Performing standoff detection tests of aerosol clouds is complicated by the level of effort to set up for such experiments and uncontrolled ambient airflows. A more controlled approach is desirable for early stage experiments and for infield validation tests or calibration purposes. Here we describe the development of a static panel coated with the particle constituents of an aerosol cloud collapsed into a two-dimensional rendering. Providing the support panel is sufficiently optically benign at relevant wavelengths for the target aerosol particles and the particles are separated by sufficient distances to interact with infrared light independently from one another suitable test panels maybe be fabricated. Here we elaborate on our two-dimensional static aerosol panel concept and discuss the choice of infrared benign support, how to deposit aerosol particles over a relatively large footprint up to one-meter square and the optical characterization of test panels produced.
KEYWORDS: Stochastic processes, Hyperspectral imaging, Matrices, Spectroscopy, Chemical analysis, Data analysis, Signal to noise ratio, Statistical analysis, Mixtures, Infrared spectroscopy
The next generation of infrared spectroscopic solutions collect a massive amount of data that is realistically much too dense to be intuitively understood by a human. Thus, as a practical necessity, the user is generally interested in a smaller number of “latent” variables that aren’t directly observed. However, the usual method of considering a more manageable subset of the raw data throws away a great deal of collected information. The problem of distilling the latent variables and related uncertainties from the raw data is one of statistical inference. We adopt a Bayesian approach to better quantify the uncertainties in the latent variables. While our prior work has focused on exact inference methods such as Gibbs Sampling and Hamiltonian Monte-Carlo, we have begun exploring techniques for approximate inference, which allow such data analysis to proceed in quasi real-time.
In support of the detection of explosives and threat chemicals by active infrared backscatter hyperspectral imaging, we are training algorithms to process and alert on possible threats. Surfaces are interrogated using infrared quantum cascade lasers (QCL) and the backscattered signal is collected using a cooled MCT focal plane array (FPA). The QCLs can tune across their full wavelength range, from 6 – 11 m, in less than one second. Full 128 X 128 pixel frames from the FPA are collected and compiled into a hyperspectral image (HSI) cube containing spectral and spatial information from the target. The HSI cubes are processed and the spectra from extracted pixel locations are then run through an algorithm to detect and identify traces of explosives. We train our algorithms on both synthetic and experimental data. In this presentation, we utilize machine learning algorithms to classify HSI cubes from a series of targets coupons fabricated on relevant substrates (glass, painted metal, plastics, cardboard). We explain how the algorithm training uses reference spectral measurements from our cart system as well as from a benchtop FTIR. The generation and utility of synthetic data is described regarding how we populate the algorithms’ spectral library more densely than would be possible using only measured experimental data. The performance of several ML algorithms is described.
We seek to detect and classify chemical threats based on their infrared spectra. Specifically, we are interested in utilizing spectral signatures observed with standoff technologies that interrogate analyte micro-particles on relevant substrate surfaces such as glass, metal and plastics. In this work, we have applied six Machine Learning algorithms to classify analytes based on their infrared spectra. Two synthetic datasets were used, the first one containing 40 analytes and the second one containing 55 analytes. In both datasets the analytes were synthetically placed onto 9 substrates. The 40 analytes dataset contains 18,000 spectra, 450 for each analyte with mass loading varying from 1 to 50 μg/cm2. The 55 analytes dataset consists of 49,500 spectra, 900 for each analyte and mass loadings in the range 1 to 100 μg/cm2. Two of the algorithms used in this work are coming from the statistical field; k nearest neighbors (k-NN) and Logistic Regression. The Support Vector Machine algorithm was developed by the Machine Learning community. Multilayer Perceptrons (MLP) as well as Convolutional Neural Networks are considered Deep Learning Algorithms. In addition to that, we have considered the hybrid deep learning algorithm one dimensional CNN-LSTM. Our experimental results lead us to the conclusion that k-NN and logistic regression outperform deep learning algorithms for our synthetic data sets. However, after dimensionality reduction using PCA, the accuracy of k-NN decreases and the performance of deep learning algorithms improves. We also considered the effect of mass loadings and added noise on the performance of the classifiers.
