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AR-Light: Enabling Fast and Lightweight Multi-User Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology HoustonTX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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Analyzing the Role of Time Series Analysis in Early Detection of Pancreatic Cancer 5th

Analyzing the Role of Time Series Analysis in Early Detectio...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kavitha, R. Singh, Yudhveer Madhusudhan, M.V. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Pancreatic cancer is a vital disorder that is still the number one motive of lack of lifestyle, with a five-12 months survival fee of only 9. Early detection of pancreatic cancers is fundamental to beautifying outcomes. However, it could be tough to stumble on based totally on scientific and imaging outcomes myself. Time collection evaluation offers a progressive technique to sources in the early detection of pancreatic cancer. This paper proposes to research time series analysis for analysis and early detection of pancreatic cancers. Time series evaluation is a powerful series of techniques for analyzing statistics and temporal records, biological alerts, or healthcare facts. It uses a sequence of observations taken through the years to assess patterns and traits in facts, which could tell osteoporotic fracture hazards or provide early sickness signs and symptoms. Previous research has assessed time series evaluation's efficacy in detecting cardiovascular ailments and influenza. However, there may be a shortage of research examining the application of time series evaluation for pancreatic cancer detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition

Synergistic Spotting and Recognition of Micro-Expression via...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zou, Bochao Guo, Zizheng Qin, Wenfeng Li, Xin Wang, Kangsheng Ma, Huimin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Micro-expressions are involuntary facial movements that cannot be consciously controlled, conveying subtle cues with substantial real-world applications. The analysis of micro-expressions generally involves two main tasks: spotting micro-expression intervals in long videos and recognizing the emotions associated with these intervals. Previous deep-learning methods have primarily relied on classification networks utilizing sliding windows. However, fixed window sizes and window-level hard classification introduce numerous constraints. Additionally, these methods have not fully exploited the potential of complementary pathways for spotting and recognition. In this paper, we present a novel temporal state transition architecture grounded in the state space model, which replaces conventional window-level classification with video-level regression. Furthermore, by leveraging the inherent connections between spotting and recognition tasks, we propose a synergistic strategy that enhances overall analysis performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The codes are available at https://***/zizheng-guo/ME-TST. © 2025 IEEE.

关键词： Long videos Micro-expression analysis State space model Synergistic spotting and recognition

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BOF-Net: A Retinal Branch Vein Occlusion Segmentation Diagnostic Network for Fundus Fluorescein Angiography Images 5th

BOF-Net: A Retinal Branch Vein Occlusion Segmentation Diagno...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Wu, Jiayi Dou, Quansheng Tang, Huanling Wang, Lizi School of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science and Technology Shandong Technology and Business University Yantai China

ISBN: (纸本)9789819638628

Branch Retinal Vein Occlusion (BRVO) is a common retinal vascular disease in clinical practice, typically associated with retinal arteriosclerosis. Currently, doctors can only perform subtype diagnosis of patients by visually assessing the size ratio between non-perfused areas and the optic disc in fundus fluorescein angiography (FFA). In recent years, deep learning technology has been widely applied in medical imaging. However, due to the scarcity of retinal vein occlusion imaging data and the lack of publicly available datasets, applying deep learning network models to segment and diagnose the occlusion areas remains challenging. Our goal is to propose a segmentation network model, BOF-Net, based on BRVO imaging data, to segment the non-perfused areas and the optic disc regions in the images and calculate their respective areas, thereby assisting doctors in more accurately performing subtype diagnosis. BOF-Net achieved a Dice coefficient of 85.26% and an accuracy of 93.99% on the BRVO dataset. Compared to other segmentation network models, BOF-Net shows greater potential and effectiveness in the auxiliary diagnosis of BRVO. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models 31

Rethinking Vocabulary Augmentation: Addressing the Challenge...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lin, Nankai Zeng, Peijian Zheng, Weixiong Jiang, Shengyi Zhou, Dong Yang, Aimin School of Information Science and Technology Guangdong University of Foreign Studies China School of Computer Science and Technology Guangdong University of Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (纸本)9798891761964

