Bachelor Thesis
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This thesis explores a 3D object detection and pose estimation approach based on the point pair features method presented by Drost et. al. [Dro+10]. While pose estimation methods have shown good improvements, they still remain a crucial problem on the computer vision field. In this work, we implemented a program that takes point cloud scenes as input and returns the detected object with their estimated pose. The program fully covers an object detection pipeline by processing 3D models during an offline phase, extracting their point pair features and creating a global descriptor out of them. During an online phase, the same features are extracted from a point cloud scene and are matched to the model features. After the voting scheme, potential poses of the object are retrieved. The poses end being clustered together and post-processed to finally deliver a result. The program was tested using simulated and real data. We evaluate these tests and present the final results, by discussing the achieved accuracy of the detections and the estimated poses.
Social media platforms such as Twitter or Reddit allow users almost unrestricted access to publish their opinions on recent events or discuss trending topics. While the majority of users approach these platforms innocently, some groups have set their mind on spreading misinformation and influencing or manipulating public opinion. These groups disguise as native users from various countries to spread frequently manufactured articles, strong polarizing opinions in the political spectrum and possibly become providers of hate-speech or extremely political positions. This thesis aims to implement an AutoML pipeline for identifying second language speakers from English social media texts. We investigate style differences of text in different topics and across the platforms Reddit and Twitter, and analyse linguistic features. We employ feature-based models with datasets from Reddit, which include mostly English conversation from European users, and Twitter, which was newly created by collecting English tweets from selected trending topics in different countries. The pipeline classifies language family, native language and origin (Native or non-Native English speakers) of a given textual input. We evaluate the resulting classifications by comparing prediction accuracy, precision and F1 scores of our classification pipeline to traditional machine learning processes. Lastly, we compare the results from each dataset and find differences in language use for topics and platforms. We obtained high prediction accuracy for all categories on the Twitter dataset and observed high variance in features such as average text length especially for Balto-Slavic countries.
Molecular dynamics (MD) as a field of molecular modelling has great potential to revolutionize our knowledge and understanding of complex macromolecular structures. Its field of application is huge, reaching from computational chemistry and biology over material sciences to computer-aided drug design. This thesis on one hand provides insights into the underlying physical concepts of molecular dynamics simulations and how they are applied in the MD algorithm, and also briefly illustrates different approaches, as for instance the molecular mechanics and molecular quantum mechanics approaches.
On the other hand an own all-atom MD algorithm is implemented utilizing and simplifying a version of the molecular mechanics based AMBER force field published by \big[\cite{cornell1995second}\big]. This simulation algorithm is then used to show by the example of oxytocin how individual energy terms of a force field function. As a result it has been observed, that applying the bond stretch forces alone caused the molecule to be compacted first in certain regions and then as a whole, and that with adding more energy terms the molecule got to move with increasing flexibility.
The Internet of Things is still one of the most relevant topics in the field of economics and research powered by the increasing demand of innovative services. Cost reductions in manufacturing of IoT hardware and the development of completely new communication ways has led to the point of bil-lions of devices connected to the internet. But in order to rule this new IoT landscape a standardized solution to conquer these challenges must be developed, the IoT Architecture.
This thesis examines the structure, purpose and requirements of IoT Architecture Models in the global IoT landscape and proposes an overview across the selected ones. For that purpose, a struc-tured literature analysis on this topic is conducted within this thesis, including an analysis on three existing research approaches trying to frame this topic and a tool supported evaluation of IoT Archi-tecture literature with over 200 accessed documents.
Furthermore, a coding of literature with the help of the specialised coding tool ATLAS.ti 8 is conduct-ed on 30 different IoT Architecture Models. In a final step these Architecture Models are categorized and compared to each other showing that the environment of IoT and its Architectures gets even more complex the further the research goes.
The development of a game engine is considered a non-trivial problem. [3] The architecture of such simulation software must be able to manage large amounts of simulation objects in real-time while dealing with “crosscutting concerns” [3,p. 36] between subsystems. The use of object oriented paradigms to model simulation objects in class hierarchies has been reported as incompatible with constantly changing demands during game development [2, p. 9], resulting in anti-patterns and eventual, messy refactoring.[13]
Alternative architectures using data oriented paradigms revolving around object composition and aggregation have been proposed as a result. [13, 9, 1, 11]
This thesis describes the development of such an architecture with the explicit goals to be simple, inherently compatible with data oriented design, and to make reasoning about performance characteristics possible. Concepts are formally defined to help analyze the problem and evaluate results. A functional implementation of the architecture is presented together with use cases common to simulation software.
The mitral valve is one of four human heart valves. It is located in the left heart and acts as a unidirectional passageway for blood between the left atrium and the left ventricle. A correctly functioning mitral valve prevents a backflow of blood into the pulmonary circulation (lungs) and thus constitutes a vital part of the cardiac cycle. Pathologies of the mitral valve can manifest in a variety of symptoms with severity ranging from chest pain and fatigue to pulmonary edema (fluid accumulation in the tissue and air space of lungs), which may ultimately cause respiratory failure.