KEYWORDS: Spectroscopy, Signal to noise ratio, Infrared radiation, Data modeling, Systems modeling, Chemical analysis, Line scan image sensors, Infrared imaging, Principal component analysis, Statistical analysis
The next generation of infrared spectroscopic solutions collect massive amounts of data that is realistically much too dense to be understood by a human. Thus, as a practical necessity, the user is generally interested in a smaller number of “critical” variables that aren’t directly observed. However, considering a more manageable subset of the raw data throws away a great deal of collected information. The problem of distilling the critical variables and related uncertainties from the raw data is one of statistical inference. We adopt a Bayesian approach to better quantify the uncertainties in the critical variables. This approach, when paired with an appropriate model of the hardware and the system being observed, can greatly improve the effective signal to noise and/or reduce the required measurement time.
This report describes inverse spectral analysis of diffuse-reflectance spectra measured using Infrared Backscatter Imaging spectroscopy (IBIS). In IBIS, a tunable infrared laser illuminates a target while an infrared camera detects the backscatter. Target analytes are identified by analyzing the pattern of absorption dips in the detected backscatter and comparing them to the known or simulated reflectance spectra of hazardous materials. The backscatter spectrum is comparable to diffuse reflectance measured using a Fourier transform infrared (FTIR) spectrometer. The analysis methodology applied here entails iterative adjustment of spectra using phenomenological backgrounds. and estimation of absorbance using the Kubelka-Munk (KM) theory of diffuse reflectance. Applying spectrum-feature enhancement, measured with a field spectrometer, can provide a better estimation of dielectric response, which is for comparison to reference dielectric functions, for identification of target materials.
We are developing a vapor detection platform that combines the mixture separation power of gas chromatography with the identification power of infrared spectroscopy. The gas chromatography column is implemented using a meandering channel (imprinted in a molded lid) with the bottom of the channel consisting of a germanium wafer where an infrared laser is introduced in order to perform ATR IR spectroscopy along several segments of the column. We overcome the relative insensitivity of the ATR method by exploiting multiple bounces along the ATR wafer, as well as coating the wafer with a thin film of sorbent which adsorbs and concentrates the analyte in the evanescent region at the surface of the wafer. A unique advantage of our technique is that we collect signal from multiple points along the GC column (including the beginning) for rapid response. This is a major milestone towards implementing a complete micro-gas chromatography sensor for rapid analysis of complex chemical mixtures. Such technology is very attractive for early warning applications in defense and environmental monitoring.
We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The machine learning algorithm utilizes a 1-dimensional convolutional neural network (CNN) that has been trained on augmented FTIR diffuse reflectance spectra. In this manuscript, we implement a 1-D CNN to identify chemicals within an IBIS hypercube. This demonstrates a form of active chemical imaging where the CNN identifies a chemical within each pixel of an IBIS hypercube. Chemical imaging capability goes beyond point detection and identification to indicate where each chemical is within the field of view, as well as identifying multiple target chemicals simultaneously.
A numerical-analytical model and simulations are described concerning diffuse reflectance for surface-distributed material particles on substrates. The model combines an analytical formulation of Mie-scattering theory and a numerical procedure based on Kramers-Kronig analysis. The results of simulations using this model are compared with experimental measurements of diffuse reflectance for material particles distributed on a glass surface. The purpose of these comparisons is estimating the influence of background contributions to spectral features due to resonant scattering from finite-size particles. Evaluating the sensitivity of spectrum-feature extraction methodologies with respect to these background contributions is significant for practial detection of target materials.
