Refine
Year of publication
Document Type
- Conference Proceeding (39)
- Article (reviewed) (9)
- Part of a Book (5)
- Patent (4)
- Article (unreviewed) (3)
- Contribution to a Periodical (2)
- Book (1)
Conference Type
- Konferenzartikel (33)
- Konferenz-Abstract (3)
- Konferenz-Poster (2)
- Sonstiges (1)
Keywords
- E-Learning (5)
- Couplings (3)
- Design automation (3)
- Finite difference methods (3)
- Finite-Elemente-Methode (3)
- Mobile Learning (3)
- Algorithmus (2)
- Blended Learning (2)
- FETs (2)
- Frequency (2)
Institute
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (63) (remove)
Open Access
- Closed (23)
- Open Access (23)
- Bronze (14)
- Closed Access (5)
- Gold (1)
“Today’s network landscape consists of quite different network technologies, wide range of end-devices with large scale of capabilities and power, and immense quantity of information and data represented in different formats” [9]. A lot of efforts are being done in order to establish open, scalable and seamless integration of various technologies and content presentation for different devices including mobile considering individual situation of the end user. This is very difficult because various kinds of devices used by different users or in different times/parallel by the same user which is not predictable and have to be recognized by the system in order to know device capabilities. Not only the devices but also Content and User Interfaces are big issues because they could include different kinds of data format like text, image, audio, video, 3D Virtual Reality data and upcoming other formats. Language Learning Game (LLG) is such an example of a device independent application where different kinds of devices and data formats, as a content of a flashcard is used for a collaborative learning. The idea of this game is to create a short story in a foreign language by using mobile devices. The story is developed by a group of participants by exchanging sentences/data via a flashcard system. This way the participants can learn from each other by knowledge sharing without fear of making mistakes because the group members are anonymous. Moreover they do not need a constant support from a teacher.
The invention concerns a method for spectrum monitoring a given frequency band, in which the spectral power density (S(f)) within the given frequency band is determined for all noise and signal components in the frequency band and, in order to detect the presence of one or more signals within the given frequency band, it is evaluated whether the spectral power density (S(f)) exceeds a threshold value (&lgr;). According to the invention, the threshold value (&lgr;) is calculated in accordance with an estimation of a distribution density (hR(S)) for the noise component of the spectral power density (S(f)) within the given frequency band and in accordance with a predefined value for the false-alarm probability (Pfa).
We propose secure multi-party computation techniques for the distributed computation of the average using a privacy-preserving extension of gossip algorithms. While recently there has been mainly research on the side of gossip algorithms (GA) for data aggregation itself, to the best of our knowledge, the aforementioned research line does not take into consideration the privacy of the entities involved. More concretely, it is our objective to not reveal a node's private input value to any other node in the network, while still computing the average in a fully-decentralized fashion. Not revealing in our setting means that an attacker gains only minor advantage when guessing a node's private input value. We precisely quantify an attacker's advantage when guessing - as a mean for the level of data privacy leakage of a node's contribution. Our results show that by perturbing the input values of each participating node with pseudo-random noise with appropriate statistical properties (i) only a minor and configurable leakage of private information is revealed, by at the same time (ii) providing a good average approximation at each node. Our approach can be applied to a decentralized prosumer market, in which participants act as energy consumers or producers or both, referred to as prosumers.
Nowadays the processing power of mobile phones, smartphones and PDAs is increasing as well as the transmission bandwidth. Nevertheless there is still the need to reduce the content and the need of processing the data. We discuss the proposals and solutions for dynamic reduction of the transmitted content. For that, device specific properties are taken into account, as much as for the aim to reduce the need of processing power at the client side to be able to display the 3D (virtual reality) data. Therefore, well known technologies, e.g. data compression are combined with new developed ideas to reach the goal of adaptive content transmission. To achieve a device dependant reduction of processing power the data have to be preprocessed at the server side or the server even has to take over functionality of weak mobile devices.
Nowadays, it is assumed of many applications, companies and parts of the society to be always available online. However, according to [Times, Oct, 31 2011], 73% of the world population do not use the internet and thus aren't “online” at all. The most common reasons for not being “online” are expensive personal computer equipment and high costs for data connections, especially in developing countries that comprise most of the world’s population (e.g. parts of Africa, Asia, Central and South America). However it seems that these countries are leap-frogging the “PC and landline” age and moving directly to the “mobile” age. Decreasing prices for smart phones with internet connectivity and PC-like operating systems make it more affordable for these parts of the world population to join the “always-online” community. Storing learning content in a way accessible to everyone, including mobile and smart phones, seems therefore to be beneficial. This way, learning content can be accessed by personal computers as well as by mobile and smart phones and thus be accessible for a big range of devices and users. A new trend in the Internet technologies is to go to “the cloud”. This paper discusses the changes, challenges and risks of storing learning content in the “cloud”. The experiences were gathered during the evaluation of the necessary changes in order to make our solutions and systems “cloud-ready”.
Der Studienbeginn wird an der Hochschule Offenburg durch Vorbereitungskurse, sogenannte Brückenkurse, unterstützt. Wir stellen vorläufige Ergebnisse beim Einsatz von Smartphones und Tablets im Rahmen des Physik-Brückenkurses vor, bei dem die Studenten Hilfen zum selbständigen Üben durch eine App erhalten. Durch die Überarbeitung des Kurses und den Einsatz der App konnte der Teilnehmerschwund verringert werden. Die Evaluationsergebnisse bestätigen eine hohe Akzeptanz der Neuerungen seitens der Studierenden. Erste Auswertungen von Ein- und Ausgangstests deuten darauf hin, dass durch den Brückenkurs eine Angleichung der Vorkenntnisse der Studienanfänger erreicht wird, da Teilnehmer mit geringeren Vorkenntnissen tendenziell einen größeren Lernfortschritt erreichen. Durch unterschiedliche Schwierigkeitsstufen und selbstregulierte Übungsphasen in individuellem Tempo können aber auch die Erfordernisse der stärkeren Teilnehmer angemessen berücksichtigt werden.
Signal detection and bandwidth estimation, also known as channel segmentation or information channel estimation, is a perpetual topic in communication systems. In the field of radio monitoring this issue is extremely challenging, since unforeseeable effects like fading occur accidentally. In addition, most radio monitoring devices normally scan a wide frequency range of several hundred MHz and have to detect a multitude of different signals, varying in signal power, bandwidth and spectral shape. Since narrowband sensing techniques cannot be directly applied, most radio monitoring devices use Nyquist wideband sensing to discover the huge frequency range. In practice, sensing is normally conducted by an FFT sweep spectrum analyzer that delivers the power spectral density (PSD) values to the radio monitoring system. The channel segmentation is the initial step of a comprehensive signal analysis in a radio monitoring system based on the PSD values. In this paper, a novel approach for channel segmentation is presented that is based on a quantization and a histogram evaluation of the measured PSD. It will be shown that only the combination of both evaluations will lead to an successful automatic channel segmentation. The performance of the proposed algorithm is shown in a real radio monitoring szenario.