@article{StrohmeierWalterRotheetal.2018,
  author    = {Strohmeier, Michael and Walter, Thomas and Rothe, Julian and Montenegro, Sergio},
  title     = {Ultra-wideband based pose estimation for small unmanned aerial vehicles},
  series = {IEEE Access},
  volume    = {6},
  journal   = {IEEE Access},
  doi       = {10.1109/ACCESS.2018.2873571},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-177503},
  pages     = {57526-57535},
  year      = {2018},
  abstract  = {This paper proposes a 3-D local pose estimation system for a small Unmanned Aerial Vehicle (UAV) with a weight limit of 200 g and a very small footprint of 10 cm×10cm. The system is realized by fusing 3-D position estimations from an Ultra-Wide Band (UWB) transceiver network with Inertial Measurement Unit (IMU) sensor data and data from a barometric pressure sensor. The 3-D position from the UWB network is estimated using Multi-Dimensional Scaling (MDS) and range measurements between the transceivers. The range measurements are obtained using Double-Sided Two-Way Ranging (DS-TWR), thus eliminating the need for an additional clock synchronization mechanism. The sensor fusion is accomplished using a loosely coupled Extended Kalman Filter (EKF) architecture. Extensive evaluation of the proposed system shows that a position accuracy with a Root-Mean-Square Error (RMSE) of 0.20cm can be obtained. The orientation angle can be estimated with an RMSE of 1.93°.},
  language  = {en}
}
@phdthesis{Borrmann2018,
  author    = {Borrmann, Dorit},
  title     = {Multi-modal 3D mapping - Combining 3D point clouds with thermal and color information},
  isbn      = {978-3-945459-20-1},
  issn      = {1868-7474},
  doi       = {10.25972/OPUS-15708},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157085},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Imagine a technology that automatically creates a full 3D thermal model of an environment and detects temperature peaks in it. For better orientation in the model it is enhanced with color information. The current state of the art for analyzing temperature related issues is thermal imaging. It is relevant for energy efficiency but also for securing important infrastructure such as power supplies and temperature regulation systems. Monitoring and analysis of the data for a large building is tedious as stable conditions need to be guaranteed for several hours and detailed notes about the pose and the environment conditions for each image must be taken. For some applications repeated measurements are necessary to monitor changes over time. The analysis of the scene is only possible through expertise and experience. This thesis proposes a robotic system that creates a full 3D model of the environment with color and thermal information by combining thermal imaging with the technology of terrestrial laser scanning. The addition of a color camera facilitates the interpretation of the data and allows for other application areas. The data from all sensors collected at different positions is joined in one common reference frame using calibration and scan matching. The first part of the thesis deals with 3D point cloud processing with the emphasis on accessing point cloud data efficiently, detecting planar structures in the data and registering multiple point clouds into one common coordinate system. The second part covers the autonomous exploration and data acquisition with a mobile robot with the objective to minimize the unseen area in 3D space. Furthermore, the combination of different modalities, color images, thermal images and point cloud data through calibration is elaborated. The last part presents applications for the the collected data. Among these are methods to detect the structure of building interiors for reconstruction purposes and subsequent detection and classification of windows. A system to project the gathered thermal information back into the scene is presented as well as methods to improve the color information and to join separately acquired point clouds and photo series. A full multi-modal 3D model contains all the relevant geometric information about the recorded scene and enables an expert to fully analyze it off-site. The technology clears the path for automatically detecting points of interest thereby helping the expert to analyze the heat flow as well as localize and identify heat leaks. The concept is modular and neither limited to achieving energy efficiency nor restricted to the use in combination with a mobile platform. It also finds its application in fields such as archaeology and geology and can be extended by further sensors.},
  subject      = {Punktwolke},
  language  = {en}
}
@phdthesis{NguyenNgoc2018,
  author    = {Nguyen-Ngoc, Anh},
