@mastersthesis { Asl2021,
     author        = { Amanuel Aslan },
     title         = { Anpassung und Erweiterung eines Verfahrens zur Vorverarbeitung bin{"a}rer Kantenbilder },
     month         = { January },
     year          = { 2022 },
     school        = { Paderborn University },
     type          = { Bachelor's thesis },
     abstract      = { Im Rahmen dieser Bachelorarbeit soll ein bestehendes Verfahren zur echtzeitf{"a}higen Vorverarbeitung bin{"a}rer Kantenbilder reimplementiert, angepasst und um ein graphisches Werkzeug zur Kontrolle von Mehrdeutigkeiten erweitert werden. Das bestehende Verfahren wurde im GET Lab entwickelt und arbeitet im Wesentlichen rekursiv. Das Verfahren liest bin{"a}re Kantenbilder ein und bereitet die Kantenbilder zur weiteren Analyse vor. Einige Analyseverfahren setzen daf{"u}r zusammenh{"a}ngende Kantenverl{"a}ufe voraus. Jedoch ergeben sich, wenn sich Kanten ber{"u}hren oder kreuzen, mehrdeutige Verl{"a}ufe und entsprechende Mehrdeutigkeiten. Das bestehende Verfahren kann nur einige solcher Mehrdeutigkeiten verarbeiten. Durch die Anpassung und Erweiterung des Verfahrens sollen alle Kombinationen von Mehrdeutigkeiten behandelt werden k{"o}nnen. Da eine vollst{"a}ndig automatisierte Behandlung der Mehrdeutigkeiten in vielen F{"a}llen schwierig ist, wird ein Werkzeug entwickelt, mit dessen Hilfe die Mehrdeutigkeiten durch die Benutzereingaben gezielt verarbeitet werden k{"o}nnen. }
}

@book { HBK22,
     author        = { Oliver Haas and Ludwig Brabetz and Christian Koppe },
     title         = { Grundgebiete der Elektrotechnik 1: Gleichstromnetze, Operationsverst{"a}rkerschaltungen, elektrische und magnetische Felder },
     month         = { January },
     year          = { 2022 },
     edition       = { 13., korr. Auflage },
     publisher     = { De Gruyter },
     isbn          = { 978-3-11-063154-8 }
}

@book { KSW22,
     author        = { Ralf R{"u}diger Kories and Heinz Schmidt-Walter },
     title         = { Taschenbuch der Elektrotechnik: Grundlagen und Elektronik },
     month         = { January },
     year          = { 2022 },
     edition       = { 12. Auflage },
     publisher     = { Europa-Lehrmittel Verlag },
     isbn          = { 978-3-8085-5866-9 },
     abstract      = { Das Nachschlagewerk f{"u}r die Grundlagen der Elektrotechnik und Elektronik, das weit mehr als eine Formelsammlung bietet. Es behandelt die Gebiete Gleichstrom, elektrische und magnetische Felder, Wechselstrom und Drehstrom, Stromversorgungen. Neben Kapiteln zu den Themen Elektronik, Digitaltechnik, Schaltzeichen, Grundlagen der elektrischen Messtechnik und Signale und Systeme enth{"a}lt das Werk einen Abschnitt zur Laplace-Transformation sowie Tabellen zu Grundlagen, Elektrotechnik und Elektronik, Gr{"o}{\ss}en und Ma{\ss}einheiten, eine Formelsammlung sowie deutsch/englische Fachbegriffe. Beibehalten werden die kompakte Darstellung, die praktische Griffleiste und die farbigen Leseb{"a}ndchen. Fehlerkorrekturen und zahlreiche kleine Erg{"a}nzungen halten dieses Referenzwerk auf dem neuesten Stand. Zielgruppe: Studierende und Lehrende der Fachrichtungen Elektrotechnik, Nachrichtentechnik, Technische Informatik, der allgemeinen Ingenieurswissenschaften und der Naturwissenschaften; Berufspraktiker in Industrie und Wirtschaft. }
}

@book { Leo22,
     author        = { Marco Leone },
     title         = { Elektrische und magnetische Felder - Vom Coulomb-Gesetz bis zu Maxwel's Feldgleichungen },
     month         = { January },
     year          = { 2022 },
     publisher     = { De Gruyter },
     isbn          = { 978-3-11-076815-2 }
}

