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A novel real-time quality control system for 3D printing: A deep learning approach using data efficient image transformers

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Singh, Manveer Sharma, Pradyun Sharma, Satish Kumar Singh, Jasmeet Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala India Thapar Inst Engn & Technol Dept Mech Engn Patiala India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala India

Fused Deposition Modeling (FDM), an 3D printing technique being popular for rapidly fabricating polymeric prototypes as well as functional components with gradient structures such as scaffolds still faces significant hurdles in quality control and defect management. To overcome these limitations, a comprehensive approach has been proposed integrating advanced deep learning models with an Internet of Things (IoT) based quality control system. The research proposes a framework using Data-efficient image Transformer (DeiT) model, engineered to identify and classify three high-impact FDM defects: warping, layer delamination, and gaps in raster lines. The model has been fine-tuned on a curated dataset of original images, enhanced through pre-processing techniques. The DeiT model combined with a proposed Weighted Classification Accuracy (WCA) approach achieves an accuracy of 99.3%. Furthermore, the response time of the entire system is calculated to be 0.1121 s, providing real time monitoring and control. The research represents a significant step towards intelligent and optimized manufacturing systems in the context of Industry 4.0, addressing current challenges in FDM printing while paving the way for more autonomous and efficient 3D printing processes in the future.

关键词： deep learning Fused deposition modeling Vision transformers DeiT Quality control system Smart manufacturing IoT

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Evaluating image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4539-4539页

作者： Cho, Kyungwoon Bahn, Hyokyung Ewha Womans Univ Embedded Software Res Ctr Seoul 03760 South Korea Ewha Womans Univ Dept Comp Engn Seoul 03760 South Korea

Featured Application This work can be applied to enhance the robustness of image filtering systems in large-scale content platforms, specifically for detecting unauthorized images and their transformed versions, preventing the dissemination of manipulated *** image filtering systems have become essential in large-scale content platforms to prevent the dissemination of unauthorized data. While extensive research has focused on identifying images based on categories or visual similarity, the filtering problem addressed in this study presents distinct challenges. Specifically, it involves a predefined set of filtering images and requires real-time detection of whether a distributed image is derived from an unauthorized source. Although three major approaches-bitmap-based, image processing-based, and deep learning-based techniques-have been explored, no comprehensive comparison has been conducted. To bridge this gap, we formalize the concept of image equivalence and introduce performance metrics tailored for fair evaluation. Through extensive experiments, we derive the following key findings. First, bitmap-based methods are practically viable in real-world scenarios, offering reasonable detection rates and fast search speeds even under resource constraints. Second, despite their success in tasks such as image classification, deep learning-based methods underperform in our problem domain, highlighting the need for customized models and architectures. Third, image processing-based techniques demonstrate superior performance across all key metrics, including execution time and detection rates. These findings provide valuable insights into designing efficient image filtering systems for diverse content platforms, particularly for detecting unauthorized images and their transformations effectively.

关键词： image DNA fingerprinting unauthorized content image filtering system image equivalence

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Innovative Quantum PlasmoVision-Based Imaging for real-time deepfake Detection

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PLASMONICS 2025年 1-17页

作者： Maheshwari, R. Uma Jayasudha, A. R. Pandey, Binay Kumar Pandey, Digvijay Hindusthan Inst Technol Dept Elect & Commun Engn Coimbatore India Hindusthan Coll Engn & Technol Dept Comp Applicat Coimbatore India Govind Ballabh Pant Univ Agr & Technol Pantnagar Coll Technol Dept Informat Technol Pantnagar Udham Singh Nagar Uttarakhand India Dept Tech Educ Uttar Pradesh Kanpur India

