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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Promoting Physical Exercise Adherence and Safety in Virtual Reality Using IoT Architecture

Promoting Physical Exercise Adherence and Safety in Virtual ...

引用

ieee Annual Computing and Communication Workshop and conference (CCWC)

作者： Te-Yi Tsai Aref Smiley Joseph Finkelstein dept. Biomedical Informatics University of Utah School of Medicine Salt Lake City Utah USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Immersive virtual reality (VR) can enhance the cycling exercise experience and promote adherence to daily physical activity. The goal of this project was to facilitate exercise adherence and safety by implementing real-time visualization of cycling trajectory and oxygen saturation within a three-dimensional space generated by an interactive VR app. Our implementation included an Oculus headset and a Metaverse application for a racetrack game, leveraging IoT infrastructure to collect and process real-time data from a portable exercise bike and wireless pulse oximeter. The IoT interface facilitated transmission, storage, and visualization of exercise bike speed within the VR environment. The system wirelessly interfaced with a portable bike and pulse oximeter, enabling real-time monitoring of exercise intensity, heart rate, and oxygen saturation. These parameters were continuously displayed on the VR dashboard during the exercise. A dedicated web portal supported individualized exercise program prescriptions and adjustments based on patient performance. Real-time alerts based on personalized thresholds ensured adherence to exercise prescriptions and patient safety. Our prototype demonstrated the potential of Metaverse applications using IoT infrastructure for cost-effective home-based telerehabilitation. Future enhancements include wireless physiological sensors, vital sign monitors, and remote communication with clinical rehabilitation teams. A randomized clinical trial will evaluate system efficacy.

关键词： Wireless communication Wireless sensor networks Pulse oximeter Metaverse Data visualization Virtual reality Real-time systems Safety Trajectory Biomedical monitoring

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A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for Secure Cloud Data Management

A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for S...

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Thiruneelakandan, S. Eliyas, Sherin Sathish Kumar, M. Hindustan Institute of Technology and Science Department of Computer Application TamilNadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications TamilNadu Chennai India

ISBN: (纸本)9798331521394

Data security, integrity verification, and efficient retrieval issues are much more pronounced with large datasets in cloud storage systems. The angle of this paper is the proposal of hybrid architecture of AES, EMHT, B-tree indexing, and POR techniques to complete the optimization of cloud operations. The way the system was evaluated against the traditional approaches was based on several key metrics such as encryption time, integrity verification time, update time, and memory usage for datasets up to the 15GB limit. The experimental results show that the proposed system has 6.50% less verification time and 38.44% better update time when compared to existing methods without compromising the effectiveness of encryption. These indicate that the proposed approach is cost-effective, secure, and scalable in a large-scale cloud storage environment. The contribution of this paper is a practical solution for future enhanced data management and security in modern cloud systems. © 2025 ieee.

关键词： information management

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Location information Measurements Using Multiple Devices Toward Digital Transformation of Open Sky Museum

Location Information Measurements Using Multiple Devices Tow...

引用

ieee International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Yasuyuki Saito Shigeki Uemura Jun Okuzumi Norihiko Suzuki Nobuyuki Umezu Dept. of Inf. Eng. Nat. Inst. of Tech. Kisarazu College Kisarazu Japan Dept. of Civil Eng. Nat. Inst. of Tech. Kisarazu College Kisarazu Japan Shibayama Ancient-tomb and Clay-image Museum Shibayama-machi Board of Education Shibayama-machi Japan N&S Support Office Kurashiki Japan Dept. of Comp. and Inf. Sci. Ibaraki University Hitachi Japan

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

The purpose of this study is to compare the results of measuring location information using multiple devices. In the Open Sky Museum for digital transformation, accurate location information is necessary for presenting information on a terminal. We compare the location information obtained by receiving the quasi-zenith satellite system (QZSS) L6 correction signal with the Google API on Android tablets that we have used in our previous study. We demonstrate that the location information can be obtained stably using the QZSS L6 signal, and examine the measurement error using multiple Android devices of the same model.

关键词： Solid modeling visualization Measurement errors Fluctuations Accuracy Satellites Digital transformation Virtual reality Museums Smart phones

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Machine Learning for Mental Health Prediction From Social Media Activity

Machine Learning for Mental Health Prediction From Social Me...