  title     = {On Performance Assessment of Control Mechanisms and Virtual Components in SDN-based Networks},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-16932},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-169328},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {This dissertation focuses on the performance evaluation of all components of Software Defined Networking (SDN) networks and covers whole their architecture. First, the isolation between virtual networks sharing the same physical resources is investigated with SDN switches of several vendors. Then, influence factors on the isolation are identified and evaluated. Second, the impact of control mechanisms on the performance of the data plane is examined through the flow rule installation time of SDN switches with different controllers. It is shown that both hardware-specific and controller instance have a specific influence on the installation time. Finally, several traffic flow monitoring methods of an SDN controller are investigated and a new monitoring approach is developed and evaluated. It is confirmed that the proposed method allows monitoring of particular flows as well as consumes fewer resources than the standard approach. Based on findings in this thesis, on the one hand, controller developers can refer to the work related to the control plane, such as flow monitoring or flow rule installation, to improve the performance of their applications. On the other hand, network administrators can apply the presented methods to select a suitable combination of controller and switches in their SDN networks, based on their performance requirements},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@phdthesis{Becker2018,
  author    = {Becker, Martin},
  title     = {Understanding Human Navigation using Bayesian Hypothesis Comparison},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-163522},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Understanding human navigation behavior has implications for a wide range of application scenarios. For example, insights into geo-spatial navigation in urban areas can impact city planning or public transport. Similarly, knowledge about navigation on the web can help to improve web site structures or service experience. In this work, we focus on a hypothesis-driven approach to address the task of understanding human navigation: We aim to formulate and compare ideas — for example stemming from existing theory, literature, intuition, or previous experiments — based on a given set of navigational observations. For example, we may compare whether tourists exploring a city walk "short distances" before taking their next photo vs. they tend to "travel long distances between points of interest", or whether users browsing Wikipedia "navigate semantically" vs. "click randomly". For this, the Bayesian method HypTrails has recently been proposed. However, while HypTrails is a straightforward and flexible approach, several major challenges remain: i) HypTrails does not account for heterogeneity (e.g., incorporating differently behaving user groups such as tourists and locals is not possible), ii) HypTrails does not support the user in conceiving novel hypotheses when confronted with a large set of possibly relevant background information or influence factors, e.g., points of interest, popularity of locations, time of the day, or user properties, and finally iii) formulating hypotheses can be technically challenging depending on the application scenario (e.g., due to continuous observations or temporal constraints). In this thesis, we address these limitations by introducing various novel methods and tools and explore a wide range of case studies. In particular, our main contributions are the methods MixedTrails and SubTrails which specifically address the first two limitations: MixedTrails is an approach for hypothesis comparison that extends the previously proposed HypTrails method to allow formulating and comparing heterogeneous hypotheses (e.g., incorporating differently behaving user groups). SubTrails is a method that supports hypothesis conception by automatically discovering interpretable subgroups with exceptional navigation behavior. In addition, our methodological contributions also include several tools consisting of a distributed implementation of HypTrails, a web application for visualizing geo-spatial human navigation in the context of background information, as well as a system for collecting, analyzing, and visualizing mobile participatory sensing data. Furthermore, we conduct case studies in many application domains, which encompass — among others — geo-spatial navigation based on photos from the photo-sharing platform Flickr, browsing behavior on the social tagging system BibSonomy, and task choosing behavior on a commercial crowdsourcing platform. In the process, we develop approaches to cope with application specific subtleties (like continuous observations and temporal constraints). The corresponding studies illustrate the variety of domains and facets in which navigation behavior can be studied and, thus, showcase the expressiveness, applicability, and flexibility of our methods. Using these methods, we present new aspects of navigational phenomena which ultimately help to better understand the multi-faceted characteristics of human navigation behavior.},
  subject      = {Bayes-Verfahren},
  language  = {en}
}
@phdthesis{Furth2018,
  author    = {Furth, Sebastian},
