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The Application of 6G and Augmented reality Technology in Education and Training

WIRELESS PERSONAL COMMUNICATIONS 2024年 1-19页

作者： Zhang, Yangloucai Bin Azizui, Muhammad Farid Azizul Yan, Cong Univ Teknol Malaysia Fac Built Environm & Surveying Skudai 81200 Malaysia Chengdu Neusoft Univ Sch Digital Art & Design Chengdu 61000 Peoples R China Univ Teknol Malaysia Coll Educ & Surveying Skudai 81200 Malaysia

The short evolution of 6G and augmented reality (AR) technology heralds a transformative era in education and training, promising to redefine traditional learning paradigms using immersive, interactive reports. This paper explores the integration of advanced capsule networks (CapsNet) architectures with semantic segmentation techniques to cope with the inherent annoying conditions of applying 6G and AR in academic contexts. CapsNets, well known for its efficiency in discerning spatial hierarchies and relationships, are paired with semantic segmentation to enhance object recognition and interplay within AR environments. It offers a deep, contextual understanding essential for growing dynamic, personalised learning studies. The synergy among this technology allows for the unique delineation and classification of each image pixel into corresponding object training, facilitating seamless interaction among virtual content and the real world. Need of the highly dependable, low-latency communication exchange skills of 6G, this combination guarantees powerful processing of complex scenes in real-time, crucial for preserving student engagement and facilitating adaptive learning pathways. The paper discusses optimisation techniques for real-time performance, including version pruning and effective routing mechanisms, to cope with the computational needs of CapsNets and semantic segmentation. Moreover, it highlights the role of 6G in assisting the bandwidth and latency necessities critical for deploying these state-of-the-art AI models in AR-based instructional programs. Through this integration, we aim to release new potentials for personalised, context-aware learning experiences, which can considerably enhance knowledge retention, engagement, and accessibility in education and training across various fields.

关键词： 6G Augmented reality Capsule networks Semantic segmentation Training Personalised learning

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ADL-CDF: A deep learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第8期48卷 9661-9673页

作者： Saheb, Shaik Khasim Narayanan, B. Rao, Thota Venkat Narayana Annamalai Univ Dept CSE Chidambaram Tamil Nadu India Sreenidhi Inst Sci & Technol Dept Comp Sci & Engn Hyderabad Telangana India

The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated deep learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.

关键词： Covid-19 Medical image analysis deep learning Multi-scale feature selection Convolutional neural networks

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Formation of an algorithm for determining the degree of human involvement based on the analysis of the movement of the pupils of the operator and data on the position of the head and body

Formation of an algorithm for determining the degree of huma...

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Conference on real-time image processing and deep learning

作者： Semenishchev, Evgenii Zhdanova, Marina Zelensky, Aleksandr Gracheva, Inessa Lyakhov, Daniil Mitugov, Nikita Voronin, Viacheslav Tula State Univ TulSU 92 Sq Lenina Tula 300012 Tula Region Russia Moscow State Tech Univ STANKIN 1-A Vadkovsky Moscow 127055 Russia

ISBN: (数字)9781510661714

ISBN: (纸本)9781510661707;9781510661714

The article proposes an algorithm for processing parallel analysis of visual data obtained by a machine vision system, recorded information in the human visible spectrum, and information received by a range camera. An algorithm for the formation of stable features as elements of the human body, head and pupils of a person and parallel tracking of their increment is proposed. To highlight trend lines in element displacement and eliminate the high frequency component based on a combined criterion. The image is preliminarily processed to reduce the effect of the noise component based on a multi-criteria objective function. As test data used to evaluate the effectiveness, a video stream with a resolution of 1024x768 (8-bit, color image, visible range), 3D data, and expert evaluation data are used.

