scholarly journals Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy

2019 ◽  
Vol 11 (24) ◽  
pp. 2987 ◽  
Author(s):  
Simona Scollo ◽  
Michele Prestifilippo ◽  
Costanza Bonadonna ◽  
Raffaello Cioni ◽  
Stefano Corradini ◽  
...  
Keyword(s):  
Real Time ◽  
Satellite Images ◽  
Low Cost ◽  
Column Height ◽  
Explosive Eruptions ◽  
Volcanic Plume ◽  
Emergency Responders ◽  
Multiple Sensors ◽  
New System ◽  
Tephra Fallout

During explosive eruptions, emergency responders and government agencies need to make fast decisions that should be based on an accurate forecast of tephra dispersal and assessment of the expected impact. Here, we propose a new operational tephra fallout monitoring and forecasting system based on quantitative volcanological observations and modelling. The new system runs at the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) and is able to provide a reliable hazard assessment to the National Department of Civil Protection (DPC) during explosive eruptions. The new operational system combines data from low-cost calibrated visible cameras and satellite images to estimate the variation of column height with time and model volcanic plume and fallout in near-real-time (NRT). The new system has three main objectives: (i) to determine column height in NRT using multiple sensors (calibrated cameras and satellite images); (ii) to compute isomass and isopleth maps of tephra deposits in NRT; (iii) to help the DPC to best select the eruption scenarios run daily by INGV-OE every three hours. A particular novel feature of the new system is the computation of an isopleth map, which helps to identify the region of sedimentation of large clasts (≥5 cm) that could cause injuries to tourists, hikers, guides, and scientists, as well as damage buildings in the proximity of the summit craters. The proposed system could be easily adapted to other volcano observatories worldwide.

scholarly journals The radius of the umbrella cloud helps characterize large explosive volcanic eruptions

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Robert Constantinescu ◽  
Aurelian Hopulele-Gligor ◽  
Charles B. Connor ◽  
Costanza Bonadonna ◽  
Laura J. Connor ◽  
...  
Keyword(s):  
Satellite Images ◽  
Volcanic Hazard ◽  
Source Parameters ◽  
Volcanic Eruptions ◽  
Column Height ◽  
Explosive Eruptions ◽  
Plume Height ◽  
Direct Observations ◽  
Hazard Assessments ◽  
Explosive Volcanic Eruptions

AbstractEruption source parameters (in particular erupted volume and column height) are used by volcanologists to inform volcanic hazard assessments and to classify explosive volcanic eruptions. Estimations of source parameters are associated with large uncertainties due to various factors, including complex tephra sedimentation patterns from gravitationally spreading umbrella clouds. We modify an advection-diffusion model to investigate this effect. Using this model, source parameters for the climactic phase of the 2450 BP eruption of Pululagua, Ecuador, are different with respect to previous estimates (erupted mass: 1.5–5 × 1011 kg, umbrella cloud radius: 10–14 km, plume height: 20–30 km). We suggest large explosive eruptions are better classified by volume and umbrella cloud radius instead of volume or column height alone. Volume and umbrella cloud radius can be successfully estimated from deposit data using one numerical model when direct observations (e.g., satellite images) are not available.

scholarly journals A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

2019 ◽  
Vol 11 (5) ◽  
pp. 504 ◽  
Author(s):  
Yue Yu ◽  
Ruizhi Chen ◽  
Liang Chen ◽  
Guangyi Guo ◽  
Feng Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Line Of Sight ◽  
Multiple Sensors ◽  
Ranging Model

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Low Cost ◽  
Electronic Systems ◽  
Power Electronic ◽  
Real Time Simulation ◽  
Time Simulation

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

A real time, wireless and low cost data acquisition system for residential PV modules

2020 ◽  
Author(s):  
Cheyma BARKA ◽  
Hanen MESSAOUDI-ABID ◽  
Houda BEN ATTIA SETTHOM ◽  
Afef BENNANI-BEN ABDELGHANI ◽  
Ilhem SLAMA-BELKHODJA ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Low Cost ◽  
Data Acquisition System ◽  
Cost Data ◽  
Acquisition System ◽  
Pv Modules
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