The performance of multilingual language models (MLLMs) is notably inferior for low-resource languages (LRL) compared to high-resource ones, primarily due to the limited available corpus during the pre-training phase. This inadequacy stems from the under-representation of low-resource language words in the subword vocabularies of MLLMs, leading to their misidentification as unknown or incorrectly concatenated subwords. Previous approaches are based on frequency sorting to select words for augmenting vocabularies. However, these methods overlook the fundamental disparities between model representation distributions and frequency distributions. To address this gap, we introduce a novel Entropy-Consistency Word Selection (ECWS) method, which integrates semantic and frequency metrics for vocabulary augmentation. Our results indicate an improvement in performance, supporting our approach as a viable means to enrich vocabularies inadequately represented in current MLLMs. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Real-Time Classification of Defects During Automated Valve Testing and Inspection 5th

Real-Time Classification of Defects During Automated Valve T...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Shweta Agarwal, Trapty Haripriya Hemlata School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper offers a singular method for actual-time type of defects at some point of computerized valve testing and inspection. Mainly, an image processing gadget is developed to discover and classify various defects which includes cracks, dents, foreign items, and different floor irregularities. The machine includes a high-decision digital camera, LED lighting fixtures, processing unit, and defect reputation algorithm. The image processing system captures the picture and methods it to pick out the illness regions. The gadget then extracts the features from every of the detected regions and classifies them the usage of a guide Vector device. The model is demonstrated with manually received pics, and the outcomes show true overall performance in terms of accuracy and speed. Similarly, the system can alert the consumer whilst a potential disorder is identified and the defects can then be in addition inspected and repaired. This approach has the ability to lessen inspection time, enhance manufacturing efficiency, and reduce charges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting fixtures

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Early Detection of Amyotrophic Lateral Sclerosis with Time Collection Evaluation 5th

Early Detection of Amyotrophic Lateral Sclerosis with Time C...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mehar, Palavancha Tirumala Krishna Agarwal, Trapty Suneetha, K. Ahmad, Naseem School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper discusses the usage of time series analysis for the early detection of Amyotrophic Lateral Sclerosis (ALS). A time series approach was used to expand a set of rules that may detect the early stages of ALS via analyzing symptomatic changes in signs and symptoms consisting of muscle energy, taking walks potential, speech, swallowing, and different bodily motion. This technique become examined on a retrospective dataset, and proven if you want to come across changes in the circumstance of ALS in a well-timed way. The outcomes suggest that this technique has ability as a tool for early detection in ALS sufferers. The proposed algorithm ought to potentially be used to improve the present day standard of care and help lead to in advance analysis and higher scientific effects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion Potential Swallowing

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Multi-Domain Graph-Enhanced Joint Intent and Slot Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第2期52卷 374-382页

作者： Shi, Wu Yuan, Weihao Wang, Yang Dong, Yindong Xu, Gaojian School of Information and Artificial Intelligence Anhui Agricultural University Hefei230036 China School of Computer Science and Technology Xidian University Xi’an710126 China School of Engineering Anhui Agricultural University Hefei230036 China

Comprehension of spoken language is essential in dialog systems, as it supports two fundamental tasks: intent classification and slot filling. At present, federated modeling methodologies prevail in the domain of spoken language comprehension. Nevertheless, current models encounter constraints in accurately representing the interrelations among activities and utilizing cross-domain semantic data. This study introduces the Multi-Domain Graph-Enhanced Joint Intent and Slot Learning (MDG-JISL) paradigm to tackle these difficulties. MDG-JISL amalgamates a pre-trained BERT model with a self-trained FastText model to create superior sentence-level and word-level representations. A syntactic dependency tree is employed to create graph structures among words, which are subsequently refined by the implementation of a Graph Convolutional Network (GCN) to more effectively capture relationship properties. A Conditional Random Field (CRF) model is utilized for decoding, enhancing the model’s efficacy in natural language processing tasks. Experimental findings indicate that MDG-JISL attains remarkable performance on cross-domain datasets, with Slot(F1), Intent (Acc), and Overall (Acc) metrics of 98.7%, 98.4%, and 92.4%, respectively. The results validate the model’s efficacy in intent classification and slot filling. © (2025), (International Association of Engineers). All rights reserved.

关键词： Syntactics

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