram-matrix-based style loss, and adversarial realism regularization. The model is trained using MS-COCO (content) and WikiArt (style) datasets under a fully reproducible experimental setup with fixed hyperparameters and repeated trials. An innovative image recognition approach for real-time video surveillance is presented using a fusion generative adversarial network (fusion GAN). The method improves recognition robustness by separating structural features from appearance variations and adaptively fusing visual information under dynamic surveillance conditions. Quantitative results indicate partial improvements in structural retention but also highlight significant challenges, such as high Fréchet inception distance (FID) and low peak signal-to-noise ratio (PSNR)/structural similarity index (SSIM) values, demonstrating that the model remains an exploratory prototype rather than a high-performance system. Qualitative outputs show successful stylistic blending, while the analysis identifies key failure points and areas for optimization. Overall, fusion GAN provides a structured and extensible foundation for multi-style transfer, but further refinement is needed to achieve competitive realism and perceptual quality. The performance of the proposed fusion GAN model is evaluated using the MS-COCO dataset for content images and the WikiArt dataset for artistic styles. Evaluation metrics such as FID, SSIM, and PSNR are employed to assess the model’s effectiveness in content preservation and style fidelity.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/3970805","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Big Data and Business Intelligence Technologies for Integrating Technology Into College Student Entrepreneurship","authors":"Yuanhao Zhao,&nbsp;Liangliang Qiao,&nbsp;Yuanyuan Rong,&nbsp;Zheng Yan,&nbsp;Qingdan Hu","doi":"10.1049/cdt2/2077780","DOIUrl":"https://doi.org/10.1049/cdt2/2077780","url":null,"abstract":"<p>To solve the problem of lack of market insight and insufficient innovation creativity in entrepreneurship of college students, this paper aims to explore the application of data mining and predictive analysis technology in this field. Quantitative research methods are employed. First, large-scale data is collected, including relevant information, such as market trends, user needs, and competitor intelligence. Second, cluster analysis techniques are used to analyze and interpret these data. Third, historical data and features are leveraged to build predictive analytics models to identify potential market opportunities and innovations to evaluate the potential success of startup projects through predictive analytics. Finally, statistical analysis methods are used to quantify and compare data to obtain objective results. The results show that the number of users in specific fields is growing at a rate of 10% per year and is expected to reach 200 million in the next 3 years. Based on the predictive analysis model, the entrepreneurial success probability of the entrepreneurial project is evaluated, and the predictive model shows that the probability of project success is 80%. This data support helps entrepreneurs make informed plans, optimize project management and operational strategies, and provide more opportunities for college students to start their businesses. ig data–driven predictive analytics and business intelligence (BI) for enhancing college student entrepreneurship success. Therefore, the integration of big data and BI technology into college students’ entrepreneurship plays an important role, which can provide entrepreneurs with accurate market information and data support and improve the success rate of entrepreneurship.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/2077780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148096575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148096576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Statistical Performance Analysis Framework for Bundled-Data Asynchronous Circuits With Dynamic Voltage Scaling","authors":"Maryem Benyoussef,&nbsp;Claude Thibeault,&nbsp;Yvon Savaria","doi":"10.1049/cdt2/1529127","DOIUrl":"10.1049/cdt2/1529127","url":null,"abstract":"<p>As the demand for low-power electronic products grows, asynchronous circuits are considered a good alternative for addressing power consumption issues. Applying dynamic voltage scaling (DVS) in asynchronous circuits can further improve their power efficiency. However, asynchronous circuits face challenges, such as performance analysis considering voltage, temperature, and process variations. This paper proposes a new statistical performance analysis model for asynchronous pipelines. This model can be applied to two different styles of asynchronous circuits. The results show that this model has reasonable accuracy on estimated mean delay (2% error on average) compared to detailed analysis carried out with low-level Monte Carlo (MC) circuit simulations.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/1529127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Design and Implementation of the Behavior Judgment System for Long-Distance Track and Field Athletes","authors":"Juanjuan Zhu","doi":"10.1049/cdt2/9077598","DOIUrl":"10.1049/cdt2/9077598","url":null,"abstract":"<p>The traditional method of identifying abnormal behavior in athletes during long-distance track and field events, particularly through video surveillance, has limitations such as misjudgments and oversight. To address these challenges, this study explores the use of artificial intelligence (AI) and intelligent surveillance technology for event safety management. However, existing video-based abnormal behavior recognition methods face significant difficulties in adapting to marathon scenarios due to the complexities of outdoor surveillance footage. In response, this paper proposes innovative methods for recognizing abnormal behavior and identifying athletes using bib numbers, thereby enhancing the accuracy of behavior detection in marathon environments. Additionally, an AI-powered system for athlete identification and abnormal behavior detection is introduced to improve event monitoring and safety. The contributions of this study include the development of an attention-residual-based abnormal behavior recognition algorithm, a rotation-based object detection model for accurate athlete identification, and a deep learning–based intelligent recognition system for effective event management.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/9077598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147626378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148074086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}