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Modeling analysis of carbon sequestration in visual forests 3

Modeling analysis of carbon sequestration in visual forests

引用

3rd International conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Chunyan Han, Yao Gao, Shan Harbin Cambridge University Harbin150069 China

Forests are the largest pool of carbon in terrestrial ecosystems and are important for climate change mitigation. By analyzing the carbon sequestration process of forest ecosystem, the carbon sequestration amount of forest can be modeled as a nonlinear function, and the mechanism of forest carbon sequestration can be discussed on this basis. The research first introduced the relevant theories of forest carbon sequestration, followed by the preparation of the visualization system construction based on the research area and data sources, and then the construction of the visualization system, and finally carried out the simulation verification. The results show that the system is effective and accurate. © 2023 The Authors. Published by Elsevier B.V.

关键词： Carbon

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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...

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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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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...

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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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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...

引用

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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Transactional Panorama: A Conceptual Framework for User Perception in Analytical visual Interfaces 49th

Transactional Panorama: A Conceptual Framework for User Perc...

引用

49th International conference on Very Large data Bases (VLDB)

作者： Tang, Dixin Fekete, Alan Gupta, Indranil Parameswaran, Aditya G. Univ Calif Berkeley Berkeley CA 94720 USA Univ Sydney Sydney NSW Australia Univ Illinois Champaign IL USA

Many tools empower analysts and data scientists to consume analysis results in a visual interface. When the underlying data changes, these results need to be updated, but this update can take a long time-all while the user continues to explore the results. Tools can either (i) hide away results that haven't been updated, hindering exploration;(ii) make the updated results immediately available to the user (on the same screen as old results), leading to confusion and incorrect insights;or (iii) present old-and therefore stale-results to the user during the update. To help users reason about these options and others, and make appropriate trade-offs, we introduce Transactional Panorama, a formal framework that adopts transactions to jointly model the system refreshing the analysis results and the user interacting with them. We introduce three key properties that are important for user perception in this context: visibility (allowing users to continuously explore results), consistency (ensuring that results presented are from the same version of the data), and monotonicity (making sure that results don't "go back in time"). Within transactional panorama, we characterize all feasible property combinations, design new mechanisms (that we call lenses) for presenting analysis results to the user while preserving a given property combination, formally prove their relative orderings for various performance criteria, and discuss their use cases. We propose novel algorithms to preserve each property combination and efficiently present fresh analysis results. We implement our framework into a popular, open-source BI tool, illustrate the relative performance implications of different lenses, and demonstrate the benefits of the novel lenses and our optimizations.

关键词： Economic and social effects

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Opening the Black Box of 3D Reconstruction Error analysis with VECTOR

Opening the Black Box of 3D Reconstruction Error Analysis wi...

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2024 IEEE visualization and visual Analytics conference, VIS 2024

作者： Fygenson, Racquel Jawad, Kazi Li, Isabel Ayoub, Francois Deen, Robert G. Davidoff, Scott Moritz, Dominik Hess-Flores, Mauricio Northeastern University United States Wētā Fx New Zealand Art Center College of Design United States California Institute of Technology Jet Propulsion Laboratory United States Carnegie Mellon University United States

ISBN: (纸本)9798350354850

Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission. © 2024 IEEE.

关键词： NASA

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visual exploration of CO2 emissions in chinese cities 23

Visual exploration of CO2 emissions in chinese cities

引用

20th ACM International conference on Computing Frontiers (CF)

作者： Ma, Dongliang Wang, Song Chen, Shijie Liu, Yipan Wang, Yanru Wu, Yadong Southwest Univ Sci & Technol Mianyang Sichuan Peoples R China Sichuan Univ Sci & Engn Yibin Sichuan Peoples R China

ISBN: (纸本)9798400701405

As the primary source of carbon dioxide (CO2) emissions, cities are the key to solving climate change. Prior works focus on CO2 emission drivers and CO2 emission city clusters. However, the methods of mining CO2 emission drivers include narrow factors affecting CO2 emissions, and ignore the interactions among these factors. The intelligibility of the results of the Geographical Weighted Regression (GWR) that is used to analyze the spatial heterogeneity of drivers is poor. Moreover, the relations between CO2 emission city clusters and economic ties among cities are ignored. The lack of visual analysis tools is also a problem that needs to be fixed. Hence we develop a novel analysis framework. Thereinto, a novel method based on the stepwise regression (SR) is used to mine drivers;the biclustering algorithm is introduced into the traditional GWR to improve the intelligibility of the GWR;the gravity-entropy model is adopted to analyze the relations between CO2 emission city clusters and economic correlation strength among cities. Furthermore, a visual analytics system is implemented to explore the situation of CO2 emissions. We demonstrate the effectiveness of our approach through case studies conducted with socioeconomic data from 169 Chinese cities and positive feedback from experts and volunteers.

关键词： carbon dioxide emissions biclustering algorithm gravity-entropy model stepwise regression model visualization and visual analytics

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Proceedings of the 2024 Pacific Neighborhood Consortium Annual conference and Joint Meetings: Green, Flow, and Signal: Reconnecting Fragmented Communities in the Age of Digital Transformation, PNC 2024

Proceedings of the 2024 Pacific Neighborhood Consortium Annu...

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2024 Pacific Neighborhood Consortium Annual conference and Joint Meetings, PNC 2024

ISBN: (纸本)9789869531764

The proceedings contain 8 papers. The topics discussed include: a visual representation of Wittgenstein’s Tractatus Logico-Philosophicus, including the Chinese, Korean and Japanese translation;analyzing the inquiry learning abilities of elementary school students using a simulation system: a preliminary exploration;deriving temporal position of a period based on positional relationships between periods using linked data;ensuring fairness with transparent auditing of quantitative bias in AI systems;exploring cultural preservation and innovation: augmented reality packaging prototyping for the Mah Meri tribe in Malaysia;exploring Taiwan university students’ digital humanities literacy using visualization interactive data analysis dashboard;and when drone meets ai education: boosting high school students’ computational thinking and AI literacy.

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