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The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.
Die Detektion von Explosivstoffen stellt ein zentrales Feld der zivilen Sicherheitsforschung dar. Eine besondere Herausforderung liegt hierbei in dem Nachweis verpackter Substanzen, wie es bei Unkonventionellen Spreng- und Brandvorrichtung (USBV) häufig der Fall ist. Derzeit eingesetzte Verfahren arbeiten meist mit bildgebenden Techniken, durch die sich ein Anfangsverdacht ergibt. Der tatsächliche chemische Inhalt der USBV lässt sich jedoch nicht exakt ermitteln. Eine genaue Beurteilung der Gefährdung durch solche Substanzen ist allerdings von großer Bedeutung, insbesondere wenn die Entschärfung des Objekts in bewohntem Gebiet stattfinden muss. In der vorliegenden Arbeit wird ein Verfahren vorgestellt, das sich als Verifikationsverfahren bei bestehendem Anfangsverdacht gezielt einsetzen lässt. Hierzu wird mittels Laserbohrtechnik zunächst die äußere Hülle des zu untersuchenden Gegenstandes durchdrungen. Anschließend finden eine lasergestützte Probenahme des Inhalts sowie die Detektion unter Verwendung geeigneter Analysemöglichkeiten statt. Der Bohr- und Probenahmefortschritt wird über verschiedene spektroskopische und sensorische Verfahren begleitend überwacht. Zukünftig soll das System abstandsfähig auf Entschärfungsrobotern eingesetzt werden.
In der vorliegenden Arbeit wird ein neuartiges Verfahren zur Echtzeitüberwachung von Laserbohrprozessen vorgestellt. Die Untersuchungen werden an unterschiedlichen Materialien unter Einsatz eines passiv-gütegeschalteten Nd:YAG Lasers durchgeführt. Prozessbegleitend findet eine Aufzeichnung der akustischen Emissionen mit anschließender Analyse durch schnelle Fourier-Transformation statt. Hierdurch lassen sich der Durchbruch beim Bohren durch ein Material sowie der Materialübergang mehrschichtiger Systeme detektieren. Die akustischen Messungen werden durchAuswertung der Pulsfolge des Lasers mittels einer Fotodiode gestützt. Hierbei zeigt sich eine gute Übereinstimmung der im akustischen Spektrum dominanten Frequenz mit der jeweils im Laserburstauftretenden Pulsfrequenz. Das vorgestellte Verfahren ermöglicht eine Echtzeitüberwachung beim Laserbohren mittels kostengünstiger und einfacher Hardware. Zudem zeichnet es sich im Gegensatz zu bestehenden Verfahren durch eine hohe Robustheit gegen äußere Störeinflüsse aus, da eine frequenzbasierte Auswertung stattfindet.
Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalous events in the surveillance area. We describe the concept and the realization of an indoor security assistance system for real-time decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified, and potential threats are localized inside the demonstrator. Data for the person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision formation and the results of the person classification affords the localization of threats and a timely reaction of the security personnel.
Sicherheit im Fährverkehr
(2012)
In the context of the Franco-German research project Re(h)strain, this work focuses on a global system analysis integrating both safety and security analysis of international and/or urban railway stations. The Re(h)strain project focuses on terrorist attacks on high speed train systems and investigates prevention and mitigation measures to reduce the overall vulnerability and strengthen the system resilience. One main criterion regarding public transport issues is the number of passengers. For example, the railway station of Paris “Gare du Nord” deals with a bigger number of passengers than the biggest airport in the world (SNCF open Data 2014), the Atlanta airport, but in terms of passengers, it is only around the 23rd rank railway station in the world. Due to the enormous mass of people, this leads to the system approach of breaking out the station into several classes of zones, e.g. entrance, main hall, quays, trains, etc. All classes are analysed considering state-of-the-art parameters, like targets attractiveness, feasibility of attack, possible damage, possible mitigation and defences. Then, safety incidence of security defence is discussed in order to refine security requirement with regard to the considered zone. Finally, global requirements of security defence correlated to the corresponding class of zones are proposed.
A series of reactive binaphthyl‐diimine‐based dopants is prepared and investigated with respect to their potential for the chiral induction of structural coloration in nematic liquid crystal mixture E7 and the selective photonic sensing of nitrogen dioxide (NO2). Studies of the helical twisting power (HTP) in 4‐cyano‐4′‐pentylbiphenyl (5CB) reveal HTP values as high as 375 µm‐1 and the tremendous impact of structural compatibility and changes of the dihedral binaphthyl angle on the efficiency of the chiral transfer. Detailed investigation of the sensing capabilities of the systems reveals an extraordinarily high selectivity for NO2 and a response to concentrations as low as 100 ppm. The systems show a direct response to the analyte gas leading to a concentration‐dependent shift of the reflectance wavelength of up to several hundred nanometers. Incorporation of copper ions remarkably improves the sensor's properties in terms of sensitivity and selectivity, enabling the tailored tweaking of the system's properties.