  title     = {Linkable Technical Documentation},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-174185},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {The success of semantic systems has been proven over the last years. Nowadays, Linked Data is the driver for the rapid development of ever new intelligent systems. Especially in enterprise environments semantic systems successfully support more and more business processes. This is especially true for after sales service in the mechanical engineering domain. Here, service technicians need effective access to relevant technical documentation in order to diagnose and solve problems and defects. Therefore, the usage of semantic information retrieval systems has become the new system metaphor. Unlike classical retrieval software Linked Enterprise Data graphs are exploited to grant targeted and problem-oriented access to relevant documents. However, huge parts of legacy technical documents have not yet been integrated into Linked Enterprise Data graphs. Additionally, a plethora of information models for the semantic representation of technical information exists. The semantic maturity of these information models can hardly be measured. This thesis motivates that there is an inherent need for a self-contained semantification approach for technical documents. This work introduces a maturity model that allows to quickly assess existing documentation. Additionally, the approach comprises an abstracting semantic representation for technical documents that is aligned to all major standard information models. The semantic representation combines structural and rhetorical aspects to provide access to so called Core Documentation Entities. A novel and holistic semantification process describes how technical documents in different legacy formats can be transformed to a semantic and linked representation. The practical significance of the semantification approach depends on tools supporting its application. This work presents an accompanying tool chain of semantification applications, especially the semantification framework CAPLAN that is a highly integrated development and runtime environment for semantification processes. The complete semantification approach is evaluated in four real-life projects: in a spare part augmentation project, semantification projects for earth moving technology and harvesting technology, as well as an ontology population project for special purpose vehicles. Three additional case studies underline the broad applicability of the presented ideas.},
  subject      = {Linked Data},
  language  = {en}
}
@phdthesis{Muehlberger2018,
  author    = {M{\"u}hlberger, Clemens},
  title     = {Design of a Self-Organizing MAC Protocol for Dynamic Multi-Hop Topologies},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158788},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Biologically inspired self-organization methods can help to manage the access control to the shared communication medium of Wireless Sensor Networks. One lightweight approach is the primitive of desynchronization, which relies on the periodic transmission of short control messages - similar to the periodical pulses of oscillators. This primitive of desynchronization has already been successfully implemented as MAC protocol for single-hop topologies. Moreover, there are also some concepts of such a protocol formulti-hop topologies available. However, the existing implementations may handle just a certain class of multi-hop topologies or are not robust against topology dynamics. In addition to the sophisticated access control of the sensor nodes of a Wireless Sensor Network in arbitrary multi-hop topologies, the communication protocol has to be lightweight, applicable, and scalable. These characteristics are of particular interest for distributed and randomly deployed networks (e.g., by dropping nodes off an airplane). In this work we present the development of a self-organizing MAC protocol for dynamic multi-hop topologies. This implies the evaluation of related work, the conception of our new communication protocol based on the primitive of desynchronization as well as its implementation for sensor nodes. As a matter of course, we also analyze our realization with regard to our specific requirements. This analysis is based on several (simulative as well as real-world) scenarios. Since we are mainly interested in the convergence behavior of our protocol, we do not focus on the "classical" network issues, like routing behavior or data rate, within this work. Nevertheless, for this purpose we make use of several real-world testbeds, but also of our self-developed simulation framework. According to the results of our evaluation phase, our self-organizing MAC protocol for WSNs, which is based on the primitive of desynchronization, meets all our demands. In fact, our communication protocol operates in arbitrary multi-hop topologies and copes well with topology dynamics. In this regard, our protocol is the first and only MAC protocol to the best of our knowledge. Moreover, due to its periodic transmission scheme, it may be an appropriate starting base for additional network services, like time synchronization or routing.},
  language  = {en}
}
@article{RingLandesHotho2018,
  author    = {Ring, Markus and Landes, Dieter and Hotho, Andreas},
