Thierry Dubroca

Quasiferromagnetism in semiconductors

Ferromagnetic hysteresis has been observed at room temperature in materials not consisting of elements commonly associated with ferromagnetism, such as Co, Ni, Fe, or Mn-containing alloys. In particular, we report on magnetic hysteresis seen in silicon prepared by two different techniques: ion implantation (Si and Ar) and neutron irradiation. Because the material investigated contains no ferromagnetic elements, we tentatively call it “quasiferromagnetic.” The paramagnetic defects present in these materials were investigated using electron paramagnetic resonance. We suggest that these defects are one of the factors responsible for the observed macroscopic magnetic hysteresis loop.

Detection of explosives by differential hyperspectral imaging

Abstract. Our team has pioneered an explosives detection technique based on hyperspectral imaging of surfaces. Briefly, differential reflectometry (DR) shines ultraviolet (UV) and blue light on two close-by areas on a surface (for example, a piece of luggage on a moving conveyer belt). Upon reflection, the light is collected with a spectrometer combined with a charge coupled device (CCD) camera. A computer processes the data and produces in turn differential reflection spectra taken from these two adjacent areas on the surface. This differential technique is highly sensitive and provides spectroscopic data of materials, particularly of explosives. As an example, 2,4,6-trinitrotoluene displays strong and distinct features in differential reflectograms near 420 and 250 nm, that is, in the near-UV region. Similar, but distinctly different features are observed for other explosives. Finally, a custom algorithm classifies the collected spectral data and outputs an acoustic signal if a threat is detected. This paper presents the complete DR hyperspectral imager which we have designed and built from the hardware to the software, complete with an analysis of the device specifications.

Ultra-violet and visible absorption characterization of explosives by differential reflectometry.

This study presents some optical properties of TNT (2,4,6-trinitrotoluene), RDX, HMX and tetryl, specifically their absorption spectra as a function of concentration in various solvents in the ultraviolet and visible portion of the electromagnetic spectrum. We utilize a standoff explosives detection method, called differential reflectometry (DR). TNT was diluted in six different solvents (acetone, acetonitrile, ethanol, ethyl acetate, methanol, and toluene), which allowed for a direct comparison of absorption features over a wide range of concentrations. A line-shape analysis was adopted with great accuracy (R(2)>0.99) to model the absorption features of TNT in differential reflectivity spectra. We observed a blue shift in the pertinent absorption band with decreasing TNT concentration for all solvents. Moreover, using this technique, it was found that for all utilized solvents the concentration of TNT as well as of RDX, HMX, and tetryl, measured as a function of the transition wavelength of the ultra-violet absorption edge in differential reflectivity spectra shows three distinct regions. A model is presented to explain this behavior which is based on intermolecular hydrogen bonding of explosives molecules with themselves (or lack thereof) at different concentrations. Other intermolecular forces such as dipole-dipole interactions, London dispersion forces and π-stacking contribute to slight variations in the resulting spectra, which were determined to be rather insignificant in comparison to hydrogen bonding. The results are aimed towards a better understanding of the DR spectra of explosives energetic materials.

Sub-pixel target spectra estimation and detection using functions of multiple instances

The Functions of Multiple Instances (FUMI) method for learning target pattern and non-target patterns is introduced and extended. The FUMI method differs significantly from traditional supervised learning algorithms because only functions of target patterns are available. Moreover, these functions are likely to involve other non-target patterns. In this paper, data points which are convex combinations of a target prototype and several non-target prototypes are considered. The Convex-FUMI (C-FUMI) method learns the target and non-target patterns, the number of non-target patterns, and the weights (or proportions) of all the prototypes for each data point. For hyperspectral image analysis, the target and non-target prototypes estimated using C-FUMI are the end-members for the target material and non-target (background) materials. For this method, training data need only binary labels indicating whether a data point contains or does not contain some proportion of the target endmember; the specific target proportions for the training data are not needed. In this paper, the C-FUMI algorithm is extended to incorporate weights for training data such that target and non-target training data sets are balanced (resulting in the Weighted C-FUMI algorithm). After learning the target prototype using the binary-labeled training data, target detection is performed on test data. Results showing sub-pixel explosives detection and sub-pixel target detection on simulated data are presented.

An automatic detection software for differential reflection spectroscopy

Recent terrorist attacks have sprung a need for a large scale explosive detector. Our group has developed differential reflection spectroscopy which can detect explosive residue on surfaces such as parcel, cargo and luggage. In short, broad band ultra-violet and visible light is shone onto a material (such as a parcel) moving on a conveyor belt. Upon reflection off the surface, the light intensity is recorded with a spectrograph (spectrometer in combination with a CCD camera). This reflected light intensity is then subtracted and normalized with the next data point collected, resulting in differential reflection spectra in the 200-500 nm range. Explosives show spectral finger-prints at specific wavelengths, for example, the spectrum of 2,4,6, trinitrotoluene (TNT) shows an absorption edge at 420 nm. Additionally, we have developed an automated software which detects the characteristic features of explosives. One of the biggest challenges for the algorithm is to reach a practical limit of detection. In this study, we introduce our automatic detection software which is a combination of principal component analysis and support vector machines. Finally we present the sensitivity and selectivity response of our algorithm as a function of the amount of explosive detected on a given surface.