Relevant bibliographies by topics / Real-Time Pose Detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Real-Time Pose Detection'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Real-Time Pose Detection"

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K, Athira. "Real-Time feedback system for Accurate Yoga Pose Detection." International Journal of Research Publication and Reviews 6, no. 5 (2025): 11297–303. https://doi.org/10.55248/gengpi.6.0525.1898.

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Meng, Zhichao, Xiaoqiang Du, Ranjan Sapkota, Zenghong Ma, and Hongchao Cheng. "YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models." Computers in Industry 165 (February 2025): 104231. https://doi.org/10.1016/j.compind.2024.104231.

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Di Natali, Christian, Marco Beccani, and Pietro Valdastri. "Real-Time Pose Detection for Magnetic Medical Devices." IEEE Transactions on Magnetics 49, no. 7 (2013): 3524–27. http://dx.doi.org/10.1109/tmag.2013.2240899.

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Nagargoje, Shrinivas, Adesh Shinde, Pranav Tapadiya, Om Shinde, and Prof Anita Devkar. "Yoga Pose Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2053–60. http://dx.doi.org/10.22214/ijraset.2023.51821.

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Abstract: This paper describes a real-time yoga pose detection system that can accurately classify and detect yoga poses in images using Convolutional Neural Networks (CNNs) and OpenPose. By using OpenPose, the system generates a 3D joint map of the person's body, which is then used as input for linear regression to detect the individual yoga pose. The system is suitable for real-time applications, and is expected to be used in fitness centers, yoga studios, and even for personal use. Additionally, the system can also be used to track the progress of yoga practitioners, allowing them to analyz
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Sidana, Khushi. "REAL TIME YOGA POSE DETECTION USING DEEPLEARNING: A REVIEW." International Journal of Engineering Applied Sciences and Technology 7, no. 7 (2022): 61–65. http://dx.doi.org/10.33564/ijeast.2022.v07i07.011.

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With the increase in the number of yoga practitioners every year, the risk of injuries as a result of incorrect yoga postures has also increased. A selftraining model that can evaluate the posture of individuals is the optimal solution for this issue. This objective can be attained with the aid of computer vision and deep learning. A model that can detect theyoga pose performed by an individual, evaluate it in comparison to the pose performed by an expert, and provide the individual with instructive feedback would be an effective solution to this problem. Recently, numerous researchers have co
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Nenchoo and Tantrairatn. "Real-Time 3D UAV Pose Estimation by Visualization." Proceedings 39, no. 1 (2020): 18. http://dx.doi.org/10.3390/proceedings2019039018.

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This paper presents an estimation of 3D UAV position in real-time condition by using Intel RealSense Depth camera D435i with visual object detection technique as a local positioning system for indoor environment. Nowadays, global positioning system or GPS is able to specify UAV position for outdoor environment. However, for indoor environment GPS hasn’t a capability to determine UAV position. Therefore, Depth stereo camera D435i is proposed to observe on ground to specify UAV position for indoor environment instead of GPS. Using deep learning for object detection to identify target object with
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Bhargavi, Mrs Jangam, Chitikala Sairam, and Donga Hemanth. "Real time interface for deaf-hearing communication." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02356.

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Bridging the communication gap between the deaf and hearing communities using AI is achieved by integrating two key modules: Speech-to-Sign Language Translation and Sign Gesture Detection in Real Time. The first module translates English spoken language into American Sign Language (ASL) animations. It consists of three sub-modules: speech-to-text conversion using the speech recognition module in Python, English text to ASL gloss translation using an NLP model, and ASL gloss to animated video generation, where DWpose Pose Estimation, and an avatar is used for visual representation. The second m
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Liu, Yonghui, Weimin Zhang, Fangxing Li, Zhengqing Zuo, and Qiang Huang. "Real-Time Lidar Odometry and Mapping with Loop Closure." Sensors 22, no. 12 (2022): 4373. http://dx.doi.org/10.3390/s22124373.

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Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In ou
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Vatsal, Prince. "Real-Time Human Pose Estimation Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34377.

