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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

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A Pipeline for Immersive data visualization in CAVE Systems

A Pipeline for Immersive Data Visualization in CAVE Systems

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Challa, Koundinya Jaiswal, Chandra Pendem, Abhinav Gokaraju, Balakrishna North Carolina A&T State University Dept. Computational Data Science and Engineering Greensboro United States

ISBN: (纸本)9798331504847

Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi-dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making. © 2025 IEEE.

关键词： Video analysis

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GenCrawl: A Generative Multimedia Focused Crawler for Web Pages Classification 16

GenCrawl: A Generative Multimedia Focused Crawler for Web Pa...

引用

16th International conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Benfenati, Domenico Rinaldi, Antonio Maria Russo, Cristiano Tommasino, Cristian University of Naples Federico II Naples Italy

ISBN: (纸本)9789897587160

The unprecedented expansion of the internet necessitates the development of increasingly efficient techniques for systematic data categorization and organization. However, contemporary state-of-the-art techniques often need help with the complex nature of heterogeneous multimedia content within web pages. These challenges, which are becoming more pressing with the rapid growth of the internet, highlight the urgent need for advancements in information retrieval methods to improve classification accuracy and relevance in the context of varied and dynamic web content. In this work, we propose GenCrawl, a generative multimedia-focused crawler designed to enhance web document classification by integrating textual and visual content analysis. Our approach combines the most relevant topics extracted from textual and visual content, using innovative generative techniques to create a visual topic. The reported findings demonstrate significant improvements and a paradigm shift in classification efficiency and accuracy over traditional methods. GenCrawl represents a substantial advancement in web page classification, offering a promising solution for systematically organizing web content. Its practical benefits are immense, paving the way for more efficient and accurate information retrieval in the era of the expanding internet. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities 3

Data-driven visual analytics of Human Mobility data and gree...

引用

3rd IEEE International conference on Intelligent Technologies, CONIT 2023

作者： Ramanathan, Sivasubramanian Syed, Kulsoom Chavan, Tejaswini B. Tech Computer Science & Business Systems SVKMs NMIMS Navi Mumbai Mumbai India School of Technology Management & Engineering SVKMs NMIMS Navi Mumbai Mumbai India

ISBN: (纸本)9798350338607

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities. © 2023 IEEE.

关键词： Smart city

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TCMIDP: A Comprehensive database of Traditional Chinese Medicine for Network Pharmacology Research 35th

TCMIDP: A Comprehensive Database of Traditional Chinese Medi...

引用

35th International conference on database and Expert Systems Applications (DEXA)

作者： Ye, Lei Liu, Wenxiang Zheng, Yuhao Li, Jianhua East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9783031683084;9783031683091

Network pharmacology is a research method based on biological information data and networks, which can reveal the mechanism of action of Traditional Chinese Medicine, and then discover more active substances with therapeutic effects. However, most of the existing TCM databases lack the collection of TCM prescription data and in-depth data mining and visualization for both TCM and its related information, leading to a limited support in network pharmacology research. In this paper we have constructed Traditional Chinese Medicine Information database Platform (TCMIDP) for network pharmacology research. It is composed of TCM composition information database and Web-based built-in TCM network pharmacology module. The TCM composition information database collects hierarchical data which contains 4 kinds of TCM resource entities and 6 kinds of associations. In the Web-based TCM network pharmacology module, a visual interactive network diagram displays the data entities and their associations. To mine the TCM data in the above database, node mining and clustering analyses are provided for users to do network pharmacology research. The analyses result, coupled with the visual interactive network diagram can help exploring the 4 types of TCM resource entities that have key regulatory functions in the integrated information network of TCM. TCMIDP makes a significant contribution to data collection, data mining and visualization analysis of TCM, and provides more valuable information support for network pharmacology research.

关键词： TCM database data mining and visualization TCM network pharmacology research

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Experimental analysis of Radar/Optical track-to-track fusion for non-cooperative Sense and Avoid

Experimental analysis of Radar/Optical track-to-track fusion...

