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The need to measure basic aerosol parameters has increased dramatically in the last decade. This is due mainly to their harmful effect on the environment and on public health. Legislation requires that particle emissions and ambient levels, workplace particle concentrations and exposure to them are measured to confirm that the defined limits are met and the public is not exposed to harmful concentrations of aerosols.
Die in dieser Arbeit vorgestellte Vorgehensweise erlaubt die Ortung von Schienenfahrzeugen in topologischen Karten allein mit Hilfe eines Wirbelstromsensorsystems (WSS). Zur Ortung primär erforderlich ist die Identifizierung des befahrenen Gleises selbst, wofür unterschiedliche in einer Karte gespeicherte Merkmale herangezogen werden sowie der zurückgelegte Weg, der durch Zählen der passierten Schwellen ermittelt wird. Diese Merkmale werden mittels eigens definierter, virtueller Sensoren aus dem Signal des WSS gewonnen und mittels einem Bayes’schen Formalismus mit den Referenzdaten aus der vorliegenden topologischen Karte abgeglichen. Diese auf virtuellen Sensoren basierende Vorgehensweise erlaubt eine Parallelisierung der Sensorsignalverarbeitung und eine flexible Einbindung von Sensoren in das Ortungssystem. Die Möglichkeit, Weichen mit einer Trefferquote von 99% zu detektieren, erlaubt die Verfolgung der Fahrzeugposition über die gesamte Fahrstrecke hinweg, unter alleiniger Verwendung der vom WSS gelieferten Messdaten.
Für die genaue Positionsbestimmung in Innenräumen, beispielsweise in Bahnhöfen oder Einkaufszentren, soll in dem beschriebenen Projekt untersucht werden, inwiefern lokale Magnetfelder genutzt werden können, um Genauigkeit und Robustheit zu erhöhen. Hierzu wird untersucht, ob und wie kostengünstige Magnetfeldsensoren und mobile Roboterplattformen genutzt werden können, um Karten zu erstellen, die eine spätere Navigation, beispielsweise mit Smartphones oder mit anderen mobilen Geräten.
Comparison of Time Warping Algorithms for Rail Vehicle Velocity Estimation in Low Speed Scenarios
(2017)
This paper deals with the detection and segmentation of clouds on high-dynamic-range (HDR) images of the sky as well as the calculation of the position of the sun at any time of the year. In order to predict the movement of clouds and the radiation of the sun for a short period of time, the clouds thickness and position have to be known as precisely as possible. Consequently, the segmentation algorithm has to provide satisfactory results regardless of different weather, illumination and climatic conditions. The principle of the segmentation is based on the classification of each pixel as a cloud or as a sky. This classification is usually based on threshold methods, since these are relatively fast to implement and show a low computational burden. In order to predict if and when the sun will be covered by clouds, the position of the sun on the images has to be determined. For this purpose, the zenith and azimuth angles of the sun are determined and converted into XY coordinates.
Solar irradiance prediction is vital for the power management and the cost reduction when integrating solar energy. The study is towards a ground image based solar irradiance prediction which is highly dependent on the cloud coverage. The sky images are collected by using ground based sky imager (fisheye lens). In this work, different algorithms for cloud detection being a preparation step for their segmentation are compared.
The fisheye camera has been widely studied in the field of ground based sky imagery and robot vision since it can capture a wide view of the scene at one time. However, serious image distortion is a major drawback hindering its wider use. To remedy this, this paperproposes a lens calibration and distortion correction method for detecting clouds and forecasting solar radiation. Finally, the radial distortion of the fisheye image can be corrected by incorporating the estimated calibration parameters. Experimental results validate the effectiveness of the proposed method.
Amongst all the major hazard aspects for the health of people in big conglomerates is the increase of the particulate matter concentration. Traditional systems for particulate matter (PM) monitoring have a great number of drawbacks but the main issues are economical and are related to the installation costs and never ending periodical maintenance expenses. After all there are installations of such systems but their number is limited and having in mind the growth of population, cities and industry areas, there is even a bigger need for more information on air quality because PM changes non-linearly, has a wide range and different sources. In this paper, we propose an approach, based on low-cost sensor nodes, for real-time measuring and obtaining information about the PM concentration. The adoption of that approach allows for a detailed study of the intensities of pollution and its sources. The system power supply is powered by a PV module. The power supply unit is designed using a model-based design that is a new approach to prototyping power-operated electronic devices with guaranteed performance.
This paper presents an approach for implementing an automated hit detection and score calculation system for a steel dartboard using a standard webcam. First, the rectilinear field separations of the dartboard are described mathematically by means of line slopes and are than stored. These slopes serve as a basis for later score calculation. In addition, thrown darts have to be detected and the pixel at which the dart cuts the dartboard has to be determined. When this information is known, a comparison is made using the line slopes, allowing the field number of the hit to be detected. The decision for single, double or triple hit is made by evaluating the defined colors on the dartboard. All these functions are then packaged in a Matlab GUI.