BioLayer Interferometry (BLI) is an analytical technique utilized for measuring binding interactions between biomolecules. This is a label-free technology that measures the change in white light signal as molecules, proteins, antibodies, or other ligands attach to the end of an optical sensor tip throughout an assay in real time. BLI is advantageous in detection because the sample preparation is free of tagging or other manipulations, samples can be analyzed without purification, and the sample is fully recoverable. BLI is commonly used to measure affinity between two or more molecules as a characterization technique, high throughput screening of molecules for bioactivity, and quantitation of molecules in reaction mixtures or cell lysates. We utilized BLI as a yes/no detection platform to replace prolonged immunoassays for biothreat surrogate detection and developed an accompanying data analysis tool to automate the decision-making process for inexperienced users. Proprietary anti-mouse capture biosensors bound to two separate monoclonal antibodies, which then bound varying concentrations of protein to measure the on- and off-rates of the protein to the antibody at equilibrium. Software fitting reports kinetics values that were used to develop a secondary screening tool to determine a true binding event over a false positive or nonspecific interaction between binding partners. Over 1300 binding curves were generated to define the parameters of this screening tool, resulting in high confidence in the yes/no decision process. Implementation of this tool reduces the expertise needed for biothreat detection in the field or a high-throughput screening scenario, while also reducing the time needed from sample receipt to answer 2-fold over ELISA or MAGPIX immunoassays.
Standoff detection of chemicals remains a crucial need for a variety of applications of importance for defense, homeland security, environmental, and industrial applications. The goal of standoff chemical sensing is to enable the identification and classification of an unknown hazardous or toxic chemical, without any operator or instrument having to come in direct contact with the chemical itself. Currently, standoff detection of chemical vapors is carried out using optical sensing techniques. Passive infrared (IR) sensors have identified chemical vapor clouds at ranges exceeding one kilometer by detecting, spectrally resolving, and analyzing scene radiance. Currently available passive IR sensors have substantial size, weight, power, and cost (SWaP-C) limitations, which reduce the number of sensors capable of being deployed in a given area, or precludes their use altogether in certain circumstances. To address these limitations, we are developing a unique passive low SWaP-C IR sensor capable of detecting chemical vapors when viewed against a cold-sky or terrestrial background. This sensor, inspired by human color vision, will use only the response through three broadband infrared optical filters to discriminate between target chemicals and background interferents. The key technology of the PBS is a commercially available pyroelectric quadrant chip sensor which contains four channels with unique bandpass IR filters installed. We demonstrate results collected using a variable temperature blackbody in the laboratory, which represents passive IR sensing against various background conditions. These results demonstrate the first step in the development of a passive bioinspired IR sensor which will use only low-cost commercially available components, and be capable of rapidly providing actionable detection of chemical vapor clouds
Chemical Biological Radiological Nuclear and Explosive (CBRNE) sensing systems in the field provide alarms in the form of simple graphical representations, lights, vibrations, and alarm sounds to maximize the reaction time of the user in the event of a hazardous situation. Artificial Intelligence (AI) can be used to reduce the false alarms of chemical detectors, allowing users to react with confidence when an alarm does occurs. However, the Department of Defense’s AI ethics standards states that technologies incorporating AI systems be traceable, reliable, and governable. Given the complex nature of AI and the difficulties of interpreting results, testing and evaluating AI systems poses a challenge for CBRNE sensing systems. To properly interpret and evaluate AI systems it is imperative graphical user interfaces (GUI) are designed to be simple interfaces that provide easy to interpret results. Presented here is an interpretable alarm GUI for orthogonal networked sensors (IAGOnet). IAGOnet provides real-time status of connected sensors utilizing a familiar replication of their onboard results, along with simple to understand graphical representations of confidence metrics from machine learning (ML) predictions. IAGOnet allows a user to compare the detector’s original alarm state to current and previous predictions of classification algorithms, thereby reducing the false alarms. Our work demonstrates the practical nature of IAGOnet by utilizing data from an ion mobility spectrometry (IMS) based detector and a multi-gas detector.
It is of vital interest to understand how cloud particles interact with ambient atmospheric radiation fields. We developed
a comprehensive analytical radiative transfer model for passive infrared remote sensing applicable to ground-based and
airborne sensors. We show the qualitative difference between simple non-scattering aerosols (pseudo vapor cloud) and
an aerosol cloud where scattering, absorption and emission occur. Simulations revealed two interesting observations:
aerosol cloud detection from an airborne platform may be more challenging than for a ground-based sensor, and the
detection of an aerosol cloud in emission mode is different from the detection of an aerosol cloud in absorption mode.
Performance of the matched filter and anomaly detection algorithms relies on the quality of the inverse sample
covariance matrix, which depends on sample size (number of vectors). The "RMB rule" provides the number of vectors
required to achieve a specific average performance loss of the matched filter. In this paper we extend the RMB rule to
provide the number of vectors needed to ensure a minimum performance loss (within a certain confidence). We also
review a general metric for covariance estimation accuracy based on the Wishart distribution and discuss anomaly
detector performance loss.
Hyperspectral imagery is often visualized as a three-dimensional image cube (two spatial dimensions and one spectral). When a hyperspectral sensor is set to stare at a fixed location a fourth dimension (time) is created as each new cube is sampled in time. In a ground-based stare-mode geometry each new cube has near perfect spatial registration with the previous data cubes. The problem with standard spectral-only hyperspectral detection algorithms is that they do not make effective use of temporal information. In this paper we combine temporal-differencing with temporal-spectral detection algorithms. The temporal-differencing allows for removal of most of the background prior to temporal-spectral detection. The temporal-spectral approaches combine temporal information with standard spectral-only statistical methods. By combining temporal-differencing with temporal-spectral information we are able to significantly improve detector performance and reduce the false alarm rate. We demonstrate the performance of these methods using data from the FIRST (Field-Portable Imaging Radiometric Spectrometer Technology). All the computer simulations and field data experiments show that temporal-differencing improves performance, inclusion of temporal-spectral information improves performance, and that the combination of temporal-differencing with temporal-spectral information greatly improves performance.
An algorithm using N-way analysis for the detection of multiple clouds in multi-wavelength lidar data is presented. Nway
analysis is a tool for algebraic manipulation of N-dimensional (ND) data arrays, and it allows for spatial (range),
temporal (time), and spectral (wavelength) information to be extracted simultaneously from 3D lidar data. The algorithm
tracks the spectral signal strength and location of each of the multiple clouds through time within the lidar measurements
via a method that is shown to be similar to multivariate anomaly detection. The method is data driven and can be applied
to arrays of any number of dimensions (e.g., polarization as the 4th dimension). Results of the algorithm for CO2 lidar
simulations of aerosol clouds are shown and discussed.
Recent experimental work has shown that passive systems such as hyperspectral FTIR and frequency-tunable IR cameras have application in detection of biological aerosols. This provided the motivation for a new detection technique, which we call Aerosol Ranging Spectroscopy (ARS), whereby a scattering LIDAR is used to augment passive spectrometer data to determine the location and optical depth of the aerosol plume. When the two systems are co-aligned or boresighted, the hybrid data product provides valuable enhancements for signal exploitation of the passive spectral data. This paper presents the motivation and theoretical basis for the ARS technique. A prototype implementation of an ARS system will also be described, along with preliminary results from recent outdoor field experiments.
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