GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques
نویسندگان
چکیده
منابع مشابه
GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques
Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR) in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on machine learning techniques is also presented with the aim of automatically detecting under...
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According to the United Nations, as of the year 2000 there were 70 million landmines planted in a third of the world’s nations affecting global causality rate of up to 20,000/year, (Anderson, 2002). That is why landmine detection has attracted much attention by many research teams around the world during the last two decades; among them is our research team in Nagoya University. Anti-personnel ...
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The land mine crisis is all over frightening since there are presently 500 million unexploded, buried mines in about 70 countries. Governments are noticing this situation seriously since land mines are claiming the limbs and lives of civilians’ very day. A multiple of landmine extraction from the data which are obtained from the Ground Penetrating Radar (GPR). Traditional algorithms targets on ...
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Ground Penetrating Radar (GPR) is a promising sensor for landmine detection, however there are two major problems to overcome. One is the rough ground surface. The other problem is the distance between the antennas of GPR. It remains irremovable clutters on a sub-surface image output from GPR by first problem. Geography adaptive scanning is useful to image objects beneath rough ground surface. ...
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Machine learning of ECG is a core component in any of the ECG-based healthcare informatics system. Since the ECG is a nonlinear signal, the subtle changes in its amplitude and duration are not well manifested in time and frequency domains. Therefore, in this chapter, we introduce a machine-learning approach to screen arrhythmia from normal sinus rhythm from the ECG. The methodology consists of ...
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2014
ISSN: 2072-4292
DOI: 10.3390/rs6109729