During the last decade, the safety regulations of the airports have been set to a new level. As the number of
passengers is constantly increasing, yet effective but quick security control at checkpoints sets great requirements
to the 21st century security systems. In this paper, we shall introduce a novel metal detector concept that
enables not only to detect but also to classify hidden items, though their orientation and accurate location
are unknown. Our new prototype walk-through metal detector generates mutually orthogonal homogeneous
magnetic fields so that the measured dipole moments allow classification of even the smallest of the items with
high degree of accuracy in real-time. Invariant to different rotations of an object, the classification is based
on eigenvalues of the polarizability tensor that incorporate information about the item (size, shape, orientation
etc.); as a further novelty, we treat the eigenvalues as time series. In our laboratory settings, no assumptions
concerning the typical place, where an item is likely situated, are made. In that case, 90 % of the dangerous and
harmless items, including knives, guns, gun parts, belts etc. according to a security organisation, are correctly
classified. Made misclassifications are explained by too similar electromagnetic properties of the items in question.
The theoretical treatment and simulations are verified via empirical tests conducted using a robotic arm and our
prototype system. In the future, the state-of-the-art system is likely to speed-up the security controls significantly
with improved safety.
The detection and identification of hazardous chemical agents are important problems in the fields of security
and defense. Although the diverse environmental conditions and varying concentrations of the chemical agents
make the problem challenging, the identification system should be able to give early warnings, identify the gas
reliably, and operate with low false alarm rate. We have researched detection and identification of chemical
agents with a swept-field aspiration condenser type ion mobility spectrometry prototype. This paper introduces
an identification system, which consists of a cumulative sum algorithm (CUSUM) -based change detector and
a neural network classifier. As a novelty, the use of CUSUM algorithm allows the gas identification task to
be accomplished using carefully selected measurements. For the identification of hazardous agents we, as a
further novelty, utilize the principal component analysis to transform the swept-field ion mobility spectra into
a more compact and appropriate form. Neural networks have been found to be a reliable method for spectra
categorization in the context of swept-field technology. However, the proposed spectra reduction raises the
accuracy of the neural network classifier and decreases the number of neurons. Finally, we present comparison
to the earlier neural network solution and demonstrate that the percentage of correctly classified sweeps can be
considerably raised by using the CUSUM-based change detector.
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