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Human pose estimation is a pivotal domain within computer vision, underpinning applications from motion capture in cinematic production to sophisticated user interfaces in desktop devices. This research delineates the implementation of real-time human pose estimation within web browsers utilizing TensorFlow.js and the PoseNet model. PoseNet, an advanced machine learning model optimized for browser-based execution, facilitates precise pose detection sans specialized hardware. The primary aim of this study is to integrate PoseNet with TensorFlow.js, achieving efficient real-time pose estimation
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Mulla, A. S. "Real-Time Cyber Security Protection Tool." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 183–86. https://doi.org/10.22214/ijraset.2025.65807.

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This report aims to examine the evolving cybersecurity threats, including DeepFakes, phishing, social engineering, and malware, and analyze detection mechanisms to counter these threats. DeepFakes, generated using advanced AI techniques, pose risks such as identity theft and disinformation, with detection models like CNNs and RNNs showing promise, albeit with reduced effectiveness against high-quality manipulations. Tools like Face Forensics++ are instrumental for training such models. Phishing, which employs deceptive techniques to steal sensitive information, is addressed through URL-based d
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Dissertations / Theses on the topic "Real-Time Pose Detection"

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Balaji, Ranjani. "True Real Time Pose Independent Face Detection Using Color Information and Skin Region Segmentation." TopSCHOLAR®, 2005. http://digitalcommons.wku.edu/theses/488.

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The process of detecting a face from a video in real time is essential in applications such as human surveillance, human computer-interaction, and for further face recognition research purposes. In this paper, the face detection algorithm is divided into four stages namely, Video Database Acquisition (VDA), Frame Sequence Extraction (FSE), Skin Region Detection (SRD), and K-Mean Face Segmentation (KFS). Initially, the videos in MPEG format are converted to JPEG images depending on the user specified frame rate (FSE phase). During this conversion, the face detection process comprising of SRD an
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White, Jacob Harley. "Real-Time Visual Multi-Target Tracking in Realistic Tracking Environments." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7486.

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This thesis focuses on visual multiple-target tracking (MTT) from a UAV. Typical state-of-the-art multiple-target trackers rely on an object detector as the primary detection source. However, object detectors usually require a GPU to process images in real-time, which may not be feasible to carry on-board a UAV. Additionally, they often do not produce consistent detections for small objects typical of UAV imagery.In our method, we instead detect motion to identify objects of interest in the scene. We detect motion at corners in the image using optical flow. We also track points long-term to co
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Carraro, Marco. "Real-time RGB-Depth preception of humans for robots and camera networks." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426800.

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This thesis deals with robot and camera network perception using RGB-Depth data. The goal is to provide efficient and robust algorithms for interacting with humans. For this reason, a special care has been devoted to design algorithms which can run in real-time on consumer computers and embedded cards. The main contribution of this thesis is the 3D body pose estimation of the human body. We propose two novel algorithms which take advantage of the data stream of a RGB-D camera network outperforming the state-of-the-art performance in both single-view and multi-view tests. While the first algo
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Hinterstoißer, Stefan Verfasser], Nassir [Akademischer Betreuer] [Navab, Bernt [Akademischer Betreuer] Schiele, and Kurt [Akademischer Betreuer] Konolige. "Real-time detection and pose estimation of low-textured and texture-less objects / Stefan Hinterstoißer. Gutachter: Bernt Schiele ; Kurt Konolige. Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1030099480/34.

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Alqahtani, Faleh Mohammed A. "Three-dimensional facial tracker using a stereo vision system." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/131825/1/Faleh%20Mohammed%20A_Alqahtani_Thesis.pdf.

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This thesis develops an algorithm enabling accurate tracking of human faces, precise estimation of head poses, efficient resolution of occlusions and improved depth perception under different lighting conditions. The system also utilises two stereo cameras that have the ability to track movements across six degrees of freedom, thereby accounting for pose variations. The system can address circumstances in which facial features are no longer discernible, as the results demonstrate increased accuracy in real-time estimation of head poses and facial landmark features. It can also precisely map fa
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Stunes, Sindre. "Methods of Pore Pressure Detection from Real-time Drilling Data." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for petroleumsteknologi og anvendt geofysikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18899.