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Mohanraj, S. Krishna, R.Ramesh Adams, M. Shan Nallusamy, C. K.S.Rangasamy College of Technology Tiruchengode Department of Information Technology India

ISBN: (纸本)9798331521394

The study in question investigates applying machine learning techniques to text data from Naive Bayes classifiers in predicting depression symptomology. The dataset encompasses the text and its relevant labels, while other variables include LIWC variables such as sentence length, post ID, subreddit, and lexical attributes. After data preprocessing-cleaning and feature selection-the topic modeling process involves Latent Dirichlet Allocation (LDA). The main purpose is to detect latent themes in the textual data, as well as to assess the influence of individual words among different publications. Then the model is trained on a labeled dataset and evaluated against an independent one. These findings show good accuracy and F1 score to validate the performance to successfully detect depression symptoms on textual data. This work precisely shows how machine learning algorithms can be used in mental health research, especially in early detection and treatment for those who may manifest depressive symptoms with their online behavior. © 2025 ieee.

关键词： Learning algorithms

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Co-Sketch: Drawing Together and Taking Notes Forever

Co-Sketch: Drawing Together and Taking Notes Forever

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Bhorge, Siddharth Talewar, Mayur Goud, Sidhesh Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798331521394

In today's digital era, effective collaboration tools are essential for fostering creativity and communication. Co-Sketch is a cutting-edge web application that addresses this need by providing a platform for real-time collaborative drawing and note-taking. Authors developed an innovative web application designed to facilitate real-time collaborative drawing and note-taking along with a video meet feature that enhances. Built with a robust technology stack that includes React, HTML5 Canvas API, WebSocket's, and ***, WebRTC Co-Sketch provides a seamless and interactive platform for users to sketch, annotate, and share ideas in a dynamic online environment. Co-Sketch is designed with user collaboration in mind, offering features such as live chat, and session management to enhance the interactive experience. Whether for educational purposes like online lectures, brainstorming sessions or official meetings, Co-Sketch stands out as a reliable and engaging platform for collaborative digital creation and note-taking as well as teaching. Security and privacy is prioritized, using robust mechanisms to protect user data and manage access to collaborative sessions. © 2025 ieee.

关键词： information management

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Using Signed Distance Fields to Achieve Temporal Compression of Mesh-Based Volumetric Video

Using Signed Distance Fields to Achieve Temporal Compression...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Jakob E. Anderson Yaojie Li Andrew Hogue GAMER Lab @ Ontario Tech University Oshawa Canada

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Volumetric Video is an exciting medium that enables visualization of 3D time-varying data, often as sequence of Point Clouds or Textured Meshes. Despite over a decade of focus, there still exist several problems that make it challenging to employ. This paper focuses on one aspect of deploying Volumetric Video — data compression. Reducing the data storage size is necessary for streaming applications due to the large size of sequences consisting of even a few seconds of footage. This work explores the use of temporal compression of time-varying textured meshes using Signed Distance Fields and compare with the state-of-the art techniques. The goal is to produce a novel compression method to maximize a size-to-error compression ratio while maintaining a decompression speed of 30 frames per second. The results reflect that as file sizes decrease, the proposed method’s error increases at a slower rate than the state-of-the-art.

关键词： Point cloud compression Surface reconstruction Three-dimensional displays conferences Memory Data visualization Virtual reality Streaming media Video compression Surface texture

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Presenting BIOXRE: project on BIOphilic design of Extended Reality working Environments

Presenting BIOXRE: project on BIOphilic design of Extended R...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Michele Gattullo Enricoandrea Laviola Luana Marangelli Sara Romano Polytechnic University of Bari

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Nowadays, the technological maturity achieved by Extended Reality (XR) technologies allows to develop applications that foster positive emotions and promote personal growth. The scope of this work is to present the first results about a research project (BIOXRE - BIOphilic design of Extended Reality working Environments) focused on the application and evaluation of the biophilia hypothesis of Positive Computing in virtual workplaces. In the literature, there is effectively evidence that biophilic design improves human wellbeing. However, only a few studies evaluated its effectiveness in improving operators' wellbeing in the real workplace. For the first time, BIOXRE aims to exploit the biophilic design to XR applications supporting procedural tasks. The goal is to improve the wellbeing of human operators without affecting their performance in accomplishing manual tasks supported by XR visualization. The outcome of the project will be to extract guidelines for the spatial augmentation of real workplaces, fostered by the biophilic design, that will derive from a well-structured user evaluation in the target industrial scenario.