In recent years, the proliferation of deepfake images has posed a substantial threat to media credibility, security, and privacy. Contemporary detection techniques, predominantly reliant on deep learning algorithms, fail to identify the nuanced pixel-level discrepancies inherent in deepfake material. This study introduces PlasmoVision, an innovative quantum-enhanced plasmonic imaging technology that incorporates AI-driven deep learning for highly sensitive real-time deepfake detection. deepfakes alter digital images and videos to produce very persuasive fraudulent content, rendering traditional detection methods ineffective. Plasmonic surface resonance technology, in conjunction with quantum dots, has the capacity to capture intricate image features that can disclose such alterations. Integrating deep learning into this detection system improves the accuracy and velocity of analysis. The PlasmoVision technology employs quantum dot-enhanced plasmonic arrays to detect sub-pixel-level resonance shifts resulting from light interaction with the image surface. The optical signals are analyzed with a sophisticated convolutional neural network (CNN) that categorizes images according to the plasmonic resonance data. The AI model is trained on a varied dataset of genuine and deepfake photos, attaining an ideal equilibrium between detection sensitivity and speed. real-time picture analysis is accomplished by swift plasmonic scanning and AI-driven classification. The suggested device attained an accuracy rate of 98.6% in identifying deepfakes within a test dataset, exhibiting a false positive rate of 1.2% and a false negative rate of 0.5%. The quantum-enhanced plasmonic system identified pixel abnormalities with a sensitivity of up to 10 nm, markedly surpassing conventional deepfake detection technologies. PlasmoVision real-time analysis capacity decreased processing time by 35% relative to traditional approaches, rendering it exceptionally appropriate for extensive and real-ti

关键词： Quantum dots Plasmonic surface resonance deepfake detection AI-driven deep learning Convolutional neural networks image authentication real-time analysis Sub-pixel detection Media security Optical sensing

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The Duo of Visual Servoing and deep learning-Based Methods for Situation-Aware Disaster Management: A Comprehensive Review

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COGNITIVE COMPUTATION 2024年第5期16卷 2756-2778页

作者： Jagatheesaperumal, Senthil Kumar Hassan, Mohammad Mehedi Hassan, Md. Rafiul Fortino, Giancarlo Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi 626005 Tamilnadu India King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia Cent Connecticut State Univ Dept Comp Sci New Britain CT USA Univ Calabria Dept Informat Modeling Elect & Syst I-87036 Arcavacata Di Rende CS Italy

Unmanned aerial vehicles (UAVs) have become essential in disaster management due to their ability to provide real-time situational awareness and support decision-making processes. Visual servoing, a technique that uses visual feedback to control the motion of a robotic system, has been used to improve the precision and accuracy of UAVs in disaster scenarios. The study integrates visual servoing to enhance UAV precision while exploring recent advancements in deep learning. This integration enhances the precision and efficiency of disaster response by enabling UAVs to navigate complex environments, identify critical areas for intervention, and provide actionable insights to decision-makers in real time. It discusses disaster management aspects like search and rescue, damage assessment, and situational awareness, while also analyzing the challenges associated with integrating visual servoing and deep learning into UAVs. This review article provides a comprehensive analysis to offer real-time situational awareness and decision support in disaster management. It highlights that deep learning along with visual servoing enhances precision and accuracy in disaster scenarios. The analysis also summarizes the challenges and the need for high computational power, data processing, and communication capabilities. UAVs, especially when combined with visual servoing and deep learning, play a crucial role in disaster management. The review underscores the potential benefits and challenges of integrating these technologies, emphasizing their significance in improving disaster response and recovery, with possible means of enhanced situational awareness and decision-making.

关键词： Unmanned aerial vehicles Disaster management Visual servoing deep learning Situational awareness Decision-making

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A deep learning based architecture for multi-class skin cancer classification

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Multimedia Tools and Applications 2024年第39期83卷 87105-87127页