We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The machine learning algorithm utilizes a convolutional neural network (CNN) that has been trained on synthetic diffuse reflectance spectra. In this manuscript, we utilize a CNN to identify chemicals within an IBIS hypercube. We demonstrate a form of active chemical imaging where the CNN identifies a chemical within each pixel of an IBIS hypercube. Chemical imaging capability goes beyond point detection and identification to indicate where each chemical is within the field of view, as well as identifying multiple target chemicals simultaneously.
We present the further development of a cart-based system for infrared backscatter imaging spectroscopy (IBIS) designed to detect and analyze trace amounts of hazardous materials at proximal stand-off distances. A four-chip quantum cascade laser system quickly scans through the mid- to long-wave infrared (6 µm – 11 µm) wavelength range to illuminate samples contaminated with analyte. The backscattered light from the targets is collected with a liquid nitrogen cooled MCT focal plane array. Wavelengths are assigned to each frame collected with the MCT camera corresponding to the emission of the laser at the time of acquisition. This process builds a hyperspectral image cube containing spectral reflectance data for every pixel in the image. The experimental results of this cart-based infrared illumination and backscatter detection are presented. A single detection event can be completed in less than 1 second, and every pixel of the 128x128 camera array produces an individual spectrum. Advancements in this setup include mitigation of QCL beam wander and differentiating between nine analytes all present within the same one square inch target. Reference spectra of the target analytes are measured using a high resolution FTIR to validate the highly sensitive and chemically specific nature of the IBIS cart-based measurement. The sample was prepared to mimic real-world threats such as explosives and illicit drugs in trace amounts on relevant substrates.
We present further development of an eye-safe, invisible, stand-off technique designed for the detection of target chemicals (such as explosives) in a single “snapshot” frame. Broadband Fabry-Perot quantum cascade lasers (FP-QCLs) are employed as active illumination sources, in the Mid-LWIR (long-wave infrared) in the range of 7 to 12 µm, to interrogate the spectral features from analytes of interest. We have developed a custom-built broadband laser source utilizing an OEM FP-QCL. This “white” broadband laser source enables stand-off detection in a single snapshot frame. Light from this source was collimated and aligned toward the target several meters away. The “backscatter” and absorption signals from target chemicals are spectrally extracted by an LWIR spectrometer based on the spatial heterodyne spectroscopy (SHS) technique. The SHS offers high throughput and full spectral coverage in each single frame from an IR imaging array. This manuscript will cover the implementation and optimization of FP-QCLs for this broadband spectroscopic application. We will also discuss the operation and processing of SHS images to extract spectral information. Finally, we will present results of measurements using specific analytes to demonstrate the application of the method to stand-off detection of targets such as explosives and other chemical threats.
Inverse spectral analysis of diffuse reflectance for surface-distributed material particles on substrates is described. Inverse spectral analysisis applied using a methodology for extraction of target spectral features, which is based on diffuse reflectance theory and phenomenological multiplicative-factor decomposition of reflectance functions. Specifically, this methodology entails feature-extraction using reflectance-spectrum normalization with respect to phenomenological backgrounds. Case-study inverse spectral analyses of diffuse reflectance for surface-distributed caffeine particles on substrates demonstrate the methodology.
In this paper, we discuss the characterization of several quantum cascade lasers (QCL) as candidates for a broadband infrared illumination source for use in single “snapshot” detection of hazardous materials. Each of the lasers discussed is a Fabry-Perot quantum cascade laser (FP-QCL) chosen for its peak emission within the mid- to long-wave infrared region of 7 µm to 12 µm. These lasers are commercially available from several vendors. The output of each laser was characterized using a high resolution FTIR spectrometer to record each laser’s emission spectrum under varying operating conditions such as driving current, QCL temperature, and operating modes (continuous wave or pulsed). Time-Resolved Spectroscopy (TRS) was performed on each laser’s pulsed driven output to provide further details on how the emission of each laser evolves on the nanosecond time scale. We specifically investigate and present spectra of FP-QCL packaged in sealed OEM configurations. These devices offer center wavelengths ranging from 8.9 µm to 10.5 µm. We present the results of changing operating conditions to optimize the QCL emission to provide high-power and broad spectral coverage. By combining two or more FP-QCL, we obtain spectral coverage of approximately 3 µm. The purpose of this study is to develop a high-power, broadband, “white light” illumination source to provide wide spectral coverage over the region of interest for standoff detection and analysis of potentially hazardous materials.
