@phdthesis{Marquardt2023,
  author    = {Marquardt, Andr{\´e}},
  title     = {Machine-Learning-Based Identification of Tumor Entities, Tumor Subgroups, and Therapy Options},
  doi       = {10.25972/OPUS-32954},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-329548},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Molecular genetic analyses, such as mutation analyses, are becoming increasingly important in the tumor field, especially in the context of therapy stratification. The identification of the underlying tumor entity is crucial, but can sometimes be difficult, for example in the case of metastases or the so-called Cancer of Unknown Primary (CUP) syndrome. In recent years, methylome and transcriptome utilizing machine learning (ML) approaches have been developed to enable fast and reliable tumor and tumor subtype identification. However, so far only methylome analysis have become widely used in routine diagnostics. The present work addresses the utility of publicly available RNA-sequencing data to determine the underlying tumor entity, possible subgroups, and potential therapy options. Identification of these by ML - in particular random forest (RF) models - was the first task. The results with test accuracies of up to 99\% provided new, previously unknown insights into the trained models and the corresponding entity prediction. Reducing the input data to the top 100 mRNA transcripts resulted in a minimal loss of prediction quality and could potentially enable application in clinical or real-world settings. By introducing the ratios of these top 100 genes to each other as a new database for RF models, a novel method was developed enabling the use of trained RF models on data from other sources. Further analysis of the transcriptomic differences of metastatic samples by visual clustering showed that there were no differences specific for the site of metastasis. Similarly, no distinct clusters were detectable when investigating primary tumors and metastases of cutaneous skin melanoma (SKCM). Subsequently, more than half of the validation datasets had a prediction accuracy of at least 80\%, with many datasets even achieving a prediction accuracy of - or close to - 100\%. To investigate the applicability of the used methods for subgroup identification, the TCGA-KIPAN dataset, consisting of the three major kidney cancer subgroups, was used. The results revealed a new, previously unknown subgroup consisting of all histopathological groups with clinically relevant characteristics, such as significantly different survival. Based on significant differences in gene expression, potential therapeutic options of the identified subgroup could be proposed. Concludingly, in exploring the potential applicability of RNA-sequencing data as a basis for therapy prediction, it was shown that this type of data is suitable to predict entities as well as subgroups with high accuracy. Clinical relevance was also demonstrated for a novel subgroup in renal cell carcinoma. The reduction of the number of genes required for entity prediction to 100 genes, enables panel sequencing and thus demonstrates potential applicability in a real-life setting.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@phdthesis{Sauer2023,
  author    = {Sauer, Christian},
  title     = {Development, Simulation and Evaluation of Mobile Wireless Networks in Industrial Applications},
  doi       = {10.25972/OPUS-29923},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299238},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Manyindustrialautomationsolutionsusewirelesscommunicationandrelyontheavail- ability and quality of the wireless channel. At the same time the wireless medium is highly congested and guaranteeing the availability of wireless channels is becoming increasingly difficult. In this work we show, that ad-hoc networking solutions can be used to provide new communication channels and improve the performance of mobile automation systems. These ad-hoc networking solutions describe different communi- cation strategies, but avoid relying on network infrastructure by utilizing the Peer-to- Peer (P2P) channel between communicating entities. This work is a step towards the effective implementation of low-range communication technologies(e.g. VisibleLightCommunication(VLC), radarcommunication, mmWave communication) to the industrial application. Implementing infrastructure networks with these technologies is unrealistic, since the low communication range would neces- sitate a high number of Access Points (APs) to yield full coverage. However, ad-hoc networks do not require any network infrastructure. In this work different ad-hoc net- working solutions for the industrial use case are presented and tools and models for their examination are proposed. The main use case investigated in this work are Automated Guided Vehicles (AGVs) for industrial applications. These mobile devices drive throughout the factory trans- porting crates, goods or tools or assisting workers. In most implementations they must exchange data with a Central Control Unit (CCU) and between one another. Predicting if a certain communication technology is suitable for an application is very challenging since the applications and the resulting requirements are very heterogeneous. The proposed models and simulation tools enable the simulation of the complex inter- action of mobile robotic clients and a wireless communication network. The goal is to predict the characteristics of a networked AGV fleet. Theproposedtoolswereusedtoimplement, testandexaminedifferentad-hocnetwork- ing solutions for industrial applications using AGVs. These communication solutions handle time-critical and delay-tolerant communication. Additionally a control method for the AGVs is proposed, which optimizes the communication and in turn increases the transport performance of the AGV fleet. Therefore, this work provides not only tools for the further research of industrial ad-hoc system, but also first implementations of ad-hoc systems which address many of the most pressing issues in industrial applica- tions.},
  subject      = {Industrie},
  language  = {en}
}
@phdthesis{Tzschichholz2014,
  author    = {Tzschichholz, Tristan},
  title     = {Relative pose estimation of known rigid objects using a novel approach to high-level PMD-/CCD- sensor data fusion with regard to applications in space},
  isbn      = {978-3-923959-95-2},
  issn      = {1868-7474},
  doi       = {10.25972/OPUS-10391},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-103918},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {In this work, a novel method for estimating the relative pose of a known object is presented, which relies on an application-specific data fusion process. A PMD-sensor in conjunction with a CCD-sensor is used to perform the pose estimation. Furthermore, the work provides a method for extending the measurement range of the PMD sensor along with the necessary calibration methodology. Finally, extensive measurements on a very accurate Rendezvous and Docking testbed are made to evaluate the performance, what includes a detailed discussion of lighting conditions.},
  subject      = {Bildverarbeitung},
  language  = {en}
}
@phdthesis{Wiebusch2016,
  author    = {Wiebusch, Dennis},
  title     = {Reusability for Intelligent Realtime Interactive Systems},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-040-5 (print)},
  doi       = {10.25972/WUP-978-3-95826-041-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-121869},
  school      = {W{\"u}rzburg University Press},
  pages     = {260},
  year      = {2016},
  abstract  = {Software frameworks for Realtime Interactive Systems (RIS), e.g., in the areas of Virtual, Augmented, and Mixed Reality (VR, AR, and MR) or computer games, facilitate a multitude of functionalities by coupling diverse software modules. In this context, no uniform methodology for coupling these modules does exist; instead various purpose-built solutions have been proposed. As a consequence, important software qualities, such as maintainability, reusability, and adaptability, are impeded. Many modern systems provide additional support for the integration of Artificial Intelligence (AI) methods to create so called intelligent virtual environments. These methods exacerbate the above-mentioned problem of coupling software modules in the thus created Intelligent Realtime Interactive Systems (IRIS) even more. This, on the one hand, is due to the commonly applied specialized data structures and asynchronous execution schemes, and the requirement for high consistency regarding content-wise coupled but functionally decoupled forms of data representation on the other. This work proposes an approach to decoupling software modules in IRIS, which is based on the abstraction of architecture elements using a semantic Knowledge Representation Layer (KRL). The layer facilitates decoupling the required modules, provides a means for ensuring interface compatibility and consistency, and in the end constitutes an interface for symbolic AI methods.},
  subject      = {Virtuelle Realit{\"a}t},
  language  = {en}
}