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In 3D extended object tracking (EOT), well-established models exist for tracking the object extent using various shape priors. A single update, however, has to be performed for every measurement using these models leading to a high computational runtime for high-resolution sensors. In this paper, we address this problem by using various model-independent downsampling schemes based on distance heuristics and random sampling as pre-processing before the update. We investigate the methods in a simulated and real-world tracking scenario using two different measurement models with measurements gathered from a LiDAR sensor. We found that there is a huge potential for speeding up 3D EOT by dropping up to 95\% of the measurements in our investigated scenarios when using random sampling. Since random sampling, however, can also result in a subset that does not represent the total set very well, leading to a poor tracking performance, there is still a high demand for further research.
Im Rahmen der Lehrveranstaltung "Nachhaltigkeit im industriellen Umfeld" im Masterstudiengang Umwelt- und Verfahrenstechnik der Hochschulen Konstanz und Ravensburg-Weingarten wurde 2015 eine studentische Fachkonferenz durchgeführt.
Die Studierenden entwickelten in Einzelarbeit oder als Zweierteam Konferenzbeiträge zu folgenden Themen:
- Innovationen und Spannendes aus dem Bereich der Energieerzeugung und -wandlung
- Aspekte der Schließung von Stoffkreisläufen und Vermeidung von Schadstoffeinträgen in die Umwelt
- Chancen und Herausforderungen Nachwachsender Rohstoffe bei verschiedenen Einsatzmöglichkeiten sowie Themen der Nachhaltigkeit in der Landwirtschaft
- verschiedene Blickwinkel auf das Thema Wasser (von der Abwasserreinigung bis zum Wasserkonsum der Konsumenten)
- die Betrachtung spezifischer Industrien und Unternehmen sowie deren Werkzeuge zur Umsetzung von Nachhaltigkeit
Die Ergebnisse der studentischen Fachkonferenz zur „Nachhaltigkeit im industriellen Umfeld“ werden in der vorliegenden Publikation präsentiert.
Im Rahmen der Lehrveranstaltung "Nachhaltigkeit im industriellen Umfeld" im Masterstudiengang Umwelt- und Verfahrenstechnik der Hochschulen Konstanz und Ravensburg-Weingarten fand im Dezember 2016 eine studentische Fachkonferenz statt. Die Studierenden entwickelten in Einzelarbeit oder als Zweierteam
Konferenzbeiträge zu folgenden Themen:
- Spannendes aus dem Bereich der Energieerzeugung und der Grauen Energie
- Aspekte der Kreislaufwirtschaft
- Ökosysteme - ihre Belastung und Erhalt
- Spezifische Wirtschaftszweige und Nachhaltigkeit
Die Ergebnisse der studentischen Fachkonferenz zur „Nachhaltigkeit im
industriellen Umfeld“ werden in der vorliegenden Publikation präsentiert.
Offline handwriting recognition systems often use LSTM networks, trained with line- or word-images. Multi-line text makes it necessary to use segmentation to explicitly obtain these images. Skewed, curved, overlapping, incorrectly written text, or noise can lead to errors during segmentation of multi-line text and reduces the overall recognition capacity of the system. Last year has seen the introduction of deep learning methods capable of segmentation-free recognition of whole paragraphs. Our method uses Conditional Random Fields to represent text and align it with the network output to calculate a loss function for training. Experiments are promising and show that the technique is capable of training a LSTM multi-line text recognition system.
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.
We have analyzed a pool of 37,839 articles published in 4,404 business-related journals in the entrepreneurship research field using a novel literature review approach that is based on machine learning and text data mining. Most papers have been published in the journals ‘Small Business Economics’, ‘International Journal of Entrepreneurship and Small Business’, and ‘Sustainability’ (Switzerland), while the sum of citations is highest in the ‘Journal of Business Venturing’, ‘Entrepreneurship Theory and Practice’, and ‘Small Business Economics’. We derived 29 overarching themes based on 52 identified clusters. The social entrepreneurship, development, innovation, capital, and economy clusters represent the largest ones among those with high thematic clarity. The most discussed clusters measured by the average number of citations per assigned paper are research, orientation, capital, gender, and growth. Clusters with the highest average growth in publications per year are social entrepreneurship, innovation, development, entrepreneurship education, and (business-) models. Measured by the average yearly citation rate per paper, the thematic cluster ‘research’, mostly containing literature studies, received most attention. The MLR allows for an inclusion of a significantly higher number of publications compared to traditional reviews thus providing a comprehensive, descriptive overview of the whole research field.
Algorithms for calculating the string edit distance are used in e.g. information retrieval and document analysis systems or for evaluation of text recognizers. Text recognition based on CTC-trained LSTM networks includes a decoding step to produce a string, possibly using a language model, and evaluation using the string edit distance. The decoded string can further be used as a query for database search, e.g. in document retrieval. We propose to closely integrate dictionary search with text recognition to train both combined in a continuous fashion. This work shows that LSTM networks are capable of calculating the string edit distance while allowing for an exchangeable dictionary to separate learned algorithm from data. This could be a step towards integrating text recognition and dictionary search in one deep network.
CO2 compensation measures, in particular the compensation of flights, are becoming more and more popular. Carbon offsetting is defined as measures financed by donations that save greenhouse gases previously emitted elsewhere through climate protection projects.
CO2 abatement costs are often low in developing countries. This is why most offset projects are implemented there. Nevertheless, this does not mean that the holiday resort and the project country are in any way related to each other.
By linking carbon offset projects with the destination country, the tourist is able to get an impression of the co-financed project. In case such projects are realized in cooperation with the hotel, the hotel operator obtains a new tourist attraction and can demonstrate its efforts to climate protection in a PR-effective way.