@article{66648,
  abstract     = {{Der Beitrag untersucht Kurzfilme als Kommunikations-, Kompensations- und Diskursmedien im Religionsunterricht und entwickelt eine kritische Kurzfilmhermeneutik. Ausgangspunkt ist die Beobachtung, dass Kurzfilme im Unterricht häufig inhaltlich verengt und primär als Mittel zur Themenvermittlung genutzt werden. Demgegenüber betonen die Autor:innen die spezifische Ästhetik, Mehrdeutigkeit und mediale Komplexität des Kurzfilms. Zentrale Kategorien sind dabei das Zusammenspiel von „story“ und „discourse“ sowie inter-, intra- und transmediale Bezüge. Kurzfilme eröffnen durch Verdichtung, Leerstellen und offene Enden eigenständige Zugänge zu Sinnfragen und fördern aktive Rezeptionsprozesse. Anhand des Films „YELLOW“ wird gezeigt, wie filmische Gestaltungselemente Bedeutung generieren und gesellschaftlich-religiöse Diskurse anstoßen. Eine kritische Kurzfilmhermeneutik erweitert somit die Filmdidaktik, indem sie das Medium selbst stärker in den Fokus rückt. Ziel ist eine reflektierte, medienkompetente und dialogorientierte Nutzung von Kurzfilmen im Religionsunterricht.
}},
  author       = {{Keuchen, Marion and Ta Van, Juliane and Tuna, Mehmet H}},
  issn         = {{1863-0502}},
  journal      = {{TheoWeb. Zeitschrift für Religionspädagogik}},
  keywords     = {{interreligiöses Lernen, Religionsunterricht, Positionierung, Islam, Christentum, Unterrichtsbeobachtung, Position, Fremdheit}},
  number       = {{1}},
  pages        = {{173--189}},
  title        = {{{ Position, Positionierungen und Positionalität im Religionsunterricht im Unterrichten über Religionen, denen man nicht angehört Autor/innen}}},
  doi          = {{10.58069/theow.2026.1.120}},
  volume       = {{25}},
  year         = {{2026}},
}

@article{61887,
  abstract     = {{Language proficiency is crucial for migrants’ social position in the labour market and therefore plays a key role in the (re-)production of social inequalities in modern societies. There are different ways of capturing language skills in quantitative studies. However, it is important to question the extent to which existing language measures mirror migrants’ realities and relevant linguistic everyday life practices. In our paper, we contribute to this question by disentangling various measures of language proficiency. We use a large sample of migrants in Germany (GSOEP) that contains numerous language measures. We conduct detailed quantitative analyses on how various language variables influence migrants’ social position, by which we mean migrants’ socioeconomic status (as measured by ISEI). The ISEI is mainly based on occupation, but also on education and income. Our findings indicate that especially the self-assessed German speaking proficiency is an important and parsimonious predictor for migrants’ social position in Germany.}},
  author       = {{Diekmann, Isabell and Fröhlich, Joanna Jadwiga}},
  issn         = {{1741-8992}},
  journal      = {{Migration Letters}},
  keywords     = {{migration, social inequalities, language proficiency, social position, language measures}},
  number       = {{5}},
  pages        = {{695–704}},
  publisher    = {{Transnational Press London}},
  title        = {{{How Can Migrants’ Language Proficiency Be Measured? A Discussion of Opportunities and Challenges When Studying the Impact of Language Skills on Social Position }}},
  doi          = {{10.33182/ml.v17i5.803}},
  volume       = {{17}},
  year         = {{2020}},
}

@inproceedings{11816,
  abstract     = {{In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.}},
  author       = {{Hoang, Manh Kha and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian processes, Global Positioning System, convergence, expectation-maximisation algorithm, fingerprint identification, indoor radio, signal classification, wireless LAN, EM algorithm, ML estimation, WiFi indoor positioning, censored Gaussian data classification, clipped data, convergence properties, expectation maximization algorithm, fingerprinting method, maximum likelihood estimation, optimal classification, parameters estimation, portable devices sensitivity, signal strength measurements, wireless LAN positioning systems, Convergence, IEEE 802.11 Standards, Maximum likelihood estimation, Parameter estimation, Position measurement, Training, Indoor positioning, censored data, expectation maximization, signal strength, wireless LAN}},
  pages        = {{3721--3725}},
  title        = {{{Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}}},
  doi          = {{10.1109/ICASSP.2013.6638353}},
  year         = {{2013}},
}