The ability to rapidly detect hazardous airborne chemicals in a complex chemical background with high fidelity remains a significant challenge. Separation through traditional Gas chromatography (GC) can significantly augment most detection technologies for high fidelity detection, but with the disadvantage of requiring the chemicals to elute off the column before detection can occur. This translates to added time for any decision-making process. Microfabrication of GC systems has reduced footprint and power consumption, but the end-of-column detection paradigm has remained. We present the first in-column detection system which probes the GC stationary phase, coated on an IR transparent column substrate, with an active infrared source. The optical evanescent field interacting with the stationary phase (US. Patent# 9,599,567, Navy Case number 211024-US1) allows for detection along the column without having to wait for complete elution. These spectral signatures, collected at different regions along the column, are analyzed by an algorithm to identify components in a complex mixture. We present results with an ATR-based system with a molded micro-GC column whose base comprises an optically transparent material coated with the stationary phase on proof of concept mixtures.
While infrared spectroscopy is often used for chemical vapor identification, it has two major disadvantages: relatively low sensitivity and the inability to reliably identify components in a complex mixture. This paper presents a method that overcomes the low sensitivity challenge. The low-sensitivity is overcome by using a thin germanium wafer in an ATR configuration, providing 10 to 20 times more bounces per centimeter than commercially available multi-bounce ATR crystals. By using a 25 mm long crystal, we can detect nanogram amounts of analyte which is adequate for possible applications in detecting CWA threats, environmental monitoring and medicine (e.g. breath analysis). While not specifically discussed in this paper, the mixture challenge can be overcome by using a “cocktail problem” algorithm which requires multiple spectra with differing concentration ratios of analytes. This is provided by collecting the sampled vapor on either a single spin-coated sorbent film layer or several parallel sorbent strips with differing chemistries to separate the mixture into classes. In both cases, temperature ramping of the sorbent-coated crystal provides additional unique spectral data through boiling point separation. Full gas chromatography column separation is also possible and this is the main topic of a companion paper. In the current experimental setup, we use a tunable quantum cascade laser as the light source and a TE cooled MCT detector.
This study describes inverse spectral analysis of diffuse reflectance for surface-distributed material particles on substrates. In particular, an algorithm for extraction of target spectral features for surface-distributed materials of specified dielectric response. This algorithm is based on diffuse-reflectance theory and linear combinations of basis functions representing response characteristics of different types of scattering processes. The basis functions are constructed using absorbance functions and analytical models of Mie-type scattering. Prototype inverse spectral analysis of diffuse reflectance for surface-distributed explosive particles on substrates are described, which demonstrate characteristics of the algorithm.
This study describes a methodogy for spectrum-feature extraction from diffuse reflectance for distributions of materials on substrates, which is based on diffuse-reflectance theory and phenomenological multiplicative-factor decomposition of reflectance functions. Specifically, this methodology entails feature-extraction using reflectance-spectrum normalization with respect to phenomenological backgrounds. A mathematical analysis of the feature-extraction methodology with respect to its formulation is presented. In addition, results of inverse analyses demonstrating application of the methodology are described.
