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This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.
Mapping of tree seedlings is useful for tasks ranging from monitoring natural succession and regeneration to effective silvicultural management. Development of methods that are both accurate and cost-effective is especially important considering the dramatic increase in tree planting that is required globally to mitigate the impacts of climate change. The combination of high-resolution imagery from unmanned aerial vehicles and object detection by convolutional neural networks (CNNs) is one promising approach. However, unbiased assessments of these models and methods to integrate them into geospatial workflows are lacking. In this study, we present a method for rapid, large-scale mapping of young conifer seedlings using CNNs applied to RGB orthomosaic imagery. Importantly, we provide an unbiased assessment of model performance by using two well-characterised trial sites together containing over 30,000 seedlings to assemble datasets with a high level of completeness. Our results showed CNN-based models trained on two sites detected seedlings with sensitivities of 99.5% and 98.8%. False positives due to tall weeds at one site and naturally regenerating seedlings of the same species led to slightly lower precision of 98.5% and 96.7%. A model trained on examples from both sites had 99.4% sensitivity and precision of 97%, showing applicability across sites. Additional testing showed that the CNN model was able to detect 68.7% of obscured seedlings missed during the initial annotation of the imagery but present in the field data. Finally, we demonstrate the potential to use a form of weakly supervised training and a tile-based processing chain to enhance the accuracy and efficiency of CNNs applied to large, high-resolution orthomosaics.
Creative industry and cultural tourism destination Lake Constance - a media discourse analysis
(2020)
The following media discourse analysis examines the news media coverage of four regional online newspapers, about the topics “creative industries” and “cultural tourism” at Lake Constance region in the period from 2006 until 2016. The results show that, besides event-relater reporting, there is currently no vibrant media discourse on the topics “creative industries” and “cultural tourism”. Even though the image of the Lake Constance region is heavily influenced by tourism, “cultural tourism” also plays a secondary role when it comes to regional news reporting. Moreover, discourses do not overlap and thus no synergies within the local media discourse are formed. This result is relevant for the regional tourism development, because the cooperation between “creative industries” and “cultural tourism” creates opportunities such as the expansion of the tourism offer and an extension of the tourist season. To activate unused opportunities at the different destinations of the region, a supra-regional visibility of the sector “creative industries” should be developed and the cooperation of the sector with local stakeholders of cultural tourism should be promoted.
A conceptual framework for indigenous ecotourism projects – a case study in Wayanad, Kerala, India
(2020)
This paper analyses indigenous ecotourism in the Indian district of Wayanad, Kerala, using a conceptual framework based on a PATA 2015 study on indigenous tourism that includes the criteria: human rights, participation, business and ecology. Detailed indicator sets for each criterion are applied to a case study of the Priyadarshini Tea Environs with a qualitative research approach addressing stakeholders from the public sector, non-governmental organisations, academia, tour operators and communities including Adivasi and non-Adivasi. In-depth interviews were supported by participant and non-participant observations. The authors adapted this framework to the needs of the case study and consider that this modified version is a useful tool for academics and practitioners wishing to evaluate and develop indigenous ecotourism projects. The results show that the Adivasi involved in the Priyadarshini Tea Environs project benefit from indigenous ecotourism. But they could profit more if they had more involvement in and control of the whole tourism value chain.
The Lake Constance region is due to its scenic attractiveness one of the most visited destinations in German-speaking countries. Scenic attractiveness as well as so-called landscape stereotypes also play a decisive role in tourism marketing. Tour operators reproduce supra-individual landscape concepts and establish mental geographies that ultimately influence the choice of destinations. A growing trend in tourism is the emergence of creative narratives in tourism marketing and tourism offers induced by creative companies. By means of a discourse-analytical investigation, whose theoretical and conceptual frame of reference is the hegemony and discourse theory of Laclau and Mouffe (1985), recurring landscape stereotypes are identified in tourist promotional material for the destination Bodensee. Based on these results as well as expert interviews with regional tourism stakeholders, a discussion of the creative economic potential for regional tourism marketing will take place. The investigation shows that these potentials are currently not being exhausted. At the same time, creative tourism can help a rural region, such as Lake Constance, to position itself as an alternative to city tourism, while at the same time addressing the lucrative target group 60plus.