@inproceedings{11832,
  abstract     = {{In this paper we propose an approach to retrieve the absolute geometry of an acoustic sensor network, consisting of spatially distributed microphone arrays, from reverberant speech input. The calibration relies on direction of arrival measurements of the individual arrays. The proposed calibration algorithm is derived from a maximum-likelihood approach employing circular statistics. Since a sensor node consists of a microphone array with known intra-array geometry, we are able to obtain an absolute geometry estimate, including angles and distances. Simulation results demonstrate the effectiveness of the approach.}},
  author       = {{Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Geometry calibration, microphone arrays, position self-calibration}},
  pages        = {{116--120}},
  title        = {{{DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic}}},
  doi          = {{10.1109/ICASSP.2013.6637620}},
  year         = {{2013}},
}

@inproceedings{11818,
  abstract     = {{In this paper we present a system for indoor navigation based on received signal strength index information of Wireless-LAN access points and relative position estimates. The relative position information is gathered from inertial smartphone sensors using a step detection and an orientation estimate. Our map data is hosted on a server employing a map renderer and a SQL database. The database includes a complete multilevel office building, within which the user can navigate. During navigation, the client retrieves the position estimate from the server, together with the corresponding map tiles to visualize the user's position on the smartphone display.}},
  author       = {{Hoang, Manh Kha and Schmitz, Sarah and Drueke, Christian and Vu, Dang Hai Tran and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{Positioning Navigation and Communication (WPNC), 2013 10th Workshop on}},
  keywords     = {{SQL, navigation, smart phones, wireless LAN, RSSI, SQL database, complete multilevel office building, inertial sensor information, inertial smartphone sensors, map renderer, received signal strength index information, relative position estimates, server based indoor navigation, step detection, wireless-LAN access points, Smartphone, fingerprint, indoor navigation, map tile}},
  pages        = {{1--6}},
  title        = {{{Server based indoor navigation using RSSI and inertial sensor information}}},
  doi          = {{10.1109/WPNC.2013.6533263}},
  year         = {{2013}},
}

@inproceedings{11833,
  abstract     = {{In this paper we propose an approach to retrieve the geometry of an acoustic sensor network consisting of spatially distributed microphone arrays from unconstrained speech input. The calibration relies on Direction of Arrival (DoA) measurements which do not require a clock synchronization among the sensor nodes. The calibration problem is formulated as a cost function optimization task, which minimizes the squared differences between measured and predicted observations and additionally avoids the existence of minima that correspond to mirrored versions of the actual sensor orientations. Further, outlier measurements caused by reverberation are mitigated by a Random Sample Consensus (RANSAC) approach. The experimental results show a mean positioning error of at most 25 cm even in highly reverberant environments.}},
  author       = {{Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{International Workshop on Acoustic Signal Enhancement (IWAENC 2012)}},
  keywords     = {{Unsupervised, geometry calibration, microphone arrays, position self-calibration}},
  title        = {{{Microphone Array Position Self-Calibration from Reverberant Speech Input}}},
  year         = {{2012}},
}

@inproceedings{11726,
  abstract     = {{In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car.}},
  author       = {{Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{7th Workshop on Positioning Navigation and Communication (WPNC 2010)}},
  keywords     = {{altitude measurement unit, barometers, barometric altimeter, barometric error Kalman filter, barometric height estimation, direct cosine matrix, global positioning system, Global Positioning System, GPS device, height information, height measurement, inertial measurement unit, Kalman filters, loosely-coupled error state space Kalman filter, loosely-coupled Kalman-filter, map matching, robust information, robust location estimation, sensor fusion, topographic map information, vertical user position}},
  pages        = {{128--134}},
  title        = {{{Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter}}},
  doi          = {{10.1109/WPNC.2010.5650745}},
  year         = {{2010}},
}