Rapid scanning quantum cascade lasers are utilized in the detection of trace amounts of explosive materials. Infrared backscatter imaging spectroscopy employs a quick tuning infrared quantum cascade laser system to illuminate targets with mid-IR light, 6 – 11 μm in wavelength, and to perform spectroscopic measurements in less than one second. A narrow cone of the signal backscattered from targets at standoff distance is collected and imaged onto a liquid nitrogen cooled MCT focal plane array. This backscattered signal is processed into a hyperspectral image cube containing spectral and spatial information. The analysis of the experimental data measured with the system is discussed. This includes the processing of the raw camera frames (using signals from individual components of the system) into discrete wavelength bins, typically 0.01 μm in width. Spectra are generated by plotting the signal from regions of interest, typically clusters of adjacent pixels within the frames, as a function of the wavelength associated with the binned frames. These spectra are compared against the FTIR diffuse reflectance of the analytes on an equivalent substrate for identification. Methods to optimize signal to noise and produce identifications with high confidence are presented. For a single experiment, taking less than 1 second, with the camera running at full frame over 16,000 individual spectra are generated. Targets are prepared by sieving and also dry transfer to mimic real world threats, in trace amounts and on relevant substrates. Traces of explosives, as well as illicit drugs are investigated.
We present a cart-based system based on infrared backscatter imaging spectroscopy (IBIS) for detecting and analyzing trace amounts of hazardous materials as particles on solid substrates. A system comprising four quantum cascade lasers rapidly scans through the mid-LWIR (6 μm – 11 μm) wavelength range to illuminate samples containing target analytes. The infrared backscatter signal is collected as a series of images to form a hyperspectral image cube. Each image is collected at a specified excitation wavelength using a liquid nitrogen cooled MCT focal plane array. The experimental results of this cart-based infrared illumination and backscatter detection are presented. Results compare imaged spectra over a range of different wavelength tuning speeds and different combinations of substrates and analytes. Camera frames are collected while the laser is sweeping through its wavelength range. A single complete analysis can be completed in less than 1 second. In every camera frame, each pixel of the 128x128 pixel camera array produces an individual intensity. These frames are then binned and assigned a discrete wavelength in steps, typically 0.01 μm, to produce a spectrum over 6 – 11 μm for each camera pixel. Target samples are prepared by sieving particles or by a dry transfer technique, to mimic particle size distributions associated with real world threats at trace levels, for explosives and illicit drugs on relevant substrates.
We are developing algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures when they are deposited as particles on surfaces. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The algorithm utilizes a convolutional neural network (CNN) that has been pre-trained on synthetic diffuse reflectance spectra. In this manuscript, we present an approach to generate large libraries of synthetic infrared reflectance spectra for use in training and testing the CNN. We demonstrate advancements in the number of analytes, a method to generate synthetic substrate spectra, and the benefits of subtracting the substrate “background” to train and test the CNN on the resulting differential spectra.
We present the development of an eye-safe, invisible, stand-off technique designed for the detection of target chemicals (such as explosives) in a single “snapshot” frame. Broadband Fabry-Perot quantum cascade lasers (FP-QCLs) in the wavelength range of 7 to 12 microns, are directed to a target to interrogate its spectral features. The “backscatter” return signals from target chemicals are spectrally discriminated by an LWIR spatial heterodyne spectrometer (SHS). The SHS offers high throughput and full spectral coverage in each single frame from an IR imaging array. This presentation will cover the performance and optimization of FP-QCLs for this broadband spectroscopic application. We will also discuss the operation and processing of SHS images to extract spectral information. Finally, we will present results of measurements using specific analytes to demonstrate the application of the method to stand-off detection of targets such as explosives and other chemical threats.
We are using active infrared (IR) spectroscopic imaging to detect trace explosives on surfaces at proximal distances up to a few meters. The technology comprises an IR quantum cascade laser (QCL) for illumination and an IR focal plane array (FPA) sensor to collect signal backscattered from surfaces of interest. By sweeping the wavelength of the QCL while collecting image frames with the FPA, we generate an active hyperspectral image (HSI) cube. The HSI cube contains both spatial and spectral information, where the spectrum of a pixel, or region of interest within the image, can be extracted and compared against a known threat library. These cubes are fed into a convolutional neural network (CNN) trained on purely synthetic data to identify chemicals in the field of view. The CNN identifies chemicals by their IR signature and identifies their location within the image.