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The knowledge of formation pore pressure, and how it changes throughout the length of a well, is crucial in terms of maintaining control of the wellbore. Failure to recognize deviations from the expected pressures can lead to problems and instabilities, which increases drilling costs. A worst case scenario may lead to loss of an entire well section. Thus maintaining a real-time knowledge of the formation pore pressure is beneficial regarding both the cost and the safety of a drilling operation.In this thesis multiple methods of pore pressure detection have been implemented in a Matlab program,
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Georgantzoglou, Antonios. "Development of near real-time image processing techniques for cell detection, microbeam targeting and tracking post-irradiation." Thesis, University of Cambridge, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709522.

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Szentandrási, István. "Určení pozice kamery v reálném čase pro rozšířenou realitou." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-412551.

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Definované markery tvoří základ určování polohy kamery pro velké množství aplikací s rozšířenou realitou, v případě že jsou přísné požadavky na rychlost a robustnost. Tato práce popisuje účinnou metodu pro určení pózy kamery pomocí Uniformního pole markerů a několik realistických aplikací na bázi popsané metody. Metoda je velice výpočetně levná a poskytuje spolehlivou detekci pro několik výpočetních platforem, včetně běžných chytrých telefonů. Markery jako část zobrazené informace na monitorech jsou použité v této práci pro určení relativní orientaci mezi poskytovatelem obsahu a užívatelským z
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Chen, Hsiao-Ying, and 陳曉瑩. "Real-time Multi-pose Face Detection." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/43796486801779799309.

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碩士<br>國立清華大學<br>電機工程學系<br>94<br>In the thesis, a new feature set which is composed with Gabor feature and Haar-like feature named hybrid feature set is proposed. The goal of this thesis is to create an automatic face detection system which is robust to pose and head motion. Our face detection system consists of two modules. The first module searches the potential face regions by using skin color detection and segmentation procedures. The second module selects the features of the scanned image. This system can be used in different sizes, varying poses, different expressions, and defocus problem
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Chang, Che-Hao, and 張哲豪. "Real-time Hand Pose Estimation with Feature Points Detection using Kinect." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/93138234663190940924.

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碩士<br>國立清華大學<br>電機工程學系<br>102<br>This thesis presents a real-time and precise depth image based hand pose estimation method. The depth image obtained from Kinect is converted into a feature vector for regression functions to retrieve hand joint parameters. Different from the two mainly proposed methods, model-based and appearance-based, our approach retrieves continuous result within a short period of time. In the beginning, the hand region is segmented from the depth image. Some specific feature points on the hand are located by random forest classifier, and the relative displacements of thes
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Book chapters on the topic "Real-Time Pose Detection"

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Panin, Giorgio, Sebastian Klose, and Alois Knoll. "Real-Time Articulated Hand Detection and Pose Estimation." In Advances in Visual Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10520-3_108.

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Moryossef, Amit, Ioannis Tsochantaridis, Roee Aharoni, Sarah Ebling, and Srini Narayanan. "Real-Time Sign Language Detection Using Human Pose Estimation." In Computer Vision – ECCV 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66096-3_17.

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Singh, Amritanshu Kumar, Vedant Arvind Kumbhare, and K. Arthi. "Real-Time Human Pose Detection and Recognition Using MediaPipe." In Advances in Intelligent Systems and Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7088-6_12.

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Suryanarayan, S., P. A. Arjun, K. S. Nisha, and Divya Udayan. "Real-Time Fall Pose Estimation and Fall Detection from Videos." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2694-6_44.

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Alam, Ekram, Abu Sufian, Paramartha Dutta, and Marco Leo. "Real-Time Human Fall Detection Using a Lightweight Pose Estimation Technique." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48879-5_3.

