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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Spatial-temporal modeling of background radiation using mobile sensor networks.

Zheng Liu¹, Shiva Abbaszadeh¹, Clair Julia Sullivan¹

¹Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America.

|October 20, 2018

Summary

This summary is machine-generated.

A novel mobile sensor network and maximum likelihood estimation algorithm accurately model urban background radiation. This system effectively identifies high radiation zones and tracks temporal changes, crucial for homeland security applications.

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Area of Science:

Environmental Science
Radiation Physics
Sensor Networks

Background:

Urban background radiation assessment is vital for homeland security.
Spatial-temporal fluctuations in radiation are challenging to model due to environmental factors.
Existing methods struggle to accurately capture dynamic background radiation patterns.

Purpose of the Study:

To develop an efficient system for modeling urban background radiation.
To accurately assess both spatial distribution and temporal fluctuations of background radiation.
To enhance homeland security through improved radiation monitoring.

Main Methods:

Deployment of a mobile sensor network for continuous background radiation monitoring.
Development and application of a maximum likelihood estimation algorithm.
Decoupling and estimation of spatial and temporal radiation components.

Main Results:

The system accurately identified regions with elevated background radiation.
Temporal fluctuation trends, including those during rainfall, were successfully captured.
Demonstrated the system's efficacy in real-world urban environments.

Conclusions:

The developed mobile sensor network and algorithm provide an effective solution for background radiation modeling.
This approach enhances the ability to monitor and understand dynamic radiation environments.
The system offers significant potential for improving homeland security strategies.