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The Effects of Visual Behavior and Ego-Movement on Foveated Rendering Performance in Virtual Reality. 虚拟现实中视觉行为和自我运动对注视点渲染性能的影响。
SN computer science Pub Date : 2025-01-01 Epub Date: 2025-04-15 DOI: 10.1007/s42979-025-03885-7
David Petrescu, Paul A Warren, Zahra Montazeri, Gabriel Strain, Steve Pettifer
{"title":"The Effects of Visual Behavior and Ego-Movement on Foveated Rendering Performance in Virtual Reality.","authors":"David Petrescu, Paul A Warren, Zahra Montazeri, Gabriel Strain, Steve Pettifer","doi":"10.1007/s42979-025-03885-7","DOIUrl":"https://doi.org/10.1007/s42979-025-03885-7","url":null,"abstract":"<p><p>Despite the recent developments in VR, maintaining photorealism is difficult due to the increased bandwidth capabilities and computational resources required. To make VR more affordable, techniques such as Foveated Rendering (FR) offer promising ways to optimise rendering without compromising the user experience. Near-eye displays with 6DOF tracking enable users to freely move through the environment. This was previously impossible with traditional displays. This work aims to disentangle the effect of type of ego-movement (Active versus Implied) and task type (Simple Fixations versus a task involving Fixations, Discrimination, and Counting) on a dynamic FR method developed using Variable Rate Shading (VRS) (a quarter of the native shading rate is used in the visual periphery). We also explore if the aforementioned effects are consistent under different visual behaviours (visual search versus tracking). Results show that participants actively moving and performing complex tasks (during visual search) are less sensitive to degradation than in other conditions, with only 31.7% of the FOV required to be rendered using full sampling. Additionally, we provide evidence for how instances of visual pursuit might influence these results; in this case, only 29.3% of the FOV rendered using full sampling is tolerated by participants.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 4","pages":"386"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12000250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advancing Point Cloud Perception: A Focus on People Detection. 推进点云感知：关注人的检测。
SN computer science Pub Date : 2025-01-01 Epub Date: 2025-07-28 DOI: 10.1007/s42979-025-04221-9
Assia Belbachir, Antonio M Ortiz, Atle Aalerud, Ahmed Nabil Belbachir
{"title":"Advancing Point Cloud Perception: A Focus on People Detection.","authors":"Assia Belbachir, Antonio M Ortiz, Atle Aalerud, Ahmed Nabil Belbachir","doi":"10.1007/s42979-025-04221-9","DOIUrl":"10.1007/s42979-025-04221-9","url":null,"abstract":"<p><p>Point-cloud data have become pivotal for three-dimensional scene analysis, yet robust real-time detection of humans remains challenging due to data sparsity, irregular sampling, and occlusions. In this study, we present a feature-engineered pipeline that uses a Random Forest Classifier (RFC) for efficient people detection in high-resolution LiDAR point clouds. Our contributions include: (1) detailed parameterization of a ground-removal algorithm using region growing; a compact feature set of 15 geometric and intensity-based descriptors; (3) comprehensive evaluation metrics on two datasets; and (4) comparative analysis against MLP and PointNet baselines. Experiments demonstrate that our RFC achieves good results. These results validate the practical applicability of our approach for real-time, on-device human detection in point-cloud environments.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 6","pages":"698"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fault Tree Reliability Analysis via Squarefree Polynomials: Mathematical and Experimental Analysis. 基于无平方多项式的故障树可靠性分析：数学与实验分析。
SN computer science Pub Date : 2025-01-01 Epub Date: 2025-11-14 DOI: 10.1007/s42979-025-04450-y
Milan Lopuhaä-Zwakenberg
{"title":"Fault Tree Reliability Analysis via Squarefree Polynomials: Mathematical and Experimental Analysis.","authors":"Milan Lopuhaä-Zwakenberg","doi":"10.1007/s42979-025-04450-y","DOIUrl":"10.1007/s42979-025-04450-y","url":null,"abstract":"<p><p>Quantitative analysis of risk models is essential to ensure the resilience of complex systems. Fault trees (FTs) form a ubiquitous prominent risk model, and unreliability is its key safety metric. As complex systems have larger and larger models, the complexity of algorithms computing unreliability is a pressing concern. Unfortunately, state-of-the-art algorithms, based on binary decision diagrams, do not give time complexity guarantees beyond a worst-case exponential bound. To address this issue, this paper introduces a new method to compute FT unreliability, extending the fast bottom-up algorithm for tree-shaped FTs to general FTs by framing its arithmetic in algebras of squarefree polynomials. We prove the validity of this algorithm, and that its time complexity is linear when the number of multiparent nodes is limited. Experiments establish the competitiveness of our new method.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 8","pages":"965"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks. 供水网络中氯浓度状态的物理信息机器学习的基准。