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Bhelkar, Chaitreya, Alkesh Tripathi, Shweta Mishra, Lokesh Malviya, and Snehal Awachat. "Deep Learning-Based Pose Estimation and Real-Time Toddler Fall Detection System." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-1943-3_13.

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Micilotta, Antonio S., Eng-Jon Ong, and Richard Bowden. "Real-Time Upper Body Detection and 3D Pose Estimation in Monoscopic Images." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_11.

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Patel, Mayank, Shailesh Bamoriya, Eashita Chowdhury, and Cheruvu Siva Kumar. "Pose Detection-Based Real-Time Control of Robotic Manipulator for Remote Locations." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5423-6_50.

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Díaz Barros, Jilliam M., Bruno Mirbach, Frederic Garcia, Kiran Varanasi, and Didier Stricker. "Real-Time Head Pose Estimation by Tracking and Detection of Keypoints and Facial Landmarks." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26756-8_16.

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Leunikau, Aliaksandr, Alexander Nedzved, Alexei Belotserkovsky, and Stanislav Sholtanyuk. "A Bottom-Up Method for Pose Detection of Multiple People on Real-Time Video." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98883-8_14.

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Conference papers on the topic "Real-Time Pose Detection"

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Jeyabharathi, J., Gokul Prasath S K, Gorijala VenuMadhav, Gudivada Dinesh, and Ch Veera Venkata Saikumarreddy. "Real Time Gameplay using Pose Detection." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894142.

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Dhanasekar, Swetha, and S. Sindhu. "Asana AI – Yoga Pose Detection Using Pose Estimation and Real-Time Feedback System." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11052949.

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Sikarwar, Khushi, Nookala Venu, Aditya Dubey, and Dhananjay Bisen. "AI-Based System for Real-Time Yoga Pose Detection and Correction." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10940777.

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Xu, Wenhao, Jiawen Zhou, and Hongying Liu. "Rehabot: A Real-Time Pose Detection and Feedback System for Upper Limb Rehabilitation." In 2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2025. https://doi.org/10.1109/aemcse65292.2025.11042322.

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Inchara, K. P., B. S. Rajeshwari, and Lava Kumar. "Real Time Suryanamaskara Pose Detection and Correction using Mediapipe and Comparative Analysis Using PoseNet." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796505.

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Sonawane, Achal, Varshini Dandam, Kiran Khamkar, Tanushri Wawge, and Priyanka More. "Leveraging YOLO for Real-Time Human Detection and Pose Estimation in Live Stream Environments." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020018.

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Syiemlieh, Banrilin, Lapynhunshisha Jyrwa, Chemerita Ch Marak, and Sarat Kumar Chettri. "Real-Time Yoga Pose Detection and Classification Using CNNs for Enhanced Fitness and Rehabilitation." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070442.

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Dienes, Anca, and William Fazackerley. "Effective Sand Management Using Complimentary Non-Intrusive Erosion Monitoring Solutions." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-20641.

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Abstract Sand production and erosion pose significant challenges in the oil and gas industry, leading to equipment damage, production loss, and costly repairs. Existing sand control techniques such as well completion, sand screens, sand separation, or predictive models for sand production are not 100% effective. This paper presents a comprehensive approach to sand management utilizing complimentary non-intrusive erosion monitoring solutions. The focus is on the integration of acoustic sensors and ultrasonic thickness sensors to detect and assess sand particles in pipelines and process equipmen
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Cozar, Julian R., Nicolas Guil, and Emilio L. Zapata. "Pose detection of cameras in real time." In Electronic Imaging 2003, edited by Sabry F. El-Hakim, Armin Gruen, and James S. Walton. SPIE, 2003. http://dx.doi.org/10.1117/12.473122.

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Zhou, Jian, and Xiao-hu Zhang. "An anti-disturbing real time pose estimation method and system." In International Symposium on Photoelectronic Detection and Imaging 2011. SPIE, 2011. http://dx.doi.org/10.1117/12.900564.

