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This work treats with the segmentation of 2D environment Laser data, captured by an Autonomous Mobile Indoor Robot. It is part of the data processing, which is necessary to navigate a mobile robot error free in its environment. The whole process can generally be described by data capturing, data processing and navigation. In this project the data processing deals with data, captured by a Laser-Sensor, which provides two dimensional data by a series of distance measurements i.e. point-measurements of the environment. These point series have to be filtered and processed into a more convenient representation to provide a virtual environment map, which can be used of the robot for an error free navigation. This project provides different solutions of the same problem: the conversion from distance points to model segments which should represent the real world environment as close as possible. The advantages and disadvantages of each of the different Segmentation-Algorithms will be shown as well as a comparison taking into account the Computational Time and the Robustness of the results.
The target of this thesis is the introduction of a client management system (CMS) at Haaland Internet Productions (HiP), a web design and hosting company in Burbank, California, USA. The company needs a system to track orders and improve workflow. HiP needs a system which not only tracks orders, but also stores all client information in a database. This client information can be used for a variety of marketing and contact reasons. It is an important and integral part of HiP's client relationship management (CRM). The lack of a cohesive CMS at HiP caused many fundamental business problems, such as lost orders, missed billing statements, and over/under billing. The research done during the investigation and analysis of the company and their needs should lead to a global system which totally fulfils the needs of HiP. This global system could be in the form of an off-the-shelf product with some customizations, or a completely new, in-house system. Either solution will have respective pros and cons; the goal is to reach a decision that best fits HiP's needs and situation. The following is a concise version of the project. Particular emphasis is placed upon the single steps which made up the decision process, as well as the practiced techniques, methods, and their applications.
Interpretability and uncertainty modeling are important key factors for medical applications. Moreover, data in medicine are often available as a combination of unstructured data like images and structured predictors like patient’s metadata. While deep learning models are state-of-the-art for image classification, the models are often referred to as ’black-box’, caused by the lack of interpretability. Moreover, DL models are often yielding point predictions and are too confident about the parameter estimation and outcome predictions.
On the other side with statistical regression models, it is possible to obtain interpretable predictor effects and capture parameter and model uncertainty based on the Bayesian approach. In this thesis, a publicly available melanoma dataset, consisting of skin lesions and patient’s age, is used to predict the melanoma types by using a semi-structured model, while interpretable components and model uncertainty is quantified. For Bayesian models, transformation model-based variational inference (TM-VI) method is used to determine the posterior distribution of the parameter. Several model constellations consisting of patient’s age and/or skin lesion were implemented and evaluated. Predictive performance was shown to be best by using a combined model of image and patient’s age, while providing the interpretable posterior distribution of the regression coefficient is possible. In addition, integrating uncertainty in image and tabular parts results in larger variability of the outputs corresponding to high uncertainty of the single model components.
This thesis investigates methods for the recognition of facial expressions using support vector machines. Rather than trying to recognize facial actions in the face such as raised eyebrow, mouth open and frowns. These facial actions are described in the Facial Action Coding System (FACS) and are essential facial components, which can be combined to form facial expressions. We perform independent recognition of 6 upper and 10 lower action units in the face, which may occur either individually or in combination. Based on a feature extraction from grey-level values, the system is expected to recognize under real-time conditions. Results are presented with different image resolutions, SVM kernels and variations of low-level features.
This thesis deals with background, theory, design, layout and experimental test results of an analogue CMOS VLSI current-mode analog-to-digital converter. This system supports a project, whose goal it is to build a biologically relevant model of synaptic plasticity, named the Artificial Synapse. A critical part of the design, which is based on analogue CMOS VLSI circuits, is the ability to activate a discrete number of channels by sampling an analogue signal. Since currents are the signal of interest and transistors are biased in weak inversion (subthreshold regime), the system requires a current mode A/D circuit that it can operate at ultra-low power and current levels. To meet this need, two new innovative A/D converter approaches are proposed to replace the system’s previous A/D converter design which suffered from a non-linear resolution, uncoded output code and heavy bit oscillations. The initial technical requirements and key criteria for the new converter comprise a resolution of one nano ampere, an input current range between 0 – 100nA, conversion frequencies of up to 5kHz, and a power supply voltage of less than 1.5V. Temperature range, space occupation and power dissipation aspects were not specified due to the early stage of the related Artificial Synapse project. The novel converters both produce seven bit thermometer codes, their functional principle can be best described as current mode flash analog-to-digital converters (ADCs). Due to the fact that the input signal is in the area of a subthreshold current, it is selfevident that the A/D converter design should operate at a subthreshold realm. To support low power operation, clocks or high currents could not be used and were excluded from the design from the very start. To encode the thermometer code into standard binary code, a seven-to-three encoder was designed and integrated on the chip. In October 2003, the design was submitted for production to the MOSIS circuit fabrication service. The AMI Semiconductor 1.5 micron ABN CMOS process was chosen to manufacture the chip. When it was returned in January 2004, simulation results showed that both new A/D converter approaches accomplished excellent results which were expected from SPICE simulation results. With the new chip installed, it became possible to resolve input currents as small as one nano ampere and achieve conversion frequencies of up to 5kHz. The circuits also both meet the requirements which were set at the beginning of the project to operate at a power supply voltage of less than 1.5V, processing input currents in the range between 0 – 100nA. A prototype printed circuit board (PCB) was developed, produced and employed for experiments with the chip. The major application of this test-bed is the ability to generate and measure extremely low currents with high precision. This enables the monitoring of the very small currents that are processed by the chip.