@mastersthesis { Shukla2022,
     author        = { Asish Kumar Shukla },
     title         = { Efficient 3D Human Pose Estimation on Mobile Devices },
     month         = { September },
     year          = { 2022 },
     school        = { Paderborn University },
     type          = { Master's Thesis },
     abstract      = { The recent advances in deep convolutional neural networks have shown successful results for human pose and activity classification in videos. It has also shown promising results in the classification of diving actions. However, the accurate estimation of the diving pose in the real-time video stream is still challenging due to the unconstrained environment and complex maneuvers. The thesis work primarily focuses on a weakly supervised model for learning 3D human motion dynamics for diving sports. To this end, we use three models to estimate 2D and 3D human pose and human shape. For the 2D pose, we use the BlazePose model. BlazePose first learns heatmap from 2D image and regresses 2D pose from the heatmap. For the 3D pose, we use the MobileHumanPose model. MobileHumanPose estimates 3D human pose directly from a 2D image using a volumetric heatmap. For shape estimation, we use the I2L-Meshnet model. I2L-meshnet first estimates the 3D pose, then using the 3D pose, it estimates the shape of the human. The thesis aims to develop a model that can estimate 2D and 3D human pose on a mobile device in real-time. To this end, we use BlazePose and MobileHumanPose because they both are light-weight models, but without much compromise on the accuracy of the model. Although I2L-Meshnet is a large model, we investigate light-weight backbone networks to estimate the shape. The model performed well on pose estimation task but failed to estimate the accurate shape and global orientation of the diver in the videos. We created a demonstrator tool to integrate BlazePose and MobileHumaPose into mobile devices. We successfully estimated 2D human pose in the android device using BlazePose and 3D human pose in the iOS device using MobileHumanPose. Diving sports has complex poses, despite the challenging poses our mobile models achieve over 95% accuracy with fine tuned model and around 85% with generic model. }
}

@mastersthesis { Suresh2022,
     author        = { Shivaram Goutham Suresh },
     title         = { Autonomous Exploration Using 3D Maps Semantically Augmented By Deep Learning },
     month         = { May },
     year          = { 2022 },
     school        = { Paderborn University },
     type          = { Master's thesis },
     abstract      = { Autonomous exploration is a well-known problem in robotics and has proven to be highly relevant in urban search and rescue (USAR) scenarios. For a robot to explore its surroundings, a map of the current environment is necessary. Currently, the rescue robot GETjag can build a detailed 3D map of the environment in the form of a point cloud. However, this map does not contain semantic information about the scene. For example, it is not possible for the robot to distinguish a door from a wall. Since such semantic knowledge will open up new possibilities, this thesis aims to develop a system that can provide scene understanding by adding semantic information to a 3D map and use this semantically enriched map to enhance the exploration behavior. The system is intended to be validated by testing the point-wise classification and exploration behavior in simulated and real-world environments.
 }
}

@mastersthesis { Wes2021,
     author        = { Marvin Westerholz },
     title         = { Mobile Robot Positioning for Optimal Object Interaction },
     month         = { July },
     year          = { 2022 },
     school        = { Paderborn University },
     type          = { Master's thesis },
     abstract      = { In an attempt to successfully carry out tasks with mobile robots on target objects, the choice of a suitable positioning is important. In this work, a framework which enables mobile robots to handle objects autonomously is created. One problem to be solved is to find a set of robot positions that allows to reach all operating points without affecting the stability of the robot. In addition, a suitable method has to be selected to classify objects to be handled and determine their position. The new framework should also integrate the existing solutions for 3D map creation, path planning and navigation. Finally, tests and evaluation can be carried out  using the rescue robot GETjag and different objects in both simulation and real environments. }
}

@mastersthesis { Yan2021,
     author        = { Qinyuan Yang },
     title         = { CNN Training for Detecting Curvature Extrema of Object Contours in Real Images },
     month         = { October },
     year          = { 2022 },
     school        = { Paderborn University },
     type          = { Master's thesis },
     abstract      = { Curvature extrema along contours and their surrounding image regions are interesting local image features. Their detection in real images generally requires the following main steps: edge detection, edge refinement, edge tracing, and curvature extrema detection. Due to the complexity of this process, the objective of this work was to develop an equivalent detector based on a Convolutional Neural Network (CNN). The idea is to use real images as inputs and to directly determine score maps as outputs, which describe how likely each pixel is a keypoint. This requires two steps: generating a training dataset and integrating an appropriate CNN architecture. High-quality edge images and their corresponding real images have been used from a public dataset. The dataset has then been processed with an edge tracing and curvature extrema detection method from GET Lab to generate the training data. Here, one of the key challenges was to determine meaningful and coherent object contours. This is also the main contribution of this work. Different CNN architectures have then been tested and compared. }
}