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We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.
Binary relations with certain properties such as biorders, equivalences or difunctional relations can be represented as particular matrices. In order for these properties to be identified usually a rearrangement of rows and columns is required in order to reshape it into a recognisable normal form. Most algorithms performing these transformations are working on binary matrix representations of the underlying relations. This paper presents an approach to use the RLE-compressed matrix representation as a data structure for storing relations to test whether they are biorders in a hopefully more efficient way.
The central theme of the 2014 Annual Report is human thinking.
In an interview, University President Hartmut Ihne and 3Sat moderator Gert Scobel discuss the concept of thought: "Should we be allowed to give up our autonomy voluntarily?"
Our university’s Language Centre Director James Chamberlain examines to what extent thinking varies in different languages.
Professor Paul Plöger from the Department of Computer Science explains why robots have tremendous problems understanding complex relationships in open environments.
Rather than focusing solely on our university’s future, the Annual Report links the fascinating theme to the enormous variety of life, research and tuition offered by H-BRS.
Advanced driver assistance systems (ADAS) are technology systems and devices designed as an aid to the driver of a vehicle. One of the critical components of any ADAS is the traffic sign recognition module. For this module to achieve real-time performance, some preprocessing of input images must be done, which consists of a traffic sign detection (TSD) algorithm to reduce the possible hypothesis space. Performance of TSD algorithm is critical.
One of the best algorithms used for TSD is the Radial Symmetry Detector (RSD), which can detect both Circular [7] and Polygonal traffic signs [5]. This algorithm runs in real-time on high end personal computers, but computational performance of must be improved in order to be able to run in real-time in embedded computer platforms.
To improve the computational performance of the RSD, we propose a multiscale approach and the removal of a gaussian smoothing filter used in this algorithm. We evaluate the performance on both computation times, detection and false positive rates on a synthetic image dataset and on the german traffic sign detection benchmark [29].
We observed significant speedups compared to the original algorithm. Our Improved Radial Symmetry Detector is up to 5.8 times faster than the original on detecting Circles, up to 3.8 times faster on Triangle detection, 2.9 times faster on Square detection and 2.4 times faster on Octagon detection. All of this measurements were observed with better detection and false positive rates than the original RSD.
When evaluated on the GTSDB, we observed smaller speedups, in the range of 1.6 to 2.3 times faster for Circle and Regular Polygon detection, but for Circle detection we observed a decreased detection rate than the original algorithm, while for Regular Polygon detection we always observed better detection rates. False positive rates were high, in the range of 80% to 90%.
We conclude that our Improved Radial Symmetry Detector is a significant improvement of the Radial Symmetry Detector, both for Circle and Regular polygon detection. We expect that our improved algorithm will lead the way to obtain real-time traffic sign detection and recognition in embedded computer platforms.
Communicating Sequential Processes (CSP) [7] is a calculus for concurrent systems that has been the basis of subject-oriented business process management (S-BPM) [4]. We use CSPm -- a machine readable dialect of CSP -- to create a sequence of models for a case study on an "Automated Teller Machine" [1]. We use the refinement checker FDR2 to prove that certain models are correct implementations of specifications.
Comparison of the subject-oriented and the Petri net based approach for business process automation
(2015)
The subject-oriented modelling approach [5] significally differs from the classic Petri net based approach of many business process modeling languages like EPC [9], Business Process Model and Notation (BPMN) [11], and also Yet Another Workflow Language (YAWL) [10]. In this work, we compare the two approaches by modeling a case study called "Procure to Pay"[3], a typical business process where some equipment for a construction site is rented and finally paid. The case study is not only modelled but also automated using the Metasonic Suite for the subject-oriented and YAWL for the Petri net based approach.
Extraction of text information from visual sources is an important component of many modern applications, for example, extracting the text from traffic signs on a road scene in an autonomous vehicle. For natural images or road scenes this is a unsolved problem. In this thesis the use of histogram of stroke widths (HSW) for character and noncharacter region classification is presented. Stroke widths are extracted using two methods. One is based on the Stroke Width Transform and another based on run lengths. The HSW is combined with two simple region features– aspect and occupancy ratios– and then a linear SVM is used as classifier. One advantage of our method over the state of the art is that it is script-independent and can also be used to verify detected text regions with the purpose of reducing false positives. Our experiments on generated datasets of Latin, CJK, Hiragana and Katakana characters show that the HSW is able to correctly classify at least 90% of the character regions, a similar figure is obtained for non-character regions. This performance is also obtained when training the HSW with one script and testing with a different one, and even when characters are rotated. On the English and Kannada portions of the Chars74K dataset we obtained over 95% correctly classified character regions. The use of raycasting for text line grouping is also proposed. By combining it with our HSW-based character classifier, a text detector based on Maximally Stable Extremal Regions (MSER) was implemented. The text detector was evaluated on our own dataset of road scenes from the German Autobahn, where 65% precision, 72% recall with a f-score of 69% was obtained. Using the HSW as a text verifier increases precision while slightly reducing recall. Our HSW feature allows the building of a script-independent and low parameter count classifier for character and non-character regions.
This book chapter describes application examples of gas chromatography/mass spectrometry and pyrolysis – gas chromatography/mass spectrometry in failure analysis for the identification of chemical materials like mineral oils and nitrile rubber gaskets. Furthermore, failure cases demanding identification of polymers/copolymers in fouling on the compressor wall of a car air conditioner and identification of fouling on the surface of a bearing race from the automotive industry are demonstrated. The obtained analytical results were then used for troubleshooting and remedial action of the technological process.
In the fermentation process sugars are transformed into lactic acid. pH meters have traditionally been used for fermentation process monitoring based on acidity. More recently, near infrared (NIR) spectroscopy has proven to provide an accurate and non-invasive method to detect when the transformation of sugars into lactic acid is finished. The fermentation process when sugars are transformed into lactic acid. This research proposes the use of simplified NIR spectroscopy using multispectral optical sensors as a simpler and less expensive measure to end the fermentation process. The NIR spectrum of milk and yogurt is compared to find and extract features that can be used to design a simple sensor to monitor the yogurt fermentation process. Multispectral images in four selected wavebands within the NIR spectrum are captured and show different spectral remission characteristics for milk, yogurt and water, which support the selection of these wavebands for milk and yogurt classification.
The latest advances in the field of smart card technologies allow modern cards to be more than just simple security tokens. Recent developments facilitate the use of interactive components like buttons, displays or even touch-sensors within the card's body thus conquering whole new areas of application. With interactive functionalities the usability aspect becomes the most important one for designing secure and popularly accepted products. Unfortunately, the usability can only be tested fully with completely integrated hence expensive smart card prototypes. This restricts severely application specific research, case studies of new smart card user interfaces and the optimization of design aspects, as well as hardware requirements by making usability and acceptance tests in smart card development very costly and time-consuming. Rapid development and simulation of smart card interfaces and applications can help to avoid this restriction. This paper presents a rapid development process for new smart card interfaces and applications based on common smartphone technology using a tool called SCUID^Sim. We will demonstrate the variety of usability aspects that can be analyzed with such a simulator by discussing some selected example projects.