In this work, we are developing a custom-built broadband laser source in the Mid-LWIR range by combining several high power FP-QCLs for a single snap shot application. To minimize temperature variation or reduce the thermal load while the FP-QCL emits at high currents, the FP-QCL was operated in pulsed mode with varying diode temperatures and applied currents. The spectral outputs in pulsed mode were temporally resolved using a step scan FTIR spectrometer. FP mode peaks typically broaden by driving higher currents. FP mode hopping, emerging, and disappearing were observed during the laser pulse length (3000 ns) at different applied current values. The ideal spectral characteristics for a single snap shot application are discussed, with respect to a broad spectral bandwidth, a flat-top power profile, and high spectral power density.
Gas chromatography (GC) is a staple analytical technique used to separate chemical mixtures (analytes) prior to identification with a hyphenated technique, such as mass spectrometry or Fourier transform infrared (IR) spectroscopy. Traditionally, analytes elute through the GC separation column where they are detected when they exit. We have developed a technique to perform in situ IR spectroscopy during the process of separating the analytes along the GC column. This is achieved by spin coating the stationary phase onto a germanium prism and actively probing the stationary phase in an attenuated total reflectance configuration with a quantum cascade laser.. The GC column is formed by pressing a molded epoxy lid, with grooves that form the tubular column, onto the stationary phase coated prism.
We are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images. The QCL tunes across the full wavelength range from 6 – 11 μm. Full 128 X 128 pixel frames from the FPA are collected at up to 1610 frames per second and comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify traces of explosives. The algorithm utilizes a convolutional neural network (CNN) and has been pre-trained on synthetic diffuse reflectance spectra. In this manuscript, we present backscatter data and hyperspectral image mapping from a car panel substrate deposited with traces of the explosive RDX. We have used a mask to restrict the RDX analyte deposition to small 4 mm diameter areas. The results presented here were measured at 1 meter standoff.
The use of rapid scanning quantum cascade lasers in the detection of trace amounts of explosive materials is presented. This technique, infrared backscatter imaging spectroscopy (IBIS), utilizes an array of quick tuning infrared quantum cascade lasers (QCLs) to illuminate targets with mid-IR light, 6 – 11 μm in wavelength, to perform measurements in less than one second. The backscattered signal from targets is collected with a liquid nitrogen cooled MCT focal plane array. This information is stored in a hyperspectral image cube which is then run through a detection algorithm which has been trained on synthetic reflectance spectra of analytes of interest. We discuss the experimental parameters used with the QCLs and the focal plane array to generate and collect the infrared backscatter signal. The performance of the fast scanning QCL is presented in detail along with the experimental protocol used to collect high quality data from targets at proximal standoff distance. Camera frames are collected as the laser wavelength is swept and then are binned and assigned discrete wavelength steps. Spectra are extracted from the binned frames on a pixel by pixel basis. When run at full frame imaging, this results in over 16,000 individual spectra.
The ability to rapidly detect hazardous airborne chemicals with high fidelity in a single point-detection system remains a significant challenge in a complex chemical background. Traditional Gas chromatography (GC) can significantly augment most detection technologies by separating complex mixtures for high fidelity detection, but with the disadvantage of requiring detection at the end of the GC column which adds a time disadvantage for any decision making process. Microfabrication of GC columns has reduced device footprint and power consumption, but the end-of-column detection paradigm remains. We present a rapid detection concept of in-column detection by probing the GC stationary phase which is coated on an IR transparent column substrate. The optical evanescent field interactions in the mid-infrared spectral region (US. Patent# 9,599,567) allows analyte detection along the column without having to wait for complete elution. These spectral signatures, collected at different points along the column, are analyzed by an algorithm to quickly identify components in a complex mixture. 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 base comprises an optically transparent material coated with the stationary phase.
