External-cavity quantum cascade lasers (EC-QCL) are now established as versatile wavelength-tunable light sources for analytical spectroscopy in the mid-infrared (MIR) spectral range. We report on the realization of rapid broadband spectral tuning with kHz scan rates by combining a QCL chip with a broad gain spectrum and a resonantly driven micro-opto-electro-mechanical (MOEMS) scanner with an integrated diffraction grating in Littrow configuration. The capability for real-time spectroscopic sensing based on MOEMS EC-QCLs is demonstrated by transmission measurements performed on polystyrene reference absorber sheets, as well as on hazardous substances, such as explosives. Furthermore, different applications for the EC-QCL technology in spectroscopic sensing are presented. These include the fields of process analysis with on-or even inline capability and imaging backscattering spectroscopy for contactless identification of solid and liquid contaminations on surfaces. Recent progress in trace detection of explosives and related precursors in relevant environments as well as advances in food quality monitoring by discriminating fresh and mold contaminated peanuts based on their MIR backscattering spectrum is shown.Keywords: quantum cascade lasers; external cavity quantum cascade lasers; MOEMS grating; quantum cascade laser based spectroscopy; imaging laser backscattering spectroscopy; inline spectroscopic analysis
This paper presents an online multimodal person independent workload classification system using blood volume pressure, respiration measures, electrodermal activity and electroencephalography. For each modality a classifier based on linear discriminant analysis is trained. The classification results obtained on short data frames are fused using weighted majority voting. The system was trained and evaluated on a large training corpus of 152 participants, exposed to controlled and uncontrolled scenarios for inducing workload, including a driving task conducted in a realistic driving simulator. Using person dependent feature space normalization, we achieve a classification accuracy of up to 94% for discrimination of relaxed state vs. high workload.
In this work we demonstrate imaging standoff detection of solid traces of explosives using infrared laser backscattering spectroscopy. Our system relies on active laser illumination in the 7 µm-10 µm spectral range at fully eye-safe power levels. This spectral region comprises many characteristic absorption features of common explosives, and the atmospheric transmission is sufficiently high for stand-off detection. The key component of our system is an external cavity quantum cascade laser with a tuning range of 300 cm(-1) that enables us to scan the illumination wavelength over several of the characteristic spectral features of a large number of different explosives using a single source. We employ advanced hyperspectral image analysis to obtain fully automated detection and identification of the target substances even on substrates that interfere with the fingerprint spectrum of the explosive to be detected due to their own wavelength-dependent scattering contributions to the measured backscattering spectrum. Only the pure target spectra of the explosives have to be provided to the detection routine that nevertheless accomplishes reliable background suppression without any a-priory-information about the substrate
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