Refine
Year of publication
- 2017 (27) (remove)
Document Type
- Conference Proceeding (17)
- Article (7)
- Lecture (1)
- Part of Periodical (1)
- Report (1)
Has Fulltext
- no (27) (remove)
Is part of the Bibliography
- no (27)
Keywords
Web based security applications have become increasingly important in the past years. Especially in times of blockchain based crypto currencies, user authentication is a critical aspect for the overall security, integrity and acceptance of such systems. While blockchain technologies provide a decentralized approach, the client side still largely relies on centralized security approaches. Those centralized approaches are easier to implement, but at the same time bear the risk of usual security flaws. Therefore, this paper presents a decentralized approach for increasing the security by adding a decentralized two-factor authentication mechanism to the execution of
operations.
In this contribution we present a novel approach to transform data from time-of-flight (ToF) sensors to be interpretable by Convolutional Neural Networks (CNNs). As ToF data tends to be overly noisy depending on various factors such as illumination, reflection coefficient and distance, the need for a robust algorithmic approach becomes evident. By spanning a three-dimensional grid of fixed size around each point cloud we are able to transform three-dimensional input to become processable by CNNs. This simple and effective neighborhood-preserving methodology demonstrates that CNNs are indeed able to extract the relevant information and learn a set of filters, enabling them to differentiate a complex set of ten different gestures obtained from 20 different individuals and containing 600.000 samples overall. Our 20-fold cross-validation shows the generalization performance of the network, achieving an accuracy of up to 98.5% on validation sets comprising 20.000 data samples. The real-time applicability of our system is demonstrated via an interactive validation on an infotainment system running with up to 40fps on an iPad in the vehicle interior.
The open education movement has witnessed ups and downs from initial interest in transparency and openness, followed by a lack of reuse of open educational resources (OER) and the massive boost of interest in massive open online courses (MOOCs). This article addresses educators' online behaviors and perceptions regarding participation in collaborative development of OER in online settings. Using a data-driven approach to study educators' perceptions, this article presents multiple considerations for collaborative OER development and validates a new model explaining educators' intention to participate in collaborative action. The findings reveal the contradictory nature of emotional ownership of knowledge: a critical enabling factor for commitment and a barrier to knowledge exchange in an open and transparent manner. The findings also show how outcome expectations regarding increase in reputation and status in the network do not influence the intention to share knowledge. Further interviews with idea-sharing platform users enable us to explain the favorable settings to resolve the dilemma of emotional ownership. The study contributes not only to further development of the open education movement but also to theory development of educators’ collaborative behaviors online.
Gallium Nitride (GaN) and Indium Gallium Nitride (InGaN) have become important semiconductor materials for the LED lighting industry. Recently, a photoluminescence (PL) technique for direct in-situ characterization of GaN and InGaN layers during epitaxial growth in a planetary metalorganic vapor phase epitaxy (MOVPE) reactor was reported. The PL signals reveal – at the earliest possible stage – information about current layer thickness, temperature, composition, surface roughness, and self-absorption. Thus, the PL data is valuable for both controlling and optimizing the growth parameters, thereby promising both better devices and a better yield for the LED industry. This technical report describes an extension of this PL technique to close coupled showerhead (CCS) reactors with narrow optical viewports. In contrast to the wide aperture optics in previous investigations, a compact and all-fiber optical probe without voluminous lens optics, filter elements or beam splitters was used.
Editorial
Jörg Himmel, Olfa Kanoun, Thomas Seeger, Klaus Thelen IEEE Workshop on Industrial and Medical Measurement and Sensor Technology – SENSORICA 2016 1
Beiträge Jan Taro Svejda, Andreas Rennings, Daniel Erni A metamaterial based dual-resonant coil element for combined sodium/hydrogen MRI at 7 Tesla 2
Fabian Feldhaus, Ingo Schmitz, Thomas Seeger Emission spectroscopy based sensor developed for engine testing 13
Anne-Sophie Rother, Thomas Dietz, Peter Kohns, Georg Ankerhold Molecular laser-induced breakdown spectroscopy for elemental analysis 23
Johannes Kiefer, Andreas Bösmann, Peter Wasserscheid Quantitative measurement of complex substances dissolved in an ionic liquid using IR spectroscopy and chemometrics 32
Oliver Gieseler, Hubert Roth, Jürgen Wahrburg Methods to determine the scaling factor in X-ray images for exact preoperative planning in hip surgery 38
Erwin Gerz, Matthias Mende, Hubert Roth Development of an optical tracking system for a novel flexible and soft manipulator with controllable stiffness for minimal invasive surgery (MIS) 47
Jens Weidenmüller, Christian Walk, Özgü Dogan, Pierre Gembaczka, Alexander Stanitzki, Michael Görtz Telemetric multi-sensor system for medical applications – The approach 53
Inga-Maria Eichentopf, Martin Reufer Measurement and analysis of wavefront structures of diode lasers 59