作者： Mushtaq, Snowber Singh, Omkar Srinagar190006 India

One of the deadliest forms of skin cancer is malignant melanoma, developed by aberrant melanocyte cell development. Efficient diagnostic procedures are essential due to the rising prevalence of skin illnesses resulting from several variables such as infections, allergies, dietary habits, chemical exposure, genetic sensitivities, and lifestyle changes. An increasing number of deep Convolutional Neural Network (DCNN) prototypes are used for the computerized identification of skin cancer using dermoscopic images. The research aims to develop a detailed classification framework for the multi-class categorization of skin cancer carcinoma at an earlier phase using Hair removal image processing Technique and Ensemble Visual Geometry Group-16 (EVGG-16) based on a Convolution Neural Network. The main idea is to set different initializations to the same VGG-16 model three times and then to Ensemble the results. The Experimental results show that the VGG-16 model receives an average accuracy of 87% and an F1 score of 86%. The proposed Ensemble Visual Geometry Group-16 (EVGG-16) model receives an average accuracy of 88% and an F1-score of 88% without the hair removal image processing technique;the same model receives an average accuracy of 89% and an F1-score of 88% after applying the hair removal image processing technique. The publicly accessible HAM1000 dataset was employed to experiment;it includes processed images belonging to seven classes to evaluate the model's effectiveness. In conclusion, our proposed Ensemble model with Hair removal technique performs better than other cutting-edge deep-learning techniques and skilled dermatologists. Furthermore, this might be a trustworthy real-time teaching resource for medical students in a dermatology program. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： learning systems

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deep Hypercomplex Networks for Spatiotemporal Data processing: Parameter efficiency and superior performance

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IEEE SIGNAL processing MAGAZINE 2024年第3期41卷 101-112页

作者： Bojesomo, Alabi Liatsis, Panos Marzouqi, Hasan Al Khalifa Univ Sci & Technol Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates City Univ London Elect & Elect Engn Dept London England

Hypercomplex numbers, such as quaternions and octonions, have recently gained attention because of their advantageous properties over real numbers, e.g., in the development of parameter-efficient neural networks. For instance, the 16-component sedenion has the capacity to reduce the number of network parameters by a factor of 16. Moreover, hypercomplex neural networks offer advantages in the processing of spatiotemporal data as they are able to represent variable temporal data divisions through the hypercomplex components. Similarly, they support multimodal learning, with each component representing an individual modality. In this article, the key components of deep learning in the hypercomplex domain are introduced, encompassing concatenation, activation functions, convolution, and batch normalization. The use of the backpropagation algorithm for training hypercomplex networks is discussed in the context of hypercomplex algebra. These concepts are brought together in the design of a ResNet backbone using hypercomplex convolution, which is integrated within a U-Net configuration and applied in weather and traffic forecasting problems. The results demonstrate the superior performance of hypercomplex networks compared to their real-valued counterparts, given a fixed parameter budget, highlighting their potential in spatiotemporal data processing.

关键词： Training data Convolutional neural networks Algebra Quaternions image processing Data processing Spatiotemporal phenomena Hypercomplex Neural networks Backpropagation algorithms Batch normalization

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deep learning Method for real-time Fire Detection System for Urban Fire Monitoring and Control

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-14页

作者： Yang, Wenyang Wu, Yesen Chow, Steven Kwok Keung Xian Shiyou Univ Sch Comp Xian 710065 Peoples R China South Australian Hlth & Med Res Inst Clin & Res Imaging Ctr Adelaide SA 5000 Australia

During urban fire incidents, real-time videos and images are vital for emergency responders and decision-makers, facilitating efficient decision-making and resource allocation in smart city fire monitoring systems. However, real-time videos and images require simple and embeddable models in small computer systems with highly accurate fire detection ratios. YOLOv5s has a relatively small model size and fast processing time with limited accuracy. The aim of this study is to propose a method that employs a YOLOv5s network with a squeeze-and-excitation module for image filtering and classification to meet the urgent need for rapid and accurate real-time screening of irrelevant data. In this study, over 3000 internet images were used for crawling and annotating to construct a dataset. Furthermore, the YOLOv5, YOLOv5x and YOLOv5s models were developed to train and test the dataset. Comparative analysis revealed that the proposed YOLOv5s model achieved 98.2% accuracy, 92.5% recall, and 95.4% average accuracy, with a remarkable processing speed of 0.009 s per image and 0.19 s for a 35 frames-per-second video. This surpasses the performance of other models, demonstrating the efficacy of the proposed YOLOv5s for real-time screening and classification in smart city fire monitoring systems.