  title     = {Detection of slow port scans in flow-based network traffic},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {9},
  doi       = {10.1371/journal.pone.0204507},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-226305},
  pages     = {e0204507, 1-18},
  year      = {2018},
  abstract  = {Frequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms.},
  language  = {en}
}
@phdthesis{DinhXuan2018,
  author    = {Dinh-Xuan, Lam},
  title     = {Quality of Experience Assessment of Cloud Applications and Performance Evaluation of VNF-Based QoE Monitoring},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-16918},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-169182},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {In this thesis various aspects of Quality of Experience (QoE) research are examined. The work is divided into three major blocks: QoE Assessment, QoE Monitoring, and VNF Performance Evaluation. First, prominent cloud applications such as Google Docs and a cloud-based photo album are explored. The QoE is characterized and the influence of packet loss and delay is studied. Afterwards, objective QoE monitoring for HTTP Adaptive Video Streaming (HAS) in the cloud is investigated. Additionally, by using a Virtual Network Function (VNF) for QoE monitoring in the cloud, the feasibility of an interworking of Network Function Virtualization (NFV) and cloud paradigm is evaluated. To this end, a VNF that exploits deep packet inspection technique was used to parse the video traffic. An algorithm is then designed accordingly to estimate video quality and QoE based on network and application layer parameters. To assess the accuracy of the estimation, the VNF is measured in different scenarios under different network QoS and the virtual environment of the cloud architecture. The insights show that the different geographical deployments of the VNF influence the accuracy of the video quality and QoE estimation. Various Service Function Chain (SFC) placement algorithms have been proposed and compared in the context of edge cloud networks. On the one hand, this research is aimed at cloud service providers by providing methods for evaluating QoE for cloud applications. On the other hand, network operators can learn the pitfalls and disadvantages of using the NFV paradigm for such a QoE monitoring mechanism.},
  subject      = {Quality of Experience},
  language  = {en}
}
@phdthesis{Herbst2018,
  author    = {Herbst, Nikolas Roman},
  title     = {Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164314},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {A key functionality of cloud systems are automated resource management mechanisms at the infrastructure level. As part of this, elastic scaling of allocated resources is realized by so-called auto-scalers that are supposed to match the current demand in a way that the performance remains stable while resources are efficiently used. The process of rating cloud infrastructure offerings in terms of the quality of their achieved elastic scaling remains undefined. Clear guidance for the selection and configuration of an auto-scaler for a given context is not available. Thus, existing operating solutions are optimized in a highly application specific way and usually kept undisclosed. The common state of practice is the use of simplistic threshold-based approaches. Due to their reactive nature they incur performance degradation during the minutes of provisioning delays. In the literature, a high-number of auto-scalers has been proposed trying to overcome the limitations of reactive mechanisms by employing proactive prediction methods. In this thesis, we identify potentials in automated cloud system resource management and its evaluation methodology. Specifically, we make the following contributions: We propose a descriptive load profile modeling framework together with automated model extraction from recorded traces to enable reproducible workload generation with realistic load intensity variations. The proposed Descartes Load Intensity Model (DLIM) with its Limbo framework provides key functionality to stress and benchmark resource management approaches in a representative and fair manner. We propose a set of intuitive metrics for quantifying timing, stability and accuracy aspects of elasticity. Based on these metrics, we propose a novel approach for benchmarking the elasticity of Infrastructure-as-a-Service (IaaS) cloud platforms independent of the performance exhibited by the provisioned underlying resources. We tackle the challenge of reducing the risk of relying on a single proactive auto-scaler by proposing a new self-aware auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation techniques to calculate the required resource consumption per work unit without the need for a detailed application instrumentation. It can also leverage application knowledge by solving product-form queueing networks used to derive optimized scaling actions. The Chameleon approach is first in resolving conflicts between reactive and proactive scaling decisions in an intelligent way. We are confident that the contributions of this thesis will have