Polymer fibers with liquid crystals (LCs) in the core have potential as autonomous sensors of airborne volatile organic compounds (VOCs), with a high surface-to-volume ratio enabling fast and sensitive response and an attractive non-woven textile form factor. We demonstrate their ability to continuously and quantitatively measure the concentration of toluene, cyclohexane, and isopropanol as representative VOCs, via the impact of each VOC on the LC birefringence. The response is fully reversible and repeatable over several cycles, the response time can be as low as seconds, and high sensitivity is achieved when the operating temperature is near the LC-isotropic transition temperature. We propose that a broad operating temperature range can be realized by combining fibers with different LC mixtures, yielding autonomous VOC sensors suitable for integration in apparel or in furniture that can compete with existing consumer-grade electronic VOC sensors in terms of sensitivity and response speed.
Hydrogen‐Bonded Cholesteric Liquid Crystals—A Modular Approach Toward Responsive Photonic Materials
(2022)
A supramolecular approach for photonic materials based on hydrogen-bonded cholesteric liquid crystals is presented. The modular toolbox of low-molecular-weight hydrogen-bond donors and acceptors provides a simple route toward liquid crystalline materials with tailor-made thermal and photonic properties. Initial studies reveal broad application potential of the liquid crystalline thin films for chemo- and thermosensing. The chemosensing performance is based on the interruption of the intermolecular forces between the donor and acceptor moieties by interference with halogen-bond donors. Future studies will expand the scope of analytes and sensing in aqueous media. In addition, the implementation of the reported materials in additive manufacturing and printed photonic devices is planned.
Optical gas sensors based on chiral-nematic liquid crystals (N* LCs) forming one-dimensional photonic crystals do not require electrical energy and have a considerable potential to supplement established types of sensors. A chiral-nematic phase with tunable selective reflection is induced in a nematic host LC by adding reactive chiral dopants. The selective chemical reaction between dopant and analyte is capable to vary the pitch length (the lattice constant) of the soft, self-assembled, one-dimensional photonic crystal. The progress of the ongoing chemical reaction can be observed even by naked eye because the color of the samples varies. In this work, we encapsulate the responsive N* LC in microscale polyvinylpyrrolidone (PVP) fibers via coaxial electrospinning. The sensor is, thus, given a solid form and has an improved stability against nonavoidable environmental influences. The reaction behavior of encapsulated and nonencapsulated N* LC toward a gaseous analyte is compared, systematically. Making use of the encapsulation is an important step to improve the applicability.
P30 - Das Elektrospinnen von halbleitenden Zinndioxidfasern für die Detektion von Wasserstoff
(2022)
Das Ziel dieser Arbeit ist die Entwicklung von dünnen keramischen Fasern als halbleitendes Sensormaterial zum Nachweis von Wasserstoff, möglichst bei Zimmertemperatur. Die elektrische Leitfähigkeit halbleitender Metalloxide ändert sich durch die Einwirkung von oxidierenden und reduzierenden Gasen auf die Oberfläche des Metalloxids. Dieser Effekt kann zur Messung der Gaskonzentration genutzt werden. Die Reaktion von Zinn(IV)-oxid mit Wasserstoff basiert auf der Reduktion des Zinn(IV)-oxids zum Zinn, wobei die Elektronen des Zinn(IV)-oxids im metallischen Zinn verbleiben und dort im nicht gebundenen Zustand zu einer Leitfähigkeitserhöhung beitragen. Die Reaktion des Wasserstoffes kann sowohl mit den Sauerstoffatomen des Oxids als auch mit adsorbierten Sauerstoffatomen an der Oxidoberfläche stattfinden.[ 6] Da die Reaktionen an der Oberfläche des Oxids stattfinden, sollten Sensoren mit einer großen Oberfläche im Vergleich zu metalloxidischen Bulkmaterialien eine höhere Empfindlichkeit aufweisen. [3] Die Verwendung von Fasern anstelle von Dünn- oder Dickschichten führt dabei zu einer besseren Sensitivität gegenüber Gasen.
Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030, Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meat-related pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5 % and 97.5 %, respectively.