引用

International conference on Unmanned Aircraft Systems (ICUAS)

作者： Vitiello, Federica Causa, Flavia Opromolla, Roberto Fasano, Giancarmine Univ Naples Federico II Dept Ind Engn I-80125 Naples Italy

ISBN: (纸本)9798350310375

In the framework of non-cooperative Sense and Avoid solutions, major attention is reserved to the design of sensing strategies which can enable fast and reliable identification of possible near collision threats, by exploiting passive or active exteroceptive sensors. To overcome the limits of standalone technological architectures and provide more accurate tracking solutions, which can be used to improve conflict detection and thus better support avoidance strategies, data fusion approaches can be considered. Hence, this work proposes a radar/visual fusion method based on the track-to-track fusion approach. The strategy is tested on data gathered during ground-to-air experimental flight tests involving a small UAV commanded to fly near collision approach geometries with respect to a multi-sensor setup placed on the ground. Results collected analyzing three different encounters show that the fusion solution allows retrieving meter and sub-degree level accuracies in the intruder range and bearing estimation, respectively, while ensuring declaration ranges of about 500 meters.

关键词： data fusion

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Audio Perception in Robotic Assistance for Human Space exploration: A Feasibility Study

Audio Perception in Robotic Assistance for Human Space Explo...

引用

IEEE Aerospace conference

作者： Sewtz, Marco Friedl, Werner Bauer, Adrian Koepken, Anne Lay, Florian Bechtel, Nicolai Schmaus, Peter Triebel, Rudolph Lii, Neal Y. German Aerosp Ctr DLR Inst Robot & Mechatron Muenchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665490320

Future crewed missions beyond low earth orbit will greatly rely on the support of robotic assistance platforms to perform inspection and manipulation of critical assets. This includes crew habitats, landing sites or assets for life support and operation. Maintenance and manipulation of a crewed site in extraterrestrial environments is a complex task and the system will have to face different challenges during operation. While most may be solved autonomously, in certain occasions human intervention will be required. The telerobotic demonstration mission, Surface Avatar, led by the German Aerospace Center (DLR), with partner European Space Agency (ESA), investigates different approaches offering astronauts on board the International Space Station (ISS) control of ground robots in representative scenarios, e.g. a Martian landing and exploration Site. In this work we present a feasibility study on how to integrate auditory information into the mentioned application. We will discuss methods for obtaining audio information and localizing audio sources in the environment, as well as fusing auditory and visual information to perform state estimation based on the gathered data. We demonstrate our work in different experiments to show the effectiveness of utilizing audio information, the results of spectral analysis of our mission assets, and how this information could help future astronauts to argue about the current mission situation.

关键词： Robotics

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An exploration of Machine Learning Methods for Gait analysis of Potential Guide Dogs 10

An Exploration of Machine Learning Methods for Gait Analysis...

引用

10th International conference on Animal-Computer Interaction (ACI)

作者： Wu, Yifan Nichols, Colt Foster, Marc Martin, Devon Dieffenderfer, James Enomoto, Masataka Lascelles, B. Duncan X. Russenberger, Jane Brenninkmeyer, Gerald Bozkurt, Alper Roberts, David L. NC State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA NC State Univ Coll Vet Med Raleigh NC USA Int Working Dog Registry San Antonio TX USA Guiding Eyes Blind Director Training Yorktown Hts NY USA NC State Univ Dept Comp Sci Raleigh NC USA

ISBN: (纸本)9798400716560

Gait analysis is important for guide dog organizations, as ideal guide dogs have a smooth and efficient gait, where they can also easily shift between and maintain various gaits. Gait quality and natural traveling speed are two of the multiple factors important in matching a guide dog to its visually impaired handler. Gait evaluation typically includes subjective visual observation of the dog or objective assessments obtained from special-designed equipment. Guide dog organizations need a method to easily collect and analyze objective data of gait information. In this work, we explored how various machine learning models could learn and analyze gait patterns from inertial measurements data that were collected during two different data collection experiments using a wearable sensor device. We also evaluated how well each machine learning model could generalize behavior patterns from various dogs under different environments. Additionally, we compared how sensor placement locations could affect gait prediction performance by attaching the sensor device to the dog's neck and back area respectively. The tested machine learning models were able to classify different gaits in the range of 42% to 91% in terms of accuracy, and predict various gait parameters with an error rate ranging from 14% to 29% depending on the setup. Furthermore, we also observed that using behavior data collected from the neck region contains more movement information than the back area. By performing a cross-dataset generalization test on the machine learning models, we found that even with performance drop, the models were able to learn gait-specific behavior patterns that are generalizable for different dogs. Although the results were preliminary, the proposed gait analysis exploration still showed promising potential for studying behavior patterns of candidate guide dogs.

