Aktuelle Studien- und Abschlussarbeiten
8 items found. Show all theses.
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2020/09/03
Smart Graphical User Interface for 3D Point Cloud Scene Annotation
Bachelor Thesis, Master Thesis
The annotation of datasets for deep learning models is a tedious and time-consuming task. The annotation becomes particularly challenging when preparing 3D point cloud datasets to train complex neural network architectures for tasks like segmentation or scene flow.
However, 3D point clouds present particular geometric properties, that can be leveraged to make the annotation easier. go
Supervisors: Achref Doula, Dr. Alejandro Sanchez Guinea
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2020/09/03
Time Series Augmentation for Sensory Systems
Bachelor Thesis, Master Thesis
Deep Learning performs remarkably well on large datasets that covers wide range of cases. However, the labeled data of many world time series applications may be limited. As an effective way to enhance the size and quality of the training data, we rely on data augmentation which showed a huge success in computer vision problems. However, less attention has been paid to find better data augmentation methods for time series. go
Supervisor: Dr. Alejandro Sanchez Guinea
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2020/09/03
Enhance Recognition of Driver Profile by Improving OBD Protocol
Bachelor Thesis, Master Thesis
An essential quality criterion of motor vehicles is their reliability. Reliability is equivalent to the likelihood that a product will operate faultlessly for a defined mileage under given operating and environmental conditions or under a given performance framework. In this context, the knowledge about the usage behavior and the resulting state of damage on public roads is a very important information. For example, this knowledge can be used to better design operating strategies and introduce predictive maintenance. go
Supervisor: Dr. Alejandro Sanchez Guinea
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2020/06/17
Driving pattern recognition with vehicle CAN-data
Neural Network is increasingly applied in various domains within the automotive industry. One of many is the field of predictive maintenance, which tries to estimate the current state of car parts to early access needs for maintenance and residual car value estimation. For these application signal bus system (CAN) data can be used, giving a huge amount of time series data to estimate the state of a car component. Problematic about this data is, that time series signals can vary strongly depending on driving situations, user and environmental factors, and therefore are not necessarily comparable. Knowledge about similar and different situations within the data, would enable a smarter selection of data for training and evaluation, therefore increase training data quality and performance of the model. Further, knowledge about repeating events within the data, would enable condition monitoring based on behavioral changes in well-comparable situations.
Being said that, the objective behind this work is to explore algorithms to find often occurring events within the multidimensional CAN-data. Due to the huge size of the data and high dimensionality, efficient algorithms need to be found and tested regarding the quality of the results. For this investigation, a case study from the automotive domain is considered, where the model is taking multivariate time series from the signal bus system as features in order to find often occurring and rare events such as a specific driving maneuver within testing data or commute trips within real car usage CAN data. go
Supervisor: Dr. Alejandro Sanchez Guinea
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2020/05/19
Fundamental methods for reasoning and planning of AI agents in smart environments
Bachelor Thesis, Master Thesis
Ubiquitous computing is in need of techniques that allow the system to act intelligently and autonomously. In this respect, one of the main concerns is the automated and dynamic creation of plans, which give the system the capabilities to reason about heterogenous and uncertain changes in the environment in an efficient manner. In spite of the important advancements that the field of Artificial Intelligence (AI) have made in automated planning and reasoning techniques, efficient planning for large sequence of actions in ever changing environments remains an open problem. go
Supervisor: Dr. Alejandro Sanchez Guinea
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2020/05/14
Sight beyond sight in AR based on non-visible signals
Bachelor Thesis, Master Thesis
Non-visible signals (such as sound) have been used in the past to recognize people, and people's activities. In many cases these signals are distinguishable and useful for recognition tasks even when objects obstruct the user's direct view. With such approaches as basis, it is possible to envision cases in which what is being occluded can be rendered into the user's visual field through augmented reality (AR). go
Supervisor: Dr. Alejandro Sanchez Guinea
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2020/05/14
Smart 3D Urban Reconstruction from Point Cloud Data
Bachelor Thesis, Master Thesis
Reconstruction of 3D models based on point cloud data has been widely studied in both computer graphics and computer vision communities. However, automatic reconstruction of models that accurately represent real-world models remains an open problem. go
Supervisor: Dr. Alejandro Sanchez Guinea
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2020/03/06
3D Printing: Drucken von interaktiven Objekten
Hast du dich schon mal gefragt: „Wieso kann man interaktive Objekte nicht nach den eigenen Wünschen selbst ausdrucken?“
Mit 3D-Drucker kann man schon jetzt unglaubliche Dinge drucken. Komplexe Objekte aus Kunststoff, Keramik, Metall oder sogar Schokolade sind kein Ding der Unmöglichkeit mehr. Aber bis jetzt fehlt es diesen Objekten meistens noch an Interaktivität!
Im Rahmen dieser Arbeit sollen daher verschiedene Ein- und Ausgabetechniken (bspw. Erkennung von Berührungen oder Deformationen) schrittweise in den gewöhnlichen 3D-Druck integriert werden, sodass programmierbare interaktive 3D-Objekte entstehen (nähere Informationen zu konkreten Themen gerne auf Anfrage). goSupervisor: Dr. Martin Schmitz