关键词： Measurement visualization Three-dimensional displays Graphical models Extended reality Employment Fifth Industrial Revolution Guidelines Graphical user interfaces Distribution functions

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Perception System for Autonomous Vehicles: Object Detection, Motion Tracking, and Future Position Prediction

Perception System for Autonomous Vehicles: Object Detection,...

引用

ieee International conference on Industrial Technology (ICIT)

作者： Gustavo Andreas Günther Pedro Henrique Almeida da Cruz Joáo Pedro Barcelos de Assis Carlos N. Silla Marcelo Eduardo Pellenz Marco Antonio Simões Teixeira PPGia-PUCPR Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Paraná Brazil Halmstad University Halmstad Sweden

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents a multi-sensor fusion approach for detecting, clustering, and predicting the motion of dynamic objects by integrating camera and LiDAR data. The framework combines YOLO-based visual segmentation with LiDAR point clouds to enable accurate spatial localization and motion tracking. Ego-motion compensation is achieved using IMU and velocity data, while trajectory prediction is enhanced through centroidbased orientation estimation. Detected objects are segmented into coherent clusters using DBSCAN, facilitating visualization and motion forecasting. The framework was validated on a simulated KITTI dataset, demonstrating its effectiveness in dynamic object detection and trajectory prediction.

关键词： Point cloud compression visualization Laser radar Accuracy Motion segmentation Dynamics Object detection Cameras Trajectory Vehicle dynamics

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AI-Assisted Data Modeling and High-Resolution Image Synthesis: An Interactive Framework with Latent Diffusion Model visualization

AI-Assisted Data Modeling and High-Resolution Image Synthesi...

引用

2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Borah, Asha Rani Archana Rani, K. Pushpa Ziara, Sajjad Lakhanpal, Sorabh Vidyadhari, Chalasanvi New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Punjab Phagwara India Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper, we present the design and implementation of an AI assisted interactive framework for data modeling and high resolution image synthesis that leverages both state of the art latent diffusion models and visualisation of the real-time algorithm. The system proposed enables user to be able to better understand complex data modeling processes and creates high resolution images in the latent space using diffusion. The framework is both interactive, allowing modifications in parameters and real time visualization of change, making it a useful tool both for educational and for advanced AI research. In this paper, we also discuss the underpinning AI-assisted data modeling algorithms, and why they enable efficient data manipulation as well as high quality image synthesis then provide simulation results and practical applications to illustrate the capabilities of the framework of visualizing the latent diffusion process and generating high resolution images. © 2025 ieee.

关键词： Flow visualization

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VisQ2SQL: Towards SQL-Driven Data visualization via LLMs-Grounded Preference Learning

VisQ2SQL: Towards SQL-Driven Data Visualization via LLMs-Gro...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shengze Shi Tao Ren Jun Hu State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-visualization (Text2Vis) aims to democratize data insights for non-expert users by transforming natural language query (NLQ) into visualization specification (VS). In view of the high dependence of rule-based methods on predefined VS templates and poor NLQ understanding ability of small data-driven methods, recent works resort to leveraging pre-trained LLMs to perform NLQ-understanding and VS-generating in Text2Vis tasks via prompt-guided in-context learning. However, existing LLM-based methods still fall short of satisfactory end-to-end Text2Vis performances primarily owing to the limited ability of pre-trained LLMs in directly retrieving and operating NLQ-intended tabular data. Inspired by the SQL generating ability born with latest LLMs, this paper proposes the idea of harnessing LLMs for SQL-driven visualization data retrieval and operation. Nonetheless, there remains a nonneglectable gap between data visualization queries in Text2Vis tasks and SQL retrieving queries in LLM corpus. To fill the gap, this paper proposes a visualization Query to SQL (VisQ2SQL) framework to obtain NLQ-intended data, primarily by fine-tuning LLMs through preference learning data-retrieval SQLs induced from VS and those generated by LLMs. We conduct extensive experiments to demonstrate the superiority of VisQ2SQL over SOTA methods, and various ablation studies to verify the efficacy of VisQ2SQL.

关键词： Structured Query Language Natural languages Data visualization Transforms Contrastive learning Speech enhancement Signal processing Data retrieval Acoustics

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