Malfunctioning mitral valves can be restored through complex surgical interventions, which greatly benefit from intensive planning and pre-operative analysis. Visualization techniques provide a possibility to enhance such preparation processes and can also facilitate post-operative evaluation. The work at hand extends current research in this field, building upon patient-specific mitral valve segmentations developed at the German Cancer Research Center, which result in triangulated 3D models of the valve surface. The core of this work will be the construction of a 2D-view of these models through global parameterization, a method that can be used to establish a bijective mapping between a planar parameter domain and a surface embedded in higher dimensions.
A flat representation of the mitral valve provides physicians with a view of the whole surface at once, similar to a map. This allows assessment of the valve's area and shape without the need for different viewing angles. Parts of the valve that are occluded by geometry in 3D become visible in 2D.
An additional contribution of this work will be the exploration of different visualizations of the 3D and 2D mitral valve representations. Features of the valve can be highlighted by associating them with specified colors, which can for instance directly convey pathology indicators.
Quality and effectiveness of the proposed methods were evaluated through a survey conducted at the Heidelberg University Hospital.
This work describes a novel software tool for visualizing anatomical segmentations of medical images. It was developed as part of a bachelor's thesis project, with a view to supporting research into automatic anatomical brain image segmentation. The tool builds on a widely-used visualization approach for 3D image volumes, where sections in orthogonal directions are rendered on screen as 2D images. It implements novel display modes that solve common problems with conventional viewer programs. In particular, it features a double-contour display mode to aid the user's spatial orientation in the image, as well as modes for comparing two competing segmentation labels pertaining to one and the same anatomical region. The tool was developed as an extension to an existing open-source software suite for medical image processing. The visualization modes are, however, suitable for implementation in the context of other viewer programs that follow a similar rendering approach.
The modified code can be found here: soundray.org/mm-segmentation-visualization.tar.gz.
The goal of this thesis is to create a recommender system (RS) for business processes, based on the existing ProM plugin RegPFA. To accomplish this task, firstly an interface must be created that sets up and expands a database receiving probabilistic finite automata (PFA) created by RegPFA in tsml format as input. Secondly, a Java program must be designed that uses said database to recommend the process elements that are most likely to follow a given sequence of process elements.
Abstract
This bachelor thesis delivers a comprehensive overview of the topic Internet of Things (IoT). With the help of a first literature review, important characteristics, architectures, and properties have been identified. The main aim of this bachelor thesis is to determine whether the use of IoT in the transport of food, considering the compliance with the cold chain, can provide advantages for companies to reduce food waste. For this purpose, a second literature review has been carried out with food transport systems without the use, as well as with the use of IoT. Based on the literature review, it is possible at the end to determine a theoretical ‘ideal’ system for food transport in refrigerated trucks. The respective used technologies are also mentioned. The findings of several authors have shown that often significant improvements can be achieved in surveillance, transport in general, or traceability of food, and ultimately food waste can be reduced. However, benefits can also be gained using new non-IoT-based technologies. Thus, the main knowledge of this bachelor thesis is that a theoretical ‘ideal’ transport system contains a sensible combination of technologies with and without IoT. This system includes the use of a Wireless Sensor Network (WSN) for real-time food monitoring, as well as an alarm function when the temperature exceeds a maximum. Real-time monitoring with GPS coupled with a monitoring center to prevent traffic jams is another task. Smart and energy-efficient packaging, and finally the use of the new supercooling-technology, make the system significantly more efficient in reducing food waste. These highlights, that when choosing a transport system, which is as efficient and profitable as possible for food with refrigerated transport, companies need not just rely on the use of IoT. On this basis, it is advisable to combine the systems and technologies used so far with IoT in order to avoid as much food waste as possible.
Over the past few decades society’s dependence on software systems has grown significantly. These systems are utilized in nearly every matter of life today and often handle sensitive, private data. This situation has turned software security analysis into an essential and widely researched topic in the field of computer science. Researchers in this field tend to make the assumption that the quality of the software systems' code directly affects the possibility for security gaps to arise in it. Because this assumption is based on properties of the code, proving it true would mean that security assessments can be performed on software, even before a certain version of it is released. A study based on this implication has already attempted to mathematically assess the existence of such a correlation, studying it based on quality and security metric calculations. The present study builds upon that study in finding an automatic method for choosing well-fitted software projects as a sample for this correlation analysis and extends the variety of projects considered for the it. In this thesis, the automatic generation of graphical representations both for the correlations between the metrics as well as for their evolution is also introduced. With these improvements, this thesis verifies the results of the previous study with a different and broader project input. It also focuses on analyzing the correlations between the quality and security metrics to real-world vulnerability data metrics. The data is extracted and evaluated from dedicated software vulnerability information sources and serves to represent the existence of proven security weaknesses in the studied software. The study discusses some of the difficulties that arise when trying to gather such information and link it to the difference in the information contained in the repositories of the studied projects. This thesis confirms the significant influence that quality metrics have on each other. It also shows that it is important to view them together as a whole and suppose that their correlation could influence the appearance of unwanted vulnerabilities as well. One of the important conclusions I can draw from this thesis is that the visualization of metric evolution graphs, helps the understanding of the values as well as their connection to each other in a more meaningful way. It allows for better grasp of their influence on each other as opposed to only studying their correlation values. This study confirms that studying metric correlations and evolution trends can help developers improve their projects and prevent them from becoming difficult to extend and maintain, increasing the potential for good quality as well as more secure software code.