IET Computers and Digital Techniques最新文献_第4页

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-11-18

引用次数: 0

A Systematic Literature Review on the Applications, Models, Limitations, and Future Directions of Generative Adversarial Networks 关于生成对抗网络的应用、模型、限制和未来方向的系统文献综述

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-11-18 DOI: 10.1049/cdt2/5384331

Sunawar khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan, Wasim Ahmad, Afsha Bibi, Habib Hamam

{"title":"A Systematic Literature Review on the Applications, Models, Limitations, and Future Directions of Generative Adversarial Networks","authors":"Sunawar khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan, Wasim Ahmad, Afsha Bibi, Habib Hamam","doi":"10.1049/cdt2/5384331","DOIUrl":"10.1049/cdt2/5384331","url":null,"abstract":"Generative adversarial networks (GANs), a subset of deep learning, have demonstrated breakthrough performance in domains such as computer vision (CV) and natural language processing (NLP), particularly in surveillance, autonomous driving, and automated programing assistance. Based on game theory principles, GANs utilize a generator–discriminator architecture to produce high-quality synthetic data. This study conducts a systematic literature review (SLR) to comprehensively assess the development, applications, limitations, and security-related advancements of GANs. It examines foundational models and key architectural variants, providing a critical evaluation of their roles in NLP and CV. This research explores the integration of GANs into the domain of security, highlighting their applications in information security, cybersecurity, and artificial intelligence (AI)-driven defense mechanisms. The study also discusses prominent evaluation metrics such as inception score (IS), Fréchet inception distance (FID), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) to assess GAN performance. Key strengths of GANs, including their ability to generate high-resolution data and support domain adaptation, are emphasized as driving factors for their continued evolution and adoption.","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2025 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/5384331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145572159","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}

引用次数: 0

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-11-12

引用次数: 0

Watermarking of Transient Fault-Detectable IP Designs Using Multivariate HLS Scheduling Based Multimodal Security 基于多模态安全的多变量HLS调度的瞬态故障检测IP水印设计

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-11-12 DOI: 10.1049/cdt2/5926846

Anirban Sengupta, Vishal Chourasia, Nabendu Bhui, Aditya Anshul

{"title":"Watermarking of Transient Fault-Detectable IP Designs Using Multivariate HLS Scheduling Based Multimodal Security","authors":"Anirban Sengupta, Vishal Chourasia, Nabendu Bhui, Aditya Anshul","doi":"10.1049/cdt2/5926846","DOIUrl":"10.1049/cdt2/5926846","url":null,"abstract":"Securing reusable hardware intellectual property (IP) cores used in system-on-chip (SoC) designs is crucial, due to global design supply chain that may introduce different points of security vulnerability. One of the major threats includes an untrustworthy entity in the SoC design house attempting piracy or falsely claiming ownership of the IP design. Further, owing to the importance of handling transient fault in hardware IP designs, design of fault-detectable IP designs has become a standard practice in the community. However, these fault-detectable IP designs are also similarly prone to hardware threats such as IP piracy and false claim of IP ownership. Therefore, robust sturdy countermeasure for fault-detectable IP designs against such threats is essential. This paper presents a detective countermeasure using proposed novel hardware watermarking methodology for transient fault-detectable IP designs. The proposed IP watermarking methodology introduces a novel multivariate encoded high-level synthesis (HLS) scheduling based multimodal security framework. The proposed approach is capable of embedding a robust, unique, and nonreplicable watermark in the HLS register allocation phase of fault-detectable IP design. The proposed watermarking technique is more robust than the prior watermarking approaches in terms of reduced probability of coincidence (PC; upto ~10−8), stronger tamper tolerance (TT; upto ~10130), and lower watermark decoding probability at 0% design cost overhead.","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2025 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/5926846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521816","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}

引用次数: 0

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-06-06

引用次数: 0

A Temperature Noise Correction Method for CMOS Spatial Camera Using LSTM With Attention Mechanism 基于LSTM的CMOS空间相机温度噪声校正方法

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-06-06 DOI: 10.1049/cdt2/6670185

