Glösekötter, Peter
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- Interval analysis · Particle filtering · Kalman filtering · Bayesian filtering · Sequential Monte Carlo simulation · Bounded-error estimation (1)
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Accurate self-localisation is a fundamental ability of any mobile robot. In Monte Carlo localisation, a probability distribution over a space of possible hypotheses accommodates the inherent uncertainty in the position estimate, whereas bounded-error localisation provides a region that is guaranteed to contain the robot. However, this guarantee is accompanied by a constant probability over the confined region and therefore the information yield may not be sufficient for certain practical applications. Four hybrid localisation algorithms are proposed, combining probabilistic filtering with non-linear bounded-error state estimation based on interval analysis. A forward-backward contractor and the Set Inverter via Interval Analysis are hybridised with a bootstrap filter and an unscented particle filter, respectively. The four algorithms are applied to global localisation of an underwater robot, using simulated distance measurements to distinguishable landmarks. As opposed to previous hybrid methods found in the literature, the bounded-error state estimate is not maintained throughout the whole estimation process. Instead, it is only computed once in the beginning, when solving the wake-up robot problem, and after kidnapping of the robot, which drastically reduces the computational cost when compared to the existing algorithms. It is shown that the novel algorithms can solve the wake-up robot problem as well as the kidnapped robot problem more accurately than the two conventional probabilistic filters.
This paper deals with the issue of automating the
process of machine learning and analyzing bio-datasets. For this
a user-friendly website has been developed for the interaction
with the researchers. On this website it is possible to upload
datasets and to share them, if desired, with other scientists. The
uploaded data can also be analyzed by various methods and
functions. The signals inside these datasets can also be visualized.
Furthermore several algorithms have been implemented to create
machine learning models with the uploaded data. Based on these
generated models new data can be classified or calculated. For all
these applications the simplest possible handling was
implemented to make the website available to all interested
researchers.
Magnetic field sensors based on quantum mechanic effects are often
susceptible to misalignments of the magnetic field or need advanced
procedures to compensate for these. Also, the record breaking sensitivities reported for superconducting quantum interference devices and alkali vapor magnetometers come along with large and complex experimental setups. The nitrogen vacancy center in diamond can be used to design a simple, small, and robust sensor without employing microwave radiation. By using compressed nanodiamond particles, it is possible to eliminate the need of an alignment of the magnetic field and still obtain the absolute magnetic flux density in a single measurement. In order to demonstrate the capabilities of this approach, a centimeter-sized modified automotive demo board is employed as a complete sensor with a sensitivity of 78 µT/Wurzel Hz.
State of the art classifiers split Alzheimer’s disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer’s disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.