关键词： image smoothing activity analysis preprocessing descriptors multicriteria method

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Coarse-to-fine vision-based welding spot anomaly detection in production lines of body-in-white

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JOURNAL OF MANUFACTURING SYSTEMS 2025年 81卷 144-154页

作者： Liu, Weijie Hu, Jie Qi, Jin Shanghai Jiao Tong Univ Sch Mech Engn Minhang 200240 Peoples R China

Computer vision-assisted methods for weld quality inspection enable rapid and automated surface defect detection through image data. However, the application of Computer Vision-Assisted Inspection (CVAI) in real-world production lines faces substantial, long-term challenges due to complex environments, imbalanced data samples, real-time processing demands, and safety requirements. Our paper proposes a novel two-stage Coarse-to-Fine Anomaly Detection (CTFAD) framework, which integrates the YOLOv8 network architecture for initial detection with an ensemble of neural networks for fine-grained classification. Additionally, we introduce a voting-based algorithm for improved decision-making accuracy. Experimental results on real-world datasets demonstrate that, compared to standard end-to-end methods, CTFAD enhances detection accuracy and operational efficiency. Our contributions include (1) proposing the CTFAD pipeline for weld anomaly detection, (2) establishing voting-based classification module to increase system robustness and generalization, and (3) developing an integrated weld detection system encompassing data acquisition, processing, analysis, and anomaly alerting. Our code is available at https://***/wj-liu0730/ctfad-jms.

关键词： Welding spot defect Industrial vision-based inspection deep learning Voting algorithm Integrated system

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RT-SNDETR: real-time supernova detection via end-to-end image transformers

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2024年第1期534卷 726-737页

作者： Pan, Zhi-Ren Qiu, Bo Li, Guang-Wei Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianfu Yongxing Lab Chengdu 610213 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Natl Astron Observ CAS Key Lab Opt Astron Beijing 100101 Peoples R China

In large-scale astronomical surveys, traditional supernova detection pipelines rely on complex and relatively inefficient image differencing techniques. This paper proposes an end-to-end deep-learning supernova detection network, the real-time SuperNova DEtection TRansformer (RT-SNDETR). This network partially replaces traditional pipelines by integrating image differencing, source detection, and real-bogus classification, achieving a speed 51.49 times that of the fastest image differencing method, SFFT. Additionally, it remains competitive with methods like YOLO v8, offering a well-balanced trade-off between speed and accuracy. Experimental results highlight RT-SNDETR's superior performance, with an average precision(AP) of 96.30 per cent on synthetic samples and 76.60 per cent on real supernova samples. It significantly outperforms other detection networks, including RT-DETR (+5.6 per cent AP on synthetic/+5.1 per cent AP on real samples) and Cascade R-CNN (+8.9 per cent AP on synthetic/ +28.6 per cent AP on real samples). The incorporation of CycleGAN-based data generation methods plays a significant role in enhancing RT-SNDETR's performance. These methods simulate realistic PSF variations, enabling the object detection network to learn more robust features and improving its generalization to real supernovae data. Additionally, by integrating unsupervised domain adaptation techniques, RT-SNDETR achieves an AP of 81.70 per cent on real SDSS supernova survey samples. This study demonstrates RT-SNDETR's potential to significantly enhance both the speed and accuracy of supernova detection, making it a highly effective solution for large-scale astronomical surveys.

关键词： methods: data analysis techniques: image processing techniques: photometric transients: supernovae

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From shallow to deep: Enhancing seismic resolution with weak supervision

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GEOPHYSICS 2025年第3期90卷 V223-V239页

作者： Liu, Dawei He, Yijie Wang, Xiaokai Sacchi, Mauricio D. Chen, Wenchao Du, Guanghong Zhang, Mengbo Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian Peoples R China Univ Alberta Dept Phys Edmonton AB Canada Xi An Jiao Tong Univ Shaanxi Smart Networks & Ubiquitous Access Res Ctr Xian 710049 Peoples R China Natl Engn Lab Low Permeabil Oil & Gas Explorat & D Xian Peoples R China PetroChina Changqing Oilfield Co Explorat & Dev Res Inst Xian Peoples R China

