A platform for building sensor specific machine learning detection algorithms has been developed to classify spectroscopic data. The algorithms are focused on long wave infrared reflectance (LWIR) and Raman spectroscopies. The classification algorithm is based on a one dimensional (1D) convolutional neural network (CNN) architecture. Training data is generated using an appropriate signal model that is combined with sensor specific characteristics such as spectral range, spectral resolution, and noise. Within this paper, the performance of trained CNNs for both LWIR and Raman sensor systems has been evaluated. The evaluation uses both real and synthetic data to benchmark the performance in terms of the discriminant signal. The evaluation data consists of various chemical representations and varied noise levels. The performance of the 1D CNN approach has demonstrated high classification accuracies on data with low discriminant signals. Specifically, the CNNs have demonstrated a classification accuracy <90% for infrared reflectance data down to a wavelength averaged discriminant SNR<1. For Raman systems, we have demonstrated classification accuracies <90% for data with a peak discriminant SNR of approximately 6.
Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Pérot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.
The development of PlumeNet, a thermal imagery based classifier for aerosolized chemical and biological warfare agents, is detailed. PlumeNet is a convolutional neural network designed for the real-time classification of threat-like plumes from background clutter. The model weights were trained from the ground up using thermal imagery of simulant plumes recorded at various test events. The performance between different convolutional neural network architectures are compared. An analysis of the final model layers through activation mapping methods is performed to demystify the methods by which PlumeNet performs classification. The classification performance of PlumeNet at government conducted open-release field testing at Dugway Proving Ground is detailed.
The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.
An electronic detector of surface plasmon polaritons (SPP) is reported. SPPs optically excited on a metal surface using a prism coupler are detected by using a close-coupled metal-oxide-semiconductor capacitor. Semitransparent metal and graphene gates function similarly. We report the dependence of the photoresponse on substrate carrier type, carrier concentration, and back-contact biasing.
Patterned highly absorbing gold black film has been selectively deposited on the active surfaces of a vanadium-oxide-based infrared bolometer array. Patterning by metal lift-off relies on protection of the fragile gold black with an evaporated oxide, which preserves gold black’s near unity absorption. This patterned gold black also survives the dry-etch removal of the sacrificial polyimide used to fabricate the air-bridge bolometers. Infrared responsivity is substantially improved by the gold black coating without significantly increasing noise. The increase in the time constant caused by the additional mass of gold black is a modest 14%.
Optical constants for evaporated bismuth (Bi) films were measured by ellipsometry and compared with those published for single crystal and melt-cast polycrystalline Bi in the wavelength range of 1 to 40 μm. The bulk plasma frequency ωp and high-frequency limit to the permittivity ε∞ were determined from the long-wave portion of the permittivity spectrum, taking previously published values for the relaxation time τ and effective mass m . This part of the complex permittivity spectrum was confirmed by comparing calculated and measured reflectivity spectra in the far-infrared. Properties of surface polaritons (SPs) in the long-wave infrared were calculated to evaluate the potential of Bi for applications in infrared plasmonics. Measured excitation resonances for SPs on Bi lamellar gratings agree well with calculated resonance spectra based on grating geometry and complex permittivity.
We have invented a novel photodetector by mating a surface plasmon resonance coupler with a graphene field effect transistor. The device enables wavelength selectivity for spectral sensing applications. Surface plasmon polaritons (SPPs) are generated in a 50 nm thick Ag film on the surface of a prism in the Kretschmann configuration positioned 500 nm from a graphene FET. Incident photons of a given wavelength excite SPPs at a specific incidence angle. These SPP fields excite a transient current whose amplitude follows the angular resonance spectrum of the SPP absorption feature. Though demonstrated first at visible wavelengths, the approach can be extended far into the infrared. We also demonstrate that the resonant current is strongly modulated by gate bias applied to the FET, providing a clear path towards large-scale spectral imagers with locally addressable pixels.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.