Application of Neural Network Enhanced Ground-Penetrating Radar to Localization of Burial Sites

@article{Mazurkiewicz2016ApplicationON,
  title={Application of Neural Network Enhanced Ground-Penetrating Radar to Localization of Burial Sites},
  author={Ewelina Mazurkiewicz and Ryszard Tadeusiewicz and Sylwia Tomecka-Suchoń},
  journal={Applied Artificial Intelligence},
  year={2016},
  volume={30},
  pages={844 - 860}
}
Abstract The problem of searching for burial sites is an important issue for criminology, history, and archeology. The presently employed classical ground-penetrating radar (GPR) methods often yield equivocal results. Here, we report the results of our experimental study on the possible enhancement of the GPR methodology by introduction of the neural network to help localize the places of deposition of cadavers. The experiments, employing pig bodies as a model, yielded very promising results… 
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Correction to: Ground penetrating radar use in flood prevention
The original version of this article unfortunately was not the last version of this article. The updated version is given below.
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References

SHOWING 1-10 OF 11 REFERENCES
Ground-Penetrating Radar Techniques to Discover and Map Historic Graves
Ground-penetrating radar is a geophysical technique that can be used to identify and map features commonly associated with historic graves, including intact or partially collapsed coffins and
Two-Step Inverse Problem Algorithm for Ground Penetrating Radar Technique
The aim of the article is to present the new method of GPR data interpretation. The presented methodology allows to determine the depths and diameters of hidden objects. To generate the data and to
Generalized Hough Transform and ANN for subsurface cylindrical object location and parameters inversion from GPR data
Targets location and parameter inversion are always active research field of Ground Penetrating Radar (GPR) and useful to address some challenges in civil and military applications. Since the
Neural Networks as a Tool for Georadar Data Processing
TLDR
A new neural network based method for automatic classification of ground penetrating radar (GPR) traces by polynomials approximation is proposed and it is shown that the classifier can effectively distinguish sinkholes from other geologic structures.
Geotechnical analysis and 4D GPR measurements for the assessment of the risk of sinkholes occurring in a Polish mining area
The study presented in this paper concerns georadar investigations at a selected former coal mining site in Upper Silesia (Poland) where the risk of sinkhole appearance is high. The results of 3D GPR
The use of forensic botany and geology in war crimes investigations in NE Bosnia.
  • A. G. Brown
  • Environmental Science
    Forensic science international
  • 2006
Forensic Recovery of Human Remains: Archaeological Approaches
TLDR
This chapter discusses Forensic Anthropologists and Forensic Archaeologists, as well as their roles in crime scene investigation, and some of the aspects of their work.
Exploring Neural Networks with C
TLDR
Exploring Neural Networks with C# presents the important properties of neural networks while keeping the complex mathematics to a minimum and presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.
Forensic recovery of human remains
  • Archeological approaches, 57. London, UK: Taylor & Francis Group.
  • 2006
...
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