High-resolution (HR) seismic data are crucial for accurately identifying subsurface geologic formations and reservoir properties. However, as widely observed in prestack and poststack data, seismic wave attenuation often leads to a gradual decrease in resolution with depth. This degradation leads to resolution differences between data from shallow and deep layers, characterized by high-frequency energy loss in deeper target layers. The latter could complicate the extraction of crucial deep-layer structure information, leading to increased uncertainty in reservoir modeling and significant economic losses due to our inability to image deep targets with sufficient resolution. Traditional methods with simplified assumptions about this nonlinear attenuation hinder modeling geologic complexity and variability. deep-learning methods excel at capturing complex, nonlinear relationships but often rely heavily on scarce and costly paired labels for supervised learning. As a result, deep learning without requiring paired labels for seismic resolution enhancement has become a key research focus. This study explores a method to enhance 3D seismic data resolution using weakly supervised learning, leveraging the inherent similarity of sedimentary structures across depths. Specifically, our approach uses a relatively shallow, HR data window to transfer and extrapolate learned high-frequency information to a deeper window containing attenuated responses from deeper layers. Thereby, we achieve enhanced resolution in the target region. A tailored 3D convolutional neural network with a bidirectional cycle structure, custom-designed loss functions, and data preprocessing techniques specifically addresses the challenge of resolution differences caused by seismic wave attenuation. The effectiveness of our method is validated with synthetic and real 3D poststack migration data, demonstrating its robustness for regions near the target layer. Compared with conventional spectral whitening, our

关键词： high-resolution artificial intelligence machine learning resolution signal processing

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YOLOv5-S2C2: An Improved Method of Mask Detection Based on Lightweight

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IEEE ACCESS 2024年 12卷 53306-53319页

作者： Xie, Zongyuan Ma, Hongyan He, Wei Xu, Jiechuan Wen, Haoyu Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Natl Virtual Simulat Expt Ctr Smart City Educ Beijing 100044 Peoples R China

Masks play a crucial role in preventing respiratory diseases and have diverse applications in national public health and industrial safety. Efficient mask-wearing detection systems are essential for ensuring accuracy and real-time performance. To overcome the challenges of extensive model calculations, extensive parameter volume, and complex hardware deployment in the current mask-wearing detection system, a lightweight mask detection model with improved YOLOv5 is proposed. Firstly, this study proposes a new lightweight network-EMA-FasterNet, as the backbone network of YOLOv5, which reduces the computation while preserving the information of each channel. Secondly, using Depthwise Separable Convolutions (DepthSepConv) to replace some C3 modules in the Neck further compresses the model volume, parameters, and computation. Finally, to prevent the detection of missing objects due to the removal of overlapping candidate boxes, use Soft Non-Maximum Suppression (NMS) to replace NMS. Compared with YOLOv5s, the proposed YOLOv5-S2C2 has high mAP in both Dataset I and Dataset II, while the parameters and computation of the model are reduced by about 56% and 57.0%, respectively. The volume is about 44% of the original model and the inference speed on GPU is improved by about 30%. These improvements show that the proposed model achieves a lightweight design and excellent real-time performance. It is also well suited for efficient use on hardware with limited computational resources.

关键词： YOLO Computational modeling Feature extraction Neck Solid modeling real-time systems Classification algorithms image processing deep learning Detection algorithms deep learning detection algorithms YOLOv5 FasterNet lightweight network

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Color image denoising: a hybrid approach for mixed Gaussian and impulsive noise

Color image denoising: a hybrid approach for mixed Gaussian ...

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Conference on real-time image processing and deep learning

作者： Smolka, Bogdan Kusnik, Damian Smolka, Milena Kawulok, Michal Cyganek, Boguslaw Silesian Tech Univ Gliwice Poland AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9781510673878;9781510673861

