ABSTRACT
An acoustic spectroscopic approach to detect contents within different packaging, with substantially wider applicability than other currently available subsurface spectroscopies, is presented. A frequency-doubled Nd:YAG (neodymium-doped yttrium aluminum garnet) pulsed laser (13 ns pulse length) operated at 1 Hz was used to generate the sound field of a two-component system at a distance of 50 cm. The acoustic emission was captured using a unidirectional microphone and analyzed in the frequency domain. The focused laser pulse hitting the system, with intensity above that necessary to ablate the irradiated surface, transferred an impulsive force which led the structure to vibrate. Acoustic airborne transients were directly radiated by the vibrating elastic structure of the outer component that excited the surrounding air in contact with. However, under boundary conditions, sound field is modulated by the inner component that modified the dynamical integrity of the system. Thus, the resulting frequency spectra are useful indicators of the concealed content that influences the contributions originating from the wall of the container. High-quality acoustic spectra could be recorded from a gas (air), liquid (water), and solid (sand) placed inside opaque chemical-resistant polypropylene and stainless steel sample containers. Discussion about effects of laser excitation energy and sampling position on the acoustic emission events is reported. Acoustic spectroscopy may complement the other subsurface alternative spectroscopies, severely limited by their inherent optical requirements for numerous detection scenarios.
ABSTRACT
The distance between the sensor and the target is a particularly critical factor for an issue as crucial as explosive residues recognition when a laser-assisted spectroscopic technique operates in a standoff configuration. Particularly for laser ablation, variations in operational range influence the induced plasmas as well as the sensitivity of their ensuing optical emissions, thereby confining the attributes used in sorting methods. Though efficient classification models based on optical emissions gathered under specific conditions have been developed, their successful performance on any variable information is limited. Hence, to test new information by a designed model, data must be acquired under operational conditions totally matching those used during modeling. Otherwise, the new expected scenario needs to be previously modeled. To facing both this restriction and this time-consuming mission, a novel strategy is proposed in this work. On the basis of machine learning methods, the strategy stems from a decision boundary function designed for a defined set of experimental conditions. Next, particular semisupervised models to the envisaged conditions are obtained adaptively on the basis of changes in laser fluence and light emission with variation of the sensor-to-target distance. Hence, the strategy requires only a little prior information, therefore ruling out the tedious and time-consuming process of modeling all the expected distant scenes. Residues of ordinary materials (olive oil, fuel oil, motor oils, gasoline, car wax and hand cream) hardly cause confusion in alerting the presence of an explosive (DNT, TNT, RDX, or PETN) when tested within a range from 30 to 50 m with varying laser irradiance between 8.2 and 1.3 GW cm(-2). With error rates of around 5%, the experimental assessments confirm that this semisupervised model suitably addresses the recognition of organic residues on aluminum surfaces under different operational conditions.
