In laser applications, unwanted transmission can require a beam dump positioned behind every mirror to prevent unwanted light propagation. Novel new mirrors (patent pending) based on an engineered substrate are able to reduce the power leaking through a component by several orders of magnitude while maintaining <98% of the reflective properties. Rated as both mirrors and neutral density filters, these parts greatly reduce the need for beam dumps behind components, minimizing the size of optical systems and improving laser safety. This paper will discuss the performance of these engineered mirrors and compare their reflection and transmission with traditional Fused Silica mirrors.
We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.
Detection of illegal compounds requires a reliable, selective and sensitive detection device. The successful device features automated target acquisition, identification and signal processing. It is portable, fast, user friendly, sensitive, specific, and cost efficient. LEAs are in need of such technology. CRIM-TRACK is developing a sensing device based on these requirements. We engage highly skilled specialists from research institutions, industry, SMEs and LEAs and rely on a team of end users to benefit maximally from our prototypes. Currently we can detect minute quantities of drugs, explosives and precursors thereof in laboratory settings. Using colorimetric technology we have developed prototypes that employ disposable sensing chips. Ease of operation and intuitive sensor response are highly prioritized features that we implement as we gather data to feed into machine learning. With machine learning our ability to detect threat compounds amidst harmless substances improves. Different end users prefer their equipment optimized for their specific field. In an explosives-detecting scenario, the end user may prefer false positives over false negatives, while the opposite may be true in a drug-detecting scenario. Such decisions will be programmed to match user preference. Sensor output can be as detailed as the sensor allows. The user can be informed of the statistics behind the detection, identities of all detected substances, and quantities thereof. The response can also be simplified to “yes” vs. “no”. The technology under development in CRIM-TRACK will provide custom officers, police and other authorities with an effective tool to control trafficking of illegal drugs and drug precursors.
We will show the results from a Tunable Diode Laser (TDL) spectrometer installation monitoring the O2 concentration and the temperature in an olivine pellet production plant. The spectrometer has been operating continuously for more than two years. In the pelletizing process a reduction of magnetite and sintering takes place at a temperature around 1250 degree(s)C. To achieve a high and predictable quality of the produced pellets the oxygen concentration and the temperature has to be measured in-situ inside the process furnace. A specially designed high temperature sensor was mounted on the furnace wall and an optical fiber was used to carry the probing light from the TDL spectrometer to the measurement point. The TDL spectrometer operates at two absorption lines in the near infrared wavelength region to measure the oxygen concentration and the temperature simultaneously. The temperature is measured using the relative intensity of the two absorption lines and the concentration is calculated from the temperature compensated absorbance. The accuracy of the concentration and temperature measurements at 1 s response time was 0.1 vol.% and 50 degree(s)C, respectively. In order to validate the TDL measurements the pelletizing process furnace temperature was varied between 100 degree(s)C up to 1300 degree(s)C while the oxygen and temperature readings from the TDL spectrometer was recorded. The temperature measurements were also correlated with temperature measurements using thermocouples inside the furnace. The O2 absorption line parameters were determined in a controlled laboratory experiment using a heated measurement path. This work shows that it is possible to build and field a TDL spectrometer to measure O2 and temperature in-situ in a steel making process furnace.
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.