This paper tackles the problem of mixed Gaussian and impulsive noise suppression in color images. The proposed method comprises two essential steps. Firstly, we detect impulsive noise through an approach based on the concept of digital path exploring the local pixel neighborhood. Each pixel is assigned a cost of a path connecting the boundary of a local processing window with its center. When the central pixel exhibits a high value of the path with lowest cost, it is identified as an impulse. To achieve this, we use a thresholding procedure for detecting corrupted pixels. Analyzing the distribution of minimum path costs, we employ the k-means technique to classify pixels into three distinct categories: those nearly undistorted, those corrupted by Gaussian noise, and those affected by impulsive noise. Subsequently, we employ the Laplace interpolation technique to restore the impulsive pixels - a fast and effective method yielding satisfactory denoising results. In the second step, we address the residual Gaussian noise using the Non-Local Means method, which selectively considers pixels from the local window that have not been flagged as impulsive. The experimental results confirm that our proposed hybrid method consistently yields superior outcomes compared to state-of-the-art denoising techniques. Moreover, its computational complexity remains low, rendering it suitable for real-time applications.

关键词： image enhancement color imaging mixed noise reduction impulsive noise

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real-time Change Detection for Automated Test Socket Inspection Using Advanced Computer Vision and Machine learning

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2023年第3期36卷 332-339页

作者： Edwards, Chris Vaske, Alex McDaniel, Nathan Pradhan, Dipali Panda, Debashis Intel Corp Technol Dev Analyt & Automation Package & Test Aut Chandler AZ 85226 USA Intel Corp Sort Test Technol Dev Class Test Proc Chandler AZ 85226 USA Intel Corp Burn Proc & Collateral Management Chandler AZ 85226 USA Intel Corp Network Edge Xeon & Networking Engn Chandler AZ 85226 USA

We present our automated real-time socket inspection system capable of detecting an assortment of defects including metallic and liquid staining, loose capacitors and pins, and other debris and foreign material (FM). Our test tools pick and place manufactured units into sockets for electrical testing. Any debris accumulated inside the test sockets will likely damage subsequent units until the defective socket is replaced. To quickly capture in-situ defects and mitigate further damage, we equipped each pick-and-place arm with a new vision system designed to fit within the existing tool. The tight footprint constraints required a highly compact imaging system which resulted in a variety of image artifacts, creating several unique challenges. Our inspection algorithm utilizes a variety of advanced computer vision and machine learning techniques to normalize images, extract and match features, register the images, suppress unwanted artifacts, and detect defects. The detected changes are then sent to a deep learning classifier to further filter between true defects and natural socket deterioration. The flagged socket images can be manually dispositioned by the user and the socket can be sent for repair or cleaning as needed.

关键词： Sockets Pins Inspection Feature extraction Measurement Standards Shape Defect detection inspection machine learning deep learning vision image processing change outlier anomaly

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deep learning-based Metal Extraction with No Reference in Dental CBCT for Metal Artifact Reduction

Deep Learning-based Metal Extraction with No Reference in De...

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Conference on Medical Imaging - image processing

作者： Lee, Hyunwoo Kim, Sunjung Cho, Min Kook Osstem Implant Co Ltd Imaging R&D Ctr Seoul 07789 South Korea

ISBN: (纸本)9781510671577;9781510671560

Metal artifact reduction (MAR) is crucial for improving the quality of dental cone-beam computed tomography (CBCT) images. However, inaccurate extraction of metal in MAR can lead to incomplete suppression of metal artifacts, obscuring adjacent bone structures in resultant images. The pre-existing metal artifacts such as streaks, shadowing, and cupping complicate the extraction of metallic segments. In particular, dental CBCT applications are vulnerable to metal artifacts due to the lack of anti-scatterers and the frequent appearance of metallic restorations. Conventional image processing methods for metal extraction have some limitations, for example, relying on predefined parameters and requiring manual interventions. To overcome these challenges, we introduce a deep learning-based metal extraction method in an unsupervised manner, eliminating the labor-intensive label annotation process. This method combines several methods, including unpaired image-to-image translation, weakly supervised learning, and geometric transformations, followed by supervised learning for real-time metal extraction. As a result, the proposed method achieves high-quality metal extraction with an accuracy of 90%, as evaluated with manual annotations, and shows a significant improvement in MAR performance for clinical CBCT images compared to our conventional method.

关键词： cone-beam computed tomography metal artifact reduction metal extraction deep learning

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