关键词： Fire detection YOLOv5 network Squeeze-and-excitation module

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Accelerating a Meta learning Model for Ultrasonic Non-Destructive Testing Applications Using Model Compression and FPGA Hardware

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2024年第1期96卷 15-29页

作者： Yuan, Yu Virupakshappa, Kushal Oruklu, Erdal IIT Dept Elect & Comp Engn 3301 South Dearborn St Chicago IL 60616 USA

Non-Destructive Evaluation/Testing (NDE/NDT) is comprised of advanced sensor technologies that can evaluate structures, materials and components for defects and analyze their properties. In recent years, researchers have been applying deep learning algorithms on NDT technologies to improve the capability of detecting and classifying complex sensor data. However, deep learning models often require large computational resources including specialized hardware accelerators, dedicated memory blocks and increased power consumption. It is very challenging to implement these deep learning algorithms in real-time testing scenarios in the field due to limited access to aforementioned computational resources. To address this issue, we introduce a model compression algorithm and the corresponding Field Programmable Gate Array (FPGA) accelerators for a novel deep learning model targeting ultrasonic NDT techniques. The ultrasonic deep learning algorithm which is based on Meta learning is capable of detecting and classifying different flaw types (e.g. cracks, holes) within the specimen. The results have shown that the model compression has significantly reduced the required operations with minimal accuracy loss, and the low-cost FPGA hardware platform is able to accelerate the inference using compressed model with high efficiency.

关键词： Ultrasound Non-Destructive Evaluation (NDE) Convolutional Neural Network (CNN) Meta learning Field Programmable Gate Array (FPGA) High-Level Synthesis (HLS) Software/hardware co-design

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A Regret Bound for the AdaMax Algorithm With image Segmentation Application

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2025年第9期48卷 10208-10214页

作者： Jirakipuwapat, Wachirapong King Mongkuts Univ Technol North Bangkok KMUTNB Fac Sci Energy & Environm Rayong Thailand

The AdaMax algorithm provides enhanced convergence properties for stochastic optimization problems. In this paper, we present a regret bound for the AdaMax algorithm, offering a tighter and more refined analysis compared to existing bounds. This theoretical advancement provides deeper insights into the optimization landscape of machine learning algorithms. Specifically, the You Only Look Once (YOLO) framework has become well-known as an extremely effective object segmentation tool, mostly because of its extraordinary accuracy in real-time processing, which makes it a preferred option for many computer vision applications. Finally, we used this algorithm for image segmentation.

关键词： AdaMax deep learning image segmentation regret bound YOLO

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QSAM-Net: Rain Streak Removal by Quaternion Neural Network With Self-Attention Module

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IEEE TRANSACTIONS ON MULtimeDIA 2024年 26卷 789-798页

作者： Frants, Vladimir Agaian, Sos Panetta, Karen CUNY Grad Ctr New York NY 10016 USA CUNY Coll Staten Isl New York NY 10314 USA Tufts Univ Elect & Comp Engn Dept Medford MA 02155 USA

real-world images captured in remote sensing, image or video retrieval, and outdoor surveillance are often degraded due to poor weather conditions, such as rain and mist. These conditions introduce artifacts that make visual analysis challenging and limit the performance of high-level computer vision methods. In time-critical applications, it is vital to develop algorithms that automatically remove rain without compromising the quality of the image contents. This article proposes a novel approach called QSAM-Net, a quaternion multi-stage multiscale neural network with a self-attention module. The algorithm requires significantly fewer parameters by a factor of 3.98 than the real-valued counterpart and state-of-the-art methods while improving the visual quality of the images. The extensive evaluation and benchmarking on synthetic and real-world rainy images demonstrate the effectiveness of QSAM-Net. This feature makes the network suitable for edge devices and applications requiring near real-time performance. Furthermore, the experiments show that the improved visual quality of images also leads to better object detection accuracy and training speed.

关键词： deep learning object detection quaternion image processing quaternion neural networks rain removal

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