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Institute
- Fakultät Architektur und Gestaltung (27)
- Fakultät Bauingenieurwesen (77)
- Fakultät Elektrotechnik und Informationstechnik (23)
- Fakultät Informatik (59)
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- Institut für Angewandte Forschung - IAF (44)
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- Institut für Optische Systeme - IOS (13)
- Institut für Strategische Innovation und Technologiemanagement - IST (2)
100 Jahre Türkische Republik
(2023)
Aktuelle Fachvorträge aus dem internationalen Spektrum des Bauingenieurwesens an der HTWG Konstanz, anlässlich des 100-jährigen Bestehens der Fakultät BI. Themenbereiche: Bauerhaltung und Bausanierung, Geotechnik, Konstruktiver Ingenieurbau, Höhepunkte des Hochbaus im 21. Jahrhundert, Verkehrswesen, Wasserwirtschaft, Wirtschaftsingenieurwesen. Gefördert wurde die Veranstaltung von: ADK Modulraum GmbH ; Architektenkammer BW ; Bauen mit Stahl ; Beton Marketing Süd ; Bund Deutscher Architekten BDA ; Institut Feuerverzinken ; Form TL - Ingenieure für Tragwerk und Leichtbau GmbH ; OTT Ziegel Pfullendorf ; Stahlbauzentrum Schweiz ; Euro Poles Pfleiderer ; Ingenieurk@mmer Baden-Württemberg
33 Baukultur Rezepte
(2017)
Das vorliegende Buch ist entstanden im Rahmen des Forschungsprojekts „Baukultur konkret“ im Forschungsprogramm Experimenteller Wohnungs- und Städtebau (ExWoSt) des Bundesinstituts für Bau-, Stadt- und Raumforschung BBSR im Auftrag des Bundesministeriums für Umwelt, Naturschutz, Bau und Reaktorsicherheit BMUB.
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.
This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements.
To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates.
In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis.
The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.
The McEliece cryptosystem is a promising candidate for post-quantum public-key encryption. In this work, we propose q-ary codes over Gaussian integers for the McEliece system and a new channel model. With this one Mannheim error channel, errors are limited to weight one. We investigate the channel capacity of this channel and discuss its relation to the McEliece system. The proposed codes are based on a simple product code construction and have a low complexity decoding algorithm. For the one Mannheim error channel, these codes achieve a higher error correction capability than maximum distance separable codes with bounded minimum distance decoding. This improves the work factor regarding decoding attacks based on information-set decoding.
If the process contains a delay (dead time), the Nyquist criterion is well suited to derive a PI or PID tuning rule because the delay is taken into account without approximation. The tuning of the speed of the closed loop enters naturally by the crossover frequency. The goal of robustness and performance is translated into the phase margin.
Using multi-camera matching techniques for 3d reconstruction there is usually the trade-off between the quality of the computed depth map and the speed of the computations. Whereas high quality matching methods take several seconds to several minutes to compute a depth map for one set of images, real-time methods achieve only low quality results. In this paper we present a multi-camera matching method that runs in real-time and yields high resolution depth maps. Our method is based on a novel multi-level combination of normalized cross correlation, deformed matching windows based on the multi-level depth map information, and sub-pixel precise disparity maps. The whole process is implemented completely on the GPU. With this approach we can process four 0.7 megapixel images in 129 milliseconds to a full resolution 3d depth map. Our technique is tailored for the recognition of non-technical shapes, because our target application is face recognition.
This research project has been awarded as part of the research competition organized by Connect2Recover, which is a global initiative by the International Telecommunication Union (ITU) with the priority of reinforcing and strengthening the digital infrastructure and ecosystems of developing countries. Carried out by an international and transdisciplinary research consortium, the project sets out to analyze the prospects of digital federation and data sharing within the context of Botswana. Considering the country’s stage of economic and digital development, the project team identified Botswana’s smallholder agricultural sector as the most important area of digital transformation given the development need of the country’s primary sector.
Derived from semi-structured interviews, a focus group, as well as secondary research, the project team developed a digital transformation roadmap based on three development stages: (a) crowdfarming pilot, (b) crowdfarming marketplace, and (c) digital ecosystem for smallholder agriculture. Based on a detailed review of Botswana’s smallholder agriculture and the government’s digitalization strategy, the report envisions each phase, especially the pilot project, in terms of a minimal viable product. This is to consider the low level of digital penetration of Botswana’s primary sector, while providing an incentive to connect smallholders with consumers, traders, and retailers.
The project team has been successful in receiving commitment from actual smallholder farmers, the farmer association and government, as well as support for the idea of developing a crowdfarming marketplace as a novel production model and, eventually, a digital agriculture ecosystem for smallholder farmers, livestock producers, and agricultural technology companies and start-ups. The report is a proposal for a phase-one pilot project with the objective to advance smallholder agribusiness in Botswana.