Long Cheng, Xueying Wang, Jing Xu

{"title":"A Temperature Noise Correction Method for CMOS Spatial Camera Using LSTM With Attention Mechanism","authors":"Long Cheng, Xueying Wang, Jing Xu","doi":"10.1049/cdt2/6670185","DOIUrl":"10.1049/cdt2/6670185","url":null,"abstract":"This study presents an innovative temperature-induced random noise correction method for complementary metal oxide semiconductor (CMOS) spatial cameras using an attention mechanism-enhanced long short-term memory (LSTM) model. The model, specifically designed to address pixel drift and random noise issues in CMOS space cameras due to temperature variations, incorporates a multilayer LSTM network with an attention mechanism. This study comprehensively examines the temperature-induced variations in noise characteristics of CMOS cameras across diverse thermal conditions, encompassing in-depth analyses of both dark-field and light-field scenarios. Through detailed pixel-level analysis, the study quantifies the influence of temperature on pixel values and critical performance parameters such as internal nonuniformity within the camera. The experimental results show that under the dark field condition, the fitting variance between the predicted value and the measured value ranges from 0.29585 to 5.798307. After correction in light field conditions, the average variance of images decreases to 0.29, the mean signal-to-noise ratio (SNR) increases to 80, and the photo response nonuniformity (PRNU) mean drops to 0.0161%. Compared to precorrection levels, these key metrics show significant improvements, with an average 83.57-fold reduction, 1.89-fold increase, and 84.98-fold decrease, respectively. These results confirm the effectiveness of the deep learning method in correcting temperature-induced noise, highlighting the potential for practical engineering applications.","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2025 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/6670185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220342","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}

引用次数: 0

GRSNet: An Ultra-Lightweight Neural Network for 3D Point Cloud Classification and Segmentation GRSNet：一种用于三维点云分类和分割的超轻量级神经网络

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-05-12 DOI: 10.1049/cdt2/7934018

Zourong Long, Gen Tan, You Wu, Hong Yang, Chao Ding

{"title":"GRSNet: An Ultra-Lightweight Neural Network for 3D Point Cloud Classification and Segmentation","authors":"Zourong Long, Gen Tan, You Wu, Hong Yang, Chao Ding","doi":"10.1049/cdt2/7934018","DOIUrl":"10.1049/cdt2/7934018","url":null,"abstract":"The processing of point cloud data has become a significant area of research in the modern field of perception. Classification and segmentation are critical tasks in autonomous driving, environmental perception, and digital twins. Algorithms that directly extract features from raw point cloud data have simple architectures, but they are constrained by computational demands and limited efficiency. This makes effective deployment on resource-limited devices challenging. This article introduces GRSNet, an ultra-lightweight algorithm. The principal innovation is a new sampling method named golden ratio sampling (GRS), which generates sampling point indices directly using the golden ratio to subsequently locate the corresponding sampling points. This method efficiently extracts representative points from point cloud data and integrates them into deep networks. Leveraging GRS, this study combines the concepts from GhostNet and self-attention mechanisms to develop a feature extraction module dubbed the SA_Ghost Block, forming the core of GRSNet. Comparative experiments with leading algorithms on established point cloud open-source datasets demonstrate that GRSNet achieves superior performance, maintaining only 0.7 M parameters.","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2025 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/7934018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939359","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}

引用次数: 0

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-05-12

引用次数: 0

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-05-10

引用次数: 0

Application of Lightweight Target Detection Algorithm Based on YOLOv8 for Police Intelligent Moving Targets 基于YOLOv8的轻型目标检测算法在警用智能运动目标中的应用

IF 0.8 4区计算机科学

IET Computers and Digital Techniques Pub Date : 2025-05-10 DOI: 10.1049/cdt2/9984821

Yanjie Zhang, Xiaojun Liu, Yuehan Shi, Zecong Ding, Xiaoming Zhang

{"title":"Application of Lightweight Target Detection Algorithm Based on YOLOv8 for Police Intelligent Moving Targets","authors":"Yanjie Zhang, Xiaojun Liu, Yuehan Shi, Zecong Ding, Xiaoming Zhang","doi":"10.1049/cdt2/9984821","DOIUrl":"10.1049/cdt2/9984821","url":null,"abstract":"This study presents an intelligent moving target to replicate mob attacks and other realistic events in police training to match actual fighting needs. The police intelligent moving target must deploy target detection algorithms on the hardware platform, but the traditional you only look once (YOLO)v8 algorithm has a large framework, which will slow recognition due to the hardware platform’s lack of arithmetic power. In this study, GhostNet network architecture replaces YOLOv8′s backbone network for real-time target identification, improving recognition speed. The bounding box regression issue in target detection uses the scale invariant intersection over union (SIoU) loss function to increase prediction box overlapping and identification accuracy. Finally, BiFormer uses dynamic sparse attention for more flexible computational allocation and content perception. The method’s real-time detection speed is 4.81 frames per second (FPS) faster, [email protected] is 5.38% faster, mean average precision (mAP)@0.5:0.95 is 4.19% faster, and parameter volume is 5.81 M less than the original approach. The approach developed in this work has several applications in real-time target identification and lightweight deployment.","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2025 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/9984821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930499","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}

引用次数: 0