关键词： Guide Dog Canine Behavior analysis Canine Gait analysis IMU Machine Learning

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A comparative analysis of chaos theory based medical image steganography to enhance data security 2

A comparative analysis of chaos theory based medical image s...

引用

2nd International conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Jha, Anand Kumar Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Information and Technology VelTech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper advocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical information exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain approaches, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNR and SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift Register (LFSR), and the durable Rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange. © 2024 Elsevier B.V.. All rights reserved.

关键词： Cryptography

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Multiband remote 3D-ViT data fusion 30

Multiband remote 3D-ViT data fusion

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conference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

作者： Kurz, Aidan G. Depoian, Arthur C., ii Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673816;9781510673809

Satellites are equipped with diverse sensors, capable of capturing detailed information across a multitude of wavelengths. The fusion of multispectral data is pivotal to amplify the visual representation of the area of interest. The improvement of information representation allows for enhanced processing, analysis, and other crucial tasks for numerous fields of study, including remote sensing, defense, and material characterization. Previous solutions often utilize traditional signal processing techniques, including principal component analysis (PCA), to accomplish data fusion. By performing fusion on a feature level, extracted information about the area of interest texture and boundaries are combined. The introduction of neural network techniques improved the reconstruction of data similar to the results obtained by conventional inference of humans. For example, the use of deep learning algorithms in conjunction with PCA allowed for refined reduction of redundancy and distortion of spectral data, in comparison to traditional methods alone. The introduction of the Vision Transformer (ViT) architecture, originally developed for two-dimensional image data, has revolutionized image processing tasks, vastly improving performance at the cost of a large quantity of trainable parameters. Recent experimentation has proven that optimizing ViT for efficiency allows for comparable or even superior performance while lessening the computational cost. The transition from 2D to 3D information via utilization of additional depth and spatial data has also led to superior results as the added information allows for better representation of terrain features, making it invaluable for satellite imagery analysis. Combining the principles of ViT and 3D information to process complex satellite data can result in more effective data fusion to achieve a superior level of data visualization of multispectral satellite imagery in an efficient manner.

关键词： data Fusion Multispectral Fusion Remote Sensing Deep Learning

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Evaluating visual-Selective visual-Inertial Odometry: An End-to-End Multi-Modal Pose Estimation Framework for Underwater Environments 21

Evaluating Visual-Selective Visual-Inertial Odometry: An End...

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21st International conference on Advanced Robotics, ICAR 2023

作者： Sudevan, Vidya Zayer, Fakhreddine Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342291

This paper presents an evaluation of the performance of visual-Selective visual-Inertial Odometry (VS-VIO), a hybrid learning-based multi-modal pose estimation framework, in the challenging underwater domain. The assessment is based on Root Mean Square (RMSE) scores for translation and rotation vectors, compared to their reference values. The underwater environment, characterized by low lighting and high turbidity due to suspended particles, poses significant challenges for pose estimation. Understanding how hybrid learning-based multi-modal frameworks perform in such conditions is crucial for improving underwater navigation and exploration. In this study, we thoroughly analyze the performance of VS-VIO and its baseline model at the sub-sequence level, focusing on pose error. Additionally, we assess various technical aspects during the inference phase, including inference speed, power consumption, GPU utilization, GPU memory usage, and temperature. All evaluations are conducted using the AQUALOC dataset. Our findings reveal that the policy network within VS-VIO exhibits the ability to dynamically reduce the utilization of the visual modality while maintaining pose estimation accuracy. However, our analysis shows no statistically significant reduction in the percentage of visual modality usage when altering the penalty factor. These insights provide valuable guidelines for enhancing the performance of hybrid learning-based multimodal pose estimation frameworks in challenging underwater environments, contributing to advancements in underwater navigation and exploration technologies. © 2023 IEEE.

关键词： data fusion

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