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Entertainment incentives such as gamification often work better in certain types of mobile crowdsensing applications, particularly those involving environment monitoring and location services [1].
We first present the principles of micro-seismic monitoring and location, as well as an underground explosion-proof micro-seismic monitoring system.
Moreover, this study can serve as a reference design for future system integrations where more functions, such as environment monitoring and location tracking, can be added.
In this paper, we propose a human identification, monitoring, and location tracking system, called SmartITS, which continuously tracks MAC ids of user equipment (Smartphones, BLE tags, and Bluetooth devices) and can provide a Google map-based visualization of their trajectories.
Another example is the use of a GIS to link disease registry information with public water supply monitoring and location data to determine potential study areas for evaluating the relation between disinfection byproducts exposure and adverse reproductive outcomes and cancer (Raucher et al. 2000).
Such systems would provide first responders, disaster command centres and supporting hospitals with medical data to track and monitor the condition of up to thousands of victims on a moment-to-moment basis using vital signs monitoring and location tagging (similar to global positioning system tagging).
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A site map of an abandoned HCH pesticide manufacturing waste site, showing monitoring transect and location of waste piles and storage tank is also presented.
Over the past several decades, a variety of location-based services emerged in the fields such as emergency medical systems, asset monitoring and tracking, location sensitive billing, fraud protection, fleet management, and even also including intelligent transportation systems [2, 3].
An intelligent algorithm is proposed for practical implementation of DOA technique for PD monitoring and automated location.
Fields of applications include search and rescue, medical care, intelligent transportation, location-based billing, security, home automation, industrial monitoring and control, location-assisted gaming, and social networking.
Density plots of the geographic covariates for monitoring and subject locations indicated noticeable overlap for all geographic covariates (data not shown), suggesting differences in geographic covariates between monitor and MESA locations were consistent with the concentration of MESA subjects in urban locations, not extrapolation beyond our data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com