This study describes parametric modeling of diffuse IR reflectance for sparsely surface-distributed particles of the explosive RDX. Diffuse IR spectra are modeled using a formulation that considers spectral features due to target-material reflectance, i.e., RDX, substrate reflectance and resonance scattering resulting from finite sizes of surface-distributed particles. The results of this study demonstrate an approach for parametric modeling of diffuse IR reflectance for sparsely surface-distributed particles. The mathematical formulation of this approach is that of a phenomenological scattering-matrix representation.
Threat chemicals such as explosives may persist on surfaces, enabling them to be detected by non-contact or standoff optical methods such as diffuse IR reflectance. However, due to particle size effects and optical coupling to the substrate, their IR spectral signatures will differ from laboratory reference measurements of bulk materials. This study presents an inverse analysis of diffuse IR reflectance from sparsely surface-distributed particles of the explosive PETN. A methodology using spectrum templets is applied for inverse analysis of measured spectra. The methodology is based on a generalization of extended multiplicative signal correction (EMSC). The results of this study demonstrate application of the inverse analysis methodology for extraction of spectral features for surface-distributed particles of specified dielectric response.
The results from infrared backscatter imaging spectroscopy on a mobile platform for stand-off detection of trace amounts of explosive materials on relevant substrates are presented. This technique utilizes an array of tunable infrared quantum cascade lasers to illuminate targets. The spectral range of the QCL system spans from 6 - 11 μm, which enables excitation of a wide variety of absorption bands present in analytes of interest. Targets are prepared by sieving particles through a 20 μm mesh onto substrates to simulate relevant qualities (particle size, fill factor, and mass loading) expected of real world targets. The backscatter signal from targets is collected with an IR focal plane array. This information is stored in a hyperspectral image cube to allow for post processing in a detection algorithm. We demonstrate the selectivity and sensitivity of the discussed technique down to the nanogram level for RDX and PETN on glass. Spectra are generated by extracting the signal from small regions of interest to simulate targets with miniscule coverage areas. Preliminary comparison of backscatter data with simulated data from a model that incorporates particle size, mass loading, and substrate response show good agreement. Confusant agents, such as sand, are introduced to the targets loaded with analyte to illustrate the selectivity of this technique. The results of these studies are presented, along with future improvements to the technique.
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.
This study examines using parametric models for inverse analysis of diffuse IR reflectance from particulate materials that are sparsely distributed upon a surface. Parametric models are applied for inverse analysis of simulated spectra, which are calculated using ensembles of reflectance spectra for non-interacting material particles on surfaces, which have specified dielectric response properties and particle-size distributions Simulated reflectance spectra for individual particles upon surfaces, used for prototype inverse analysis, are calculated numerically using a model based on Mie scattering theory, which assumes spherical particles on surfaces. Parametric models of diffuse reflectance spectra provide encoding of dielectric response features for physical interpretation and convenien representation.
We are developing a cart-mounted platform for chemical threat detection and identification based on active LWIR imaging spectroscopy. Infrared backscatter imaging spectroscopy (IBIS) leverages IR quantum cascade lasers, tuned through signature absorption bands (6 - 11 μm) in the analytes while illuminating a surface area of interest. An IR focal plane array captures the time-dependent backscattering surface response. The image stream forms a hyperspectral image cube composed of spatial, spectral and temporal dimensions as feature vectors for detection and identification. Our current emphasis is on rapid screening. This manuscript also describes methods for simulating IBIS data and for training detection algorithms based on convolutional neural networks (CNN). 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. Analytes tested include RDX, PETN, TNT, ammonium nitrate, caffeine and perchlorates on relevant glass, plastic, metal, and painted substrates.