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Reports on the topic "Real-Time Pose Detection"

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Stodola, Kirk, Noah Finney, Peg Gronemeyer, and Lauren Scopel. Understanding Wildlife-Vehicle Collisions in Illinois. Illinois Center for Transportation, 2025. https://doi.org/10.36501/0197-9191/25-006.

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Wildlife-vehicle collisions (WVCs), particularly those involving deer, pose significant risks to public safety, economic stability, and biodiversity. This review and analysis highlight key contributing factors, including habitat fragmentation, road design, vehicle speed, and environmental conditions, which collectively increase the likelihood of WVCs. This review identifies specific locations in Illinois where WVCs are most common and the landscape features that are attributed to them. The review also identifies a range of mitigation strategies that have been implemented with varying degrees o
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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Ass
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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Wagner, Anna, Arthur Gelvin, Jon Maakestad, et al. Initial data collection from a fiber-optic-based dam seepage monitoring and detection system. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47819.

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Visual inspection is the most used method to detect seepage at dams. Early detection can be difficult with this method, and use of appropriate real time monitoring could significantly increase the chances of recognizing possible failure. Seepages can be identified by analyzing changes in water and soil temperature. Optical fiber placed at the embankment’s downstream toe has been proven to be an efficient means of detecting real time changes at short intervals over several kilometers. This study aims to demonstrate how temperatures measured using fiber optic distributed sensing can be used to m
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Keller, Mareike, Aaron J. Becker, Nikolaj Diller, et al. Monitoring ecological consequences of marine munition in the Baltic Sea 2024 - Cruise No. AL622, 14th – 21st October 2024, Kiel (Germany) – Kiel (Germany), „POST-Clear“. GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany, 2024. https://doi.org/10.3289/cr_al622.

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ALKOR cruise AL622 took place as part of the project CONMAR (https://conmarmunition.eu/) which is part of the DAM mission sustainMare (https://www.sustainmare.de/). It was the continuation of the munition monitoring started within the BMBF‐funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/), the EMFF (European Maritime and Fisheries Fund) ‐funded projects BASTA (Boost Applied munition detection through Smart data detection in and AI workflows; https://www.basta‐munition.eu) and ExPloTect (Ex‐situ, near‐real‐time detection compou
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Evans, John T., Greg M. Shaver, Michael A. Mardikes, et al. Autonomous Mower Pilot Project. Purdue University, 2025. https://doi.org/10.5703/1288284317840.

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The Indiana Department of Transportation (INDOT) spends approximately $19 million annually on roadside mowing, which is a process that is time-consuming, costly, and poses safety risks. Automated mowing solutions have the potential to reduce costs and increase safety, but they will need to be extremely well tested before being deployed. In this work, data from current mowing practices was collected using machine mounted cameras, which was used to inform the creation of a digital twin environment that allows for rigorous initial testing of potential autonomous solution with no risk. The digital
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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Wilson, A. M., and M. C. Kelman. Assessing the relative threats from Canadian volcanoes. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328950.

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This report presents an analysis of the threat posed by active volcanoes in Canada and outlines directives to bring Canadian volcano monitoring and research into alignment with global best practices. We analyse 28 Canadian volcanoes in terms of their relative threat to people, aviation and infrastructure. The methodology we apply to assess volcanic threat was developed by the United States Geological Survey (USGS) as part of the 2005 National Volcano Early Warning System (NVEWS). Each volcano is scored on a number of hazard and exposure factors, producing an overall threat score. The overall t
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9

Wilson, A. M., and M. C. Kelman. Assessing the relative threats from Canadian volcanoes. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328950.

Full text
Abstract:
This report presents an analysis of the threat posed by active volcanoes in Canada and outlines directives to bring Canadian volcano monitoring and research into alignment with global best practices. We analyse 28 Canadian volcanoes in terms of their relative threat to people, aviation and infrastructure. The methodology we apply to assess volcanic threat was developed by the United States Geological Survey (USGS) as part of the 2005 National Volcano Early Warning System (NVEWS). Each volcano is scored on a number of hazard and exposure factors, producing an overall threat score. The overall t
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10

Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. The
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