Bachelor Thesis
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The goal of this thesis is to develop a novel type of virtual heritage medium that utilises the combined immersive and engaging potentials of interactive mixed reality environments and spatial narratives. Concretely, this is achieved through depth-sensitive compositing of real-time 3D content into the live-video of a tracked smartphone. The user can explore this mixed reality environment, watch the actions of staged 3D characters as well as interact with them and virtual artifacts. This medium would therefore provide possibilities for telling stories in direct context with existing environments along with an immersive and engaging media experience. This work will mainly focus on how this medium can be used as an edutainment medium in sites of cultural heritage. This thesis will focus on establishing the technical requirements and realisation possibilities for implementation in Unity on iPhone 5 / iOS 7. Subsequently, a prototype is implemented in order to prove the research results.
Concepts and Services for Asylum Seekers in Public Libraries Using the Example of Germany and Norway
(2016)
The goal of the following bachelor thesis is to introduce concepts of public libraries concerning asylum seekers. As an example the thesis is using public libraries in Germany and Norway. Therefore, the reader will be introduced to the general situation, living conditions and preconditions of asylum seekers in both countries as well as to preconditions of libraries and librarians concerning monetary and territorial aspects and education of library staff. Important international library representatives as well as local actors will be introduced and the importance of cooperation between libraries and other organizations will be examined. In the main part practical methods, services, offers and ways of how libraries can help asylum seekers will be elaborated and possibilities how asylum seekers can actively participate in the library will be explained. Challenges which can occur will be detected and elaborated. Furthermore, the public library of Bergen in Norway and the public library of Duisburg in Germany will be presented as best practice examples.
With the increasing use of visual effects in feature films, TV series and commercials, flexibility becomes essential to create astonishing pictures while meeting tight production schedules. Deep image compositing introduces new possibilities that increase flexibility and solve old problems of depth based compositing. The following thesis gives an introduction to deep image compositing, illustrating its power and analyzing its use in a modern visual effects pipeline.
The capabilities of Artificial Intelligence (AI) are utilized increasingly
in today‘s world. The autonomous and adaptive characteristics
allow applications to be more effective and efficient. A certain
subfield of Artificial Intelligence, Machine Learning, is enabling
services to be tailored to a user‘s specific needs. This could prove to
be useful in an information-heavy field such as Statistics. As design
research from SPSS Statistics, a legacy statistical application, has
indicated, statistics beginners struggle to tackle the challenge of
preparing a statistical research study. They turn to several sources
of information in an attempt to find help and answers but are not
always successful. This leads to them being unconfident before
they have even started to execute the statistical study. The adaptive
features of Artificial Intelligence could help support students
in this case, if designed according to established principles. This
thesis investigated the question whether an AI-powered solution
could elevate the users‘ confidence in statistical research studies.
In order to find the answer, a prototype with exemplary User Experience
was designed and implemented. Preceding research determined
the domain and market offer. User research was conducted
to ensure a human-centered outcome. The prototype was evaluated
with real test users and the results answered the question in
the affirmative.
When searching for bugs in Java enterprise applications, an essential part of the
eort consists in redeploying the source code and relaunching the server over and
over. In order to improve this situation, this thesis suggests the implementation
of a runtime debugging tool. The tool's purpose is to facilitate the enrichment of
operating application code with logging statements, which are inteded to generate
additional output concerning the webapp's current state. On behalf of this
so-called instrumentation, the actual process of debugging could be supported
and accelerated without having to interrupt the server's execution.
Due to the signicance of Java EE as well as Spring for today's enterprise development,
the implementation of a dedicated debugging tool for each platform
shall be covered. Both solutions pursue the same goal, but dier in the approach
and the programming paradigm forming their basis. This document introduces
their implementation details and evaluates them against a specication that de-
nes the general conditions and expectations in terms of the capabilities of a
satisfying result.
Deep learning methods have proven highly effective for object recognition tasks, especially
in the form of artificial neural networks. In this bachelor’s thesis, a way is shown to imple-
ment a ready-to-use object recognition implementation on the NAO robotic platform using
Convolutional Neural Networks based on pretrained models. Recognition of multiple objects
at once is realized with the help of the Multibox algorithm. The implementation’s object
recognition rates are evaluated and analyzed in several tests.
Furthermore, the implementation offers a graphical user interface with several options to
adjust the recognition process and for controlling movements of the robot’s head in order
to easier acquire objects in the field of view. Additionally, a dialogue system for querying
further results is presented.
The number of people with cognitive impairments increases together with the aging population. Thus, social robots are being researched to aid relieve the nursing
sector as well as to combat cognitive impairments. However, it raises concerns regarding how a social robot should relate to members of this group and what might
be appropriate. In this thesis, research about the current state of social robots has been conducted and focus groups with people from the nursing and medical field were held. To verify the credibility of the results and the scenario developed, final
user tests were conducted with representatives of the target group. When using a
social robot in an interaction with persons who have cognitive disabilities, the robot
should speak and behave more human-like and make use of its facial expressions,
stressing empathy and responding to the person accordingly. Though the situation
of interacting with a social robot may be more significant in future generations.
Today’s digital cameras use a mosaic of red, green, and blue color filters to capture images in three color channels on a single sensor plane. This thesis investigates the use of convolutional neural networks (CNNs) for demosaicing – the process of reconstructing full-color images from raw mosaic sensor data. While there are existing CNNs for demosaicing raw images from the well-established regular Bayer color filter array (CFA), this thesis focuses on how they perform on alternative non-regular sampling patterns that produce less aliasing artifacts, namely the stochastic Gaussian- and the RandomQuarter sampling pattern (Backes and Fröhlich, 2020).
A basic UNet (Ronneberger et al., 2015) and the spatially adaptive SANet (T. Zhang et al., 2022) are implemented in a supervised training pipeline based on the PixelShift200 image dataset (Qian et al., 2021) to investigate their suitability for the irregular demosaicing task. The experiments indicate that the basic UNet encounters difficulties in restoring the missing color values, whereas the spatially adaptive convolutional layers help in processing the irregularly sampled raw images.
In addition, this thesis enhances SANet effectiveness by employing an alternative residual branch based on a CFA-normalized Gaussian filter, as well as a tileable modification to the Gaussian CFA pattern. The modified SANet is shown to outperform the conventional dFSR algorithm (Backes & Fröhlich, 2020) in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM).
Privacy in Social Networks
(2016)
Online Social Networks (OSNs) are heavily used today and despite of all privacy concerns found a way into our daily life. After showing how heavy data collection is a violation of the user's privacy, this thesis establishes mandatory and optional requirements for a Privacy orientated Online Social Network (POSN). It evaluates twelve existing POSNs in general and in regard to those requirements. The paper will find that none of these POSNs are able to fulfill the requirements and therefore proposes features and patterns as a reference architecture.
This bachelor thesis wants to describe a prototypical implementation of a 3D user interface for intuitive real-time set editing in virtual production. Furthermore this approach is evaluated qualitatively through a user group, testing the device and fill in a questionnaire. The dimension of virtual elements created with computer graphics technology in all areas of entertainment industry is steadily growing since the past years. Nevertheless can the editing process of virtual elements still require a costly process in terms of time and money. With the appearance of new input devices and improved tracking technologies it is interesting to evaluate if a real-time editing process could improve this situation. Being currently bound to experts on special workstations, this could lead to a more intuitive and real-time workflow, enabling everybody on a film set to influence the digital editing process and work collaboratively on the scene consisting of virtual and real elements.