Durch die zunehmende Vernetzung und den Anstieg von eingesetzter Hard- und Software hat sich die Komplexität der Unternehmensarchitektur von Unternehmen über die Jahre stetig erhöht. Das Aufkommen nutzerfreundlicher Informationstechnologie (IT)-Lösungen befähigt außerdem Fachbereiche, IT innovativ einzusetzen. Dies erhöht die Heterogenität und damit nochmals die Komplexität der Unternehmensarchitektur. Darüber hinaus treibt dieser IT-Einsatz die Digitalisierung in den Unternehmen maßgeblich voran. Dies wirft die Frage auf, ob Unternehmen überhaupt noch eine Relevanz in der Reduktion der Komplexität durch IT-Integration sehen oder ob dies vor dem Hintergrund der Digitalisierung schon ein alter Hut ist. Experteninterviews und eine qualitative Datenanalyse zeigen, dass IT-Integration und Digitalisierung keine disjunkten Phänomene sind, sondern sich gegenseitig beeinflussen. Die Ergebnisse betonen, wie unterschiedlich der Begriff aufgefasst werden kann und dass die einheitliche Nutzung damit essenziell ist. Darüber hinaus zeigen sie, dass Digitalisierung einerseits Treiber der IT-Integration ist, andererseits aber auch die Möglichkeiten zur Umsetzung verändert. Dabei ist die Integrationsentscheidung durch die Vielzahl an Vor- und Nachteile komplex. Fachbereichs-IT ist selten explizites Ziel von IT-Integrationsprojekten. Der Beitrag zeigt den wissenschaftlichen Forschungsbedarf in neuen technologischen Möglichkeiten zur IT-Integration und in der Balance von Flexibilität und IT-Integration in der Unternehmensarchitektur. Er beleuchtet, dass eine gemeinsame Sprache die Basis für IT-Integrationsprojekte ist und dass eine Kultur, in der Fachbereiche aktiv an IT-Integrationsentscheidungen teilhaben, das Ziel eines jeden Unternehmens sein sollte. Insgesamt zeigen die Analysen, dass IT-Integration noch lange kein alter Hut, sondern, im Gegenteil, brandaktuell ist.
Kleine und mittelständische Unternehmen (KMU) sind bekannt für ihre Innovationskraft und bilden das Rückgrat der deutschen Wirtschaft. Wie Studien zeigen sind sie in Bezug auf Compliance-Maßnahmen im Vergleich zu
kapitalmarktorientierten Unternehmen jedoch im Rückstand. Eine gesonderte Betrachtung der IT-Compliance erfolgt dabei in den Studien in der Regel nicht. Auch wenn zu den Gründen und Motiven fehlender IT-Compliance-Strukturen in KMU kaum Forschungsergebnisse vorliegen, zeigen doch die vielen Publikationen, die sich mit Teilaspekten von Compliance und KMU beschäftigen, dass Handlungsbedarf besteht. Insbesondere die aktuellen Veränderungen unter dem Stichwort Digitalisierung deuten auf eine gesteigerte Bedeutung von IT-Compliance-Maßnahmen vor allem in mittelständischen Unternehmen. In dieser Arbeit sollen daher mithilfe einer Literaturrecherche die aktuell behandelten Themen in Bezug auf IT-Compliance und KMU analysiert sowie aktuelle Themenschwerpunkte herausgearbeitet werden.
Totally nonnegative matrices, i.e., matrices having all their minors nonnegative, and matrix intervals with respect to the checkerboard partial order are considered. It is proven that if the two bound matrices of such a matrix interval are totally nonnegative and satisfy certain conditions, then all matrices from this interval are also totally nonnegative and satisfy the same conditions.