We pursue the development of an eye-safe stand-off technique suitable for the detection of trace explosives. As the active illumination sources, tunable quantum cascade lasers (QCLs) are employed in Mid-LWIR (long-wave infrared) in the range of 6 to 11 μm, which contains many spectral features from analytes of interest. Any fluctuation of the laser beam direction and/or beam profile is amplified at the sample position, which would lead to diminished performance of the detection technique, both in sensitivity and selectivity. Several beam stabilization approaches were conducted to overcome this challenge: 1) Using a KBr/diamond pellet as a diffuser in combination with a multimode fiber 2) Feedback stabilization of quantum cascade laser beam steering. The purpose of the first method is to make a temporally and spatially incoherent laser beam source through the multimode fiber and KBr/diamond pellet. The second approach is to stabilize the beam position by using an active feedback loop. We have demonstrated that beam wander and speckle noise were successfully suppressed by these approaches. Independently, we have developed a custom-built broadband laser source in the Mid-LWIR range consisting of several high power Fabry Perot (FP)-QCLs. The FP-QCLs were operated in both CW and pulsed modes at different diode temperatures, and the emission spectra were collected by a FTIR. For our future work, the output beams will be collimated to spectrally combine multi-QCLs and aligned toward the same target. Also, a spatial heterodyne spectrometer (SHS) will be applied to discriminate spectral and spatial information from a single snapshot.
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.
Due to their high brightness, infrared (IR) lasers (such as tunable quantum cascade lasers, QCLLs) are very attractive illumination sources in both stand-off spectroscopy and micro-spectroscopy. In fact, they are the enabling device for trace-level spectroscopy. However, due to their high coherence as laser beams, QCLLs can cause speckle, especially when illuminating a rough surface. This is highly detrimental to the signal-to-noise ratio (SNR) of thee collected spectra and can easily negate the gains from using aa high brightness source. In most cases, speckle reduction is performed at the expense of optical power. In this paper, we examine several speckle reduction approaches and evaluate them for their ability to reduce speckle contrast while at the same time preserving aa high optical throughput. We analyze multi-mode fibers, integrating spheres, and stationary and moving diffusers for their speckle reduction potential. Speckle-contrast is measured directly by acquiring beam profiles of the illumination beam or, indirectly, by observing speckle formation from illuminating a rough surface (e.g. Infragold® coated surface) with an IR micro-bolometer camera. We also report on a novel speckle-reducing device with increased optical throughput. We characterize speckle contrast reduction from spatial, temporal and wavelength averaging for both CWW and pulsed QCLs. Examples of effect of speckle-reduction on hyperspectral images in both standoff and microscopy configurations are given.
This manuscript describes a mobile stand-off detection and identification of trace amounts of hazardous materials, specifically explosives. The technique utilizes an array of tunable infrared quantum cascade lasers as an illumination source which spans wavelengths from 6 to 11 μm, operated at eye-safe power levels. This spectral range enables excitation of a wide variety of absorption bands present in analytes of interest. The laser is modulated to produce a 50% duty cycle, square wave pulses, and control the frequency of irradiation. The backscatter and photo-thermal signals from samples are measured via an IR focal plane array, which allows for the observation of spatial, temporal, and thermal surface processes. A discussion of how these signals are collected and processed for use in identification of hazardous materials is presented.
We are developing a technology for standoff detection of chemicals on surfaces based on active broadband infrared imaging spectroscopy. This approach leverages one or more IR quantum cascade lasers (QCL), tuned to strong absorption bands in the analytes and directed to illuminate an area on a surface of interest. An IR focal plane array is used to image the surface response upon laser illumination. The broadband IR signal is processed as a hyperspectral image cube comprised of spatial, spectral and temporal dimensions as vectors within a detection algorithm. Such standoff spectroscopic imaging applications place stringent stability requirements on the wavelength, power, pulse width and spatial beam profile that pose a challenge for broadly tunable IR QCL. In this manuscript, we discuss methods to mitigate these challenges, including extensive calibration and active feedback stabilization. These mitigation methods should benefit many applications of IR QCL, including those for standoff detection, spectroscopy and imaging.
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