a long-term impact on the way cloud resource management approaches are assessed. While this could result in an improved quality of autonomic management algorithms, we see and discuss arising challenges for future research in cloud resource management and its assessment methods: The adoption of containerization on top of virtual machine instances introduces another level of indirection. As a result, the nesting of virtual resources increases resource fragmentation and causes unreliable provisioning delays. Furthermore, virtualized compute resources tend to become more and more inhomogeneous associated with various priorities and trade-offs. Due to DevOps practices, cloud hosted service updates are released with a higher frequency which impacts the dynamics in user behavior.},
  subject      = {Cloud Computing},
  language  = {en}
}
@phdthesis{Koch2018,
  author    = {Koch, Rainer},
  title     = {Sensor Fusion for Precise Mapping of Transparent and Specular Reflective Objects},
  isbn      = {978-3-945459-25-6},
  doi       = {10.25972/OPUS-16346},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-163462},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Almost once a week broadcasts about earthquakes, hurricanes, tsunamis, or forest fires are filling the news. While oneself feels it is hard to watch such news, it is even harder for rescue troops to enter such areas. They need some skills to get a quick overview of the devastated area and find victims. Time is ticking, since the chance for survival shrinks the longer it takes till help is available. To coordinate the teams efficiently, all information needs to be collected at the command center. Therefore, teams investigate the destroyed houses and hollow spaces for victims. Doing so, they never can be sure that the building will not fully collapse while they are inside. Here, rescue robots are welcome helpers, as they are replaceable and make work more secure. Unfortunately, rescue robots are not usable off-the-shelf, yet. There is no doubt, that such a robot has to fulfil essential requirements to successfully accomplish a rescue mission. Apart from the mechanical requirements it has to be able to build a 3D map of the environment. This is essential to navigate through rough terrain and fulfil manipulation tasks (e.g. open doors). To build a map and gather environmental information, robots are equipped with multiple sensors. Since laser scanners produce precise measurements and support a wide scanning range, they are common visual sensors utilized for mapping. Unfortunately, they produce erroneous measurements when scanning transparent (e.g. glass, transparent plastic) or specular reflective objects (e.g. mirror, shiny metal). It is understood that such objects can be everywhere and a pre-manipulation to prevent their influences is impossible. Using additional sensors also bear risks. The problem is that these objects are occasionally visible, based on the incident angle of the laser beam, the surface, and the type of object. Hence, for transparent objects, measurements might result from the object surface or objects behind it. For specular reflective objects, measurements might result from the object surface or a mirrored object. These mirrored objects are illustrated behind the surface which is wrong. To obtain a precise map, the surfaces need to be recognised and mapped reliably. Otherwise, the robot navigates into it and crashes. Further, points behind the surface should be identified and treated based on the object type. Points behind a transparent surface should remain as they represent real objects. In contrast, Points behind a specular reflective surface should be erased. To do so, the object type needs to be classified. Unfortunately, none of the current approaches is capable to fulfil these requirements. Therefore, the following thesis addresses this problem to detect transparent and specular reflective objects and to identify their influences. To give the reader a start up, the first chapters describe: the theoretical background concerning propagation of light; sensor systems applied for range measurements; mapping approaches used in this work; and the state-of-the-art concerning detection and identification of transparent and specular reflective objects. Afterwards, the Reflection-Identification-Approach, which is the core of subject thesis is presented. It describes 2D and a 3D implementation to detect and classify such objects. Both are available as ROS-nodes. In the next chapter, various experiments demonstrate the applicability and reliability of these nodes. It proves that transparent and specular reflective objects can be detected and classified. Therefore, a Pre- and Post-Filter module is required in 2D. In 3D, classification is possible solely with the Pre-Filter. This is due to the higher amount of measurements. An example shows that an updatable mapping module allows the robot navigation to rely on refined maps. Otherwise, two individual maps are build which require a fusion afterwards. Finally, the last chapter summarizes the results and proposes suggestions for future work.},
  subject      = {laserscanner},
  language  = {en}
}