Tension and Ampacity Monitoring System for Overhead Lines

Real time monitoring allows the determination of the line state and the calculation of the actual rating value. The real time monitoring systems measure sag, conductor tension, conductor temperature or weather related magnitudes. In this paper, a new ampacity monitoring system for overhead lines, based on the conductor tension, the ambient temperature, the solar radiation and the current intensity, is presented. The measurements are transmitted via GPRS to a control center where a software program calculates the ampacity value. The system takes into account the creep deformation experienced by the conductors during their lifetime and calibrates the tension-temperature reference and the maximum allowable temperature in order to obtain the ampacity. The system includes both hardware implementation and remote control software.

Visual SLAM using straight lines

The present thesis is focuses on the problem of Simultaneous Localisation and Mapping (SLAM) using only visual data (VSLAM). This means to concurrently estimate the position of a moving camera and to create a consistent map of the environment. Since implementing a whole VSLAM system is out of the scope of a degree thesis, the main aