Trace particle quantities of explosives left behind by those handling explosives materials present an opportunity to identify both the handlers, secondary handlers and the objects they have contacted. Understanding the nature of these particles is critical for tailoring optical detection strategies as well as non-optical contact harvesting methods. We are working towards developing a model to understand and quantify the nature of particles transferred from the hands to different substrate surfaces. In this preliminary paper we report on a newly developed finger test-bed to produce a robotically controlled series of fingerprints, with an artificial finger designed to mimic the physicochemical properties of the human finger. In an initial set of experiments, we examine the effect of a range of applied forces, the effect of a range of initial particle sizes, and the serial print number on the deposited mass and deposited particle sizes, for a surrogate explosive loaded as particles on gloved fingers which are subsequently pressed against a set of clean glass slides.
Machine learning based perception algorithms are increasingly being used for the development of autonomous navigation systems of self-driving vehicles. These vehicles are mainly designed to operate on structured roads or lanes and the ML algorithms are primarily used for functionalities such as object tracking, lane detection and semantic understanding. On the other hand, Autonomous/ Unmanned Ground Vehicles (UGV) being developed for military applications need to operate in unstructured, combat environment including diverse off-road terrain, inclement weather conditions, water hazards, GPS denied environment, smoke etc. Therefore, the perception algorithm requirements are different and have to be robust enough to account for several diverse terrain conditions and degradations in visual environment. In this paper, we present military-relevant requirements and challenges for scene perception that are not met by current state-of-the-art algorithms, and discuss potential strategies to address these capability gaps. We also present a survey of ML algorithms and datasets that could be employed to support maneuver of autonomous systems in complex terrains, focusing on techniques for (1) distributed scene perception using heterogeneous platforms, (2) computation in resource constrained environment (3) object detection in degraded visual imagery.
Trace quantities of explosives left behind by those handling explosives materials present an opportunity to identify both the handlers and the objects they have contaminated. Understanding the evolution of these particles is critical for tailoring detection strategies of optical techniques as well as non-optical contact harvesting methods. We are working towards a complete particle persistence model that captures the contribution of environmental factors such as temperature, airflow, and humidity as well as physical factors such as vapor pressure, particle size and inter-particle spacing to predict particle lifetimes for explosives and other chemicals. Our approach involves depositing particles onto glass substrates using particle sizes and loadings known to be deposited by fingerprint deposition, and then studying their behavior in a custom flow cell with controlled airflow, humidity and temperature. Optical microscope images of the sample taken at fixed time intervals are analyzed to monitor particle sublimation, and those images used to determine the mass loss as a function of time. The data are then fit to a model and from the fitting constants the sublimation rate is calculated. We find that the measured sublimation rate exhibits the expected dependence on vapor pressure for a given material, and that the dependence on vapor pressure is largely material independent. We focus on the behavior of a model material, 2,4-dinitrotoluene and select explosive materials under controlled conditions. We are able to use the data from 2,4-dinitrotoluene to predict the behavior of 2,4,6-trinitrotoluene using the physical properties (e.g., vapor pressure) of the respective materials and compare it to experimental results.
We are developing a stand-off technique for the detection of trace amounts of explosive materials. The motivation behind this work is to prevent loss of life and injury to military and civilian personal by detecting threats at distance. The matured technique will allow for the facile identification of possible threats with minimum user effort and enough time to take appropriate action. This manuscript illustrates the results from our infrared backscatter imaging spectroscopy mobile stand-off method to detect trace amounts of explosive materials under laboratory conditions. The described technique uses tunable quantum cascade lasers, with full spectral coverage from 6-11 μm, to illuminate a target and an infrared focal plane array to collect the backscattered signal into hyperspectral images cubes. The quantum cascade lasers are operated under eye safe levels which allows for safe and stealthy probing of objects, vehicles, and even people. Experiments are performed on tilted substrates to simulate real world conditions where it is unlikely to collect the specular reflections. The collected hyperspectral image cubes contains spectral, spatial, and temporal information that can be fed to a detection algorithm.
We are developing a cart-mounted platform for standoff chemical detection technologies based on active broadband infrared imaging spectroscopy. This approach leverages IR quantum cascade lasers, tuned through signature absorption bands (6-11 microns) of the target analytes while illuminating a surface area of interest. An IR focal plane array captures the time-dependent surface response. The image stream forms a hyperspectral image cube comprised of spatial and spectral dimensions as vectors within a detection algorithm. Our current emphasis is on rapid screening. We present the results of recent adaptations of the platform for infrared backscatter imaging spectroscopy (IBIS). Using the mobile platform, we demonstrate standoff detection of trace analytes deposited by sieving onto substrates. We have previously demonstrated standoff trace detection at several meters indoors and in field tests, while operating the lasers below the eye-safe intensity limit (100 mW/cm2). Sensitivity to explosive traces as small as a single grain (~1 ng) has been demonstrated.
A significant remaining challenge in chemical detection is the ability to rapidly detect with high fidelity a full suite of CWAs and TICs in a single point-detection system. Gas chromatography (GC) is a proven laboratory technique that can achieve the stated detection goal, but not at the required speed and not in a wearable (or even portable) form factor. Efforts in miniaturizing GCs yielded small devices, but they remain slow as they retain the end-of-column detection paradigm which results in long elution times of CWAs and TICs. We describe a novel concept of in-column detection by probing the sorbent coating (stationary phase) of a micro-GC column through optical evanescent field interactions in the long-wave infrared (“chemical fingerprint”) spectral region (U.S. Patent US9599567B2). Detection closer to the injection port ensures a rapid response for slow-eluting analytes. Although this results in poor separation (i.e. poor ability to identify chemicals), this is more than compensated by having full IR absorbance spectra at each location. This orthogonal spectral signature (along with GC retention times) is used in a powerful algorithm to quickly identify components in a complex mixture under conditions of incomplete separation. We present results with an ATR-based system that uses a focused tunable quantum cascade laser beam directed by galvo mirrors at points along a molded micro-GC column whose bottom wall is the sorbent coated ATR prism. Efforts are under way to further miniaturize this device by employing novel long-wave-IR photonic waveguides for a truly portable integrated photonic chromatographic detector of CBRNE threats.
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