scholarly journals AI Based Monitoring of Different Risk Levels in COVID-19 Context

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 298
Author(s):  
César Melo ◽  
Sandra Dixe ◽  
Jaime C. Fonseca ◽  
António H. J. Moreira ◽  
João Borges
Keyword(s):  
Synthetic Data ◽  
Virus Transmission ◽  
Data Generation ◽  
Average Precision ◽  
Risk Levels ◽  
Health Authorities ◽  
Synthetic Data Generation ◽  
Political Effects ◽  
Thermal Cameras ◽  
Keypoint Detector

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

2007 ◽  
Author(s):  
Marek K. Jakubowski ◽  
David Pogorzala ◽  
Timothy J. Hattenberger ◽  
Scott D. Brown ◽  
John R. Schott
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Hyperspectral Data ◽  
Data Generation ◽  
Synthetic Data Generation

Synthetic Data Generation to Support Irregular Sampling in Sensor Networks

2004 ◽  
pp. 211-234 ◽  
Author(s):  
Lewis Girod ◽  
Ramesh Govindan ◽  
Deepak Ganesan ◽  
Deborah Estrin ◽  
Yan Yu
Keyword(s):  
Sensor Networks ◽  
Synthetic Data ◽  
Irregular Sampling ◽  
Data Generation ◽  
Synthetic Data Generation

scholarly journals Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

2021 ◽  
Author(s):  
Maria Lyssenko ◽  
Christoph Gladisch ◽  
Christian Heinzemann ◽  
Matthias Woehrle ◽  
Rudolph Triebel
Keyword(s):  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Crowded Scenes ◽  
Instance Segmentation

Synthetic Data Generation Capabilties for Testing Data Mining Tools

MILCOM 2006 ◽  
2006 ◽  
Author(s):  
Daniel Jeske ◽  
Pengyue Lin ◽  
Carlos Rendon ◽  
Rui Xiao ◽  
Behrokh Samadi
Keyword(s):  
Data Mining ◽  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Testing Data ◽  
Mining Tools

scholarly journals Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs

2019 ◽  
Vol 30 (3) ◽  
pp. 627-648 ◽  
Author(s):  
Evelyn Buckwar ◽  
Massimiliano Tamborrino ◽  
Irene Tubikanec
Keyword(s):  
Numerical Methods ◽  
Spectral Density ◽  
Diffusion Processes ◽  
Model Simulation ◽  
Broad Class ◽  
Synthetic Data ◽  
Summary Statistics ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Partially Observed

Abstract Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling time-dependent, real-world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise: First, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the stochastic process under the same parameter configuration result in different trajectories. Second, exact simulation schemes to generate trajectories from the stochastic model are rarely available, requiring the derivation of suitable numerical methods for the synthetic data generation. To obtain summaries that are less sensitive to the intrinsic stochasticity of the model, we propose to build up the statistical method (e.g. the choice of the summary statistics) on the underlying structural properties of the model. Here, we focus on the existence of an invariant measure and we map the data to their estimated invariant density and invariant spectral density. Then, to ensure that these model properties are kept in the synthetic data generation, we adopt measure-preserving numerical splitting schemes. The derived property-based and measure-preserving ABC method is illustrated on the broad class of partially observed Hamiltonian type SDEs, both with simulated data and with real electroencephalography data. The derived summaries are particularly robust to the model simulation, and this fact, combined with the proposed reliable numerical scheme, yields accurate ABC inference. In contrast, the inference returned using standard numerical methods (Euler–Maruyama discretisation) fails. The proposed ingredients can be incorporated into any type of ABC algorithm and directly applied to all SDEs that are characterised by an invariant distribution and for which a measure-preserving numerical method can be derived.

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