Narrow Results

Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced thermal emission or absorption after passage through a region containing the CWA cloud. In this work we focus on (a) staring single-pixel sensors that sample their field of view at regular intervals of time to produce a time series of spectra and (b) scanning single or multiple pixel sensors that sample their FOV as they scan. The main objective of signal processing algorithms is to determine if and when a CWA enters the FOV of the sensor.

We shall first develop and evaluate algorithms for staring sensors following two different approaches. First, we will assume that no threat information is available and we design an adaptive anomaly detection algorithm to detect a statistically-significant change in the observed spectrum. The algorithm processes the observed spectra sequentially-in-time, estimates adaptively the background, and checks whether the next spectrum differs significantly from the background based on the Mahalanobis distance or the distance from the background subspace. In the second approach, we will assume that we know the spectral signature of the CWA and develop sequential-in-time adaptive matched filter detectors. In both cases, we assume that the sensor starts its operation before the release of the CWA; otherwise, staring at a nearby CWA-free area is required for background estimation. Experimental evaluation and comparison of the proposed algorithms is accomplished using data from a long-wave infrared (LWIR) Fourier transform spectrometer.

We have further developed widely-tunable monochromatic THz sources. These sources are based on difference-frequency generation (DFG) in GaSe and GaP crystals. Using a 47 mm long GaSe crystal the output wavelength was tuned in the range from 66.5 to 5664 μm (from 150 to 1.77 cm^-1) with the peak powers reaching 389 W. This record-high power corresponds to a conversion efficiency of ~0.1%. On the other hand, using a 20 mm long GaP crystal the output wavelength was tuned in the range 71.1–2830 μm whereas the highest peak power was 15.6 W. The advantage of using GaP over GaSe is obvious: crystal rotation is no longer required for wavelength tuning. Instead, one just needs to tune the wavelength of one mixing beam within the bandwidth of as narrow as 15.3 nm. Most recently, we implemented a new scheme for detecting THz waves based on upconversion at room temperature, i.e. by mixing the THz wave with an infrared laser beam, we observed the upconverted signal at a wavelength just slightly longer than that of the infrared laser. To date the detectable THz power is just an order of magnitude higher than that for a bolometer. This scheme allows us to measure the pulse energy density, wavelength, linewidth, and pulse width of a THz beam at room temperature. Using our widely-tunable monochromatic THz beam, we directly measured the absorption spectra of three different families of the homologues of the chemical vapors.

Resistance noise data from a single gas sensor can be utilized to identify gas mixtures. We calculated the power spectral density. higher order probability densities and the bispectrum function of the recorded noise samples; these functions are sensitive to different natural vapors and can be employed to select a proper detection criterion for gas composites and odors.

Recent progress in spectral fingerprinting of fluorescent indicators using distributed instrumentation based on consumer electronic devices is reviewed. In particular, the evaluation of disposable assays using a computer screen photo-assisted technique (CSPT) is discussed. Sample identification and optimization strategies are analyzed as well as the underlying theoretical background for polychromatic spectral fingerprinting.

Resistance noise data from a single gas sensor can be utilized to identify gas mixtures. We calculated the power spectral density, higher order probability densities and the bispectrum function of the recorded noise samples; these functions are sensitive to different natural vapors and can be employed to select a proper detection criterion for gas composites and odors.

Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced thermal emission or absorption after passage through a region containing the CWA cloud. In this work we focus on (a) staring single-pixel sensors that sample their field of view at regular intervals of time to produce a time series of spectra and (b) scanning single or multiple pixel sensors that sample their FOV as they scan. The main objective of signal processing algorithms is to determine if and when a CWA enters the FOV of the sensor.

We shall first develop and evaluate algorithms for staring sensors following two different approaches. First, we will assume that no threat information is available and we design an adaptive anomaly detection algorithm to detect a statistically-significant change in the observed spectrum. The algorithm processes the observed spectra sequentially-in-time, estimates adaptively the background, and checks whether the next spectrum differs significantly from the background based on the Mahalanobis distance or the distance from the background subspace. In the second approach, we will assume that we know the spectral signature of the CWA and develop sequential-in-time adaptive matched filter detectors. In both cases, we assume that the sensor starts its operation before the release of the CWA; otherwise, staring at a nearby CWA-free area is required for background estimation. Experimental evaluation and comparison of the proposed algorithms is accomplished using data from a long-wave infrared (LWIR) Fourier transform spectrometer.