SN computer science Pub Date : 2025-01-01 Epub Date: 2025-06-04 DOI: 10.1007/s42979-025-04008-y
Luca Hermes, André Artelt, Stelios G Vrachimis, Marios M Polycarpou, Barbara Hammer
{"title":"A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks.","authors":"Luca Hermes, André Artelt, Stelios G Vrachimis, Marios M Polycarpou, Barbara Hammer","doi":"10.1007/s42979-025-04008-y","DOIUrl":"10.1007/s42979-025-04008-y","url":null,"abstract":"<p><p>Ensuring high-quality drinking water is a critical responsibility of water utilities, with chlorine being the main disinfectant typically used. Accurate estimation of chlorine concentrations in the dynamic environment of water distribution networks (WDNs) is essential to ensure safe water supply. This work introduces a comprehensive and carefully created benchmark for training and evaluation of chlorine concentration estimation methodologies in WDNs. The benchmark includes a diverse dataset of 18,000 scenarios of the widely studied 'Hanoi', 'Net1', and the more recent and complex 'CY-DBP' water networks, featuring various chlorine injection patterns to capture diverse physical dynamics. To provide baseline evaluations, we propose and evaluate two neural surrogate models for chlorine state estimation: a physics-informed Graph Neural Network (GNN) and a physics-guided Recurrent Neural Network (RNN).</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 5","pages":"522"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm 利用 DSHS 算法实现模糊和快速匹配实体的复兴
SN computer science Pub Date : 2024-07-27 DOI: 10.1007/s42979-024-03093-9
Venkatram Kari, Geetha Mary Amalanathan
{"title":"Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm","authors":"Venkatram Kari, Geetha Mary Amalanathan","doi":"10.1007/s42979-024-03093-9","DOIUrl":"https://doi.org/10.1007/s42979-024-03093-9","url":null,"abstract":"","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"80 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
FLORUS: An Efficient Big Data Framework for Telecom Customer Behavior Analysis FLORUS：用于电信客户行为分析的高效大数据框架
SN computer science Pub Date : 2024-07-27 DOI: 10.1007/s42979-024-03057-z
Hong-Phuc Vo, Khoa-Gia-Cat Nguyen, Thanh-Van Le
{"title":"FLORUS: An Efficient Big Data Framework for Telecom Customer Behavior Analysis","authors":"Hong-Phuc Vo, Khoa-Gia-Cat Nguyen, Thanh-Van Le","doi":"10.1007/s42979-024-03057-z","DOIUrl":"https://doi.org/10.1007/s42979-024-03057-z","url":null,"abstract":"","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards Robust Colour Texture Analysis with Limited Training Data 利用有限的训练数据实现鲁棒色彩纹理分析
SN computer science Pub Date : 2024-07-27 DOI: 10.1007/s42979-024-03067-x
Mariya Shumska, Michael H. F. Wilkinson, Kerstin Bunte
{"title":"Towards Robust Colour Texture Analysis with Limited Training Data","authors":"Mariya Shumska, Michael H. F. Wilkinson, Kerstin Bunte","doi":"10.1007/s42979-024-03067-x","DOIUrl":"https://doi.org/10.1007/s42979-024-03067-x","url":null,"abstract":"","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Recent Advances in 2D Image Upscaling: A Comprehensive Review 二维图像升维的最新进展：全面回顾
SN computer science Pub Date : 2024-07-27 DOI: 10.1007/s42979-024-03070-2
Jagyanseni Panda, S. Meher
{"title":"Recent Advances in 2D Image Upscaling: A Comprehensive Review","authors":"Jagyanseni Panda, S. Meher","doi":"10.1007/s42979-024-03070-2","DOIUrl":"https://doi.org/10.1007/s42979-024-03070-2","url":null,"abstract":"","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"67 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SCCNet: Skin Cancer Detection and Multi-Class Classification Using Deep CNN Model with Estimated Disease Probabilities SCCNet：使用带有估计疾病概率的深度 CNN 模型进行皮肤癌检测和多级分类
SN computer science Pub Date : 2024-07-27 DOI: 10.1007/s42979-024-03072-0
V. A. Kumar, Chennoju Chandana, Goli Supraja, Dharmaboyina Haripriya, Ellandula Ravalika
{"title":"SCCNet: Skin Cancer Detection and Multi-Class Classification Using Deep CNN Model with Estimated Disease Probabilities","authors":"V. A. Kumar, Chennoju Chandana, Goli Supraja, Dharmaboyina Haripriya, Ellandula Ravalika","doi":"10.1007/s42979-024-03072-0","DOIUrl":"https://doi.org/10.1007/s42979-024-03072-0","url":null,"abstract":"","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"69 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Study of Text Extraction Algorithms for Natural Scene Images 自然场景图像文本提取算法研究
SN computer science Pub Date : 2024-07-27 DOI: 10.1007/s42979-024-03068-w
Shilpi Goyal, Deepak Motwani
{"title":"A Study of Text Extraction Algorithms for Natural Scene Images","authors":"Shilpi Goyal, Deepak Motwani","doi":"10.1007/s42979-024-03068-w","DOIUrl":"https://doi.org/10.1007/s42979-024-03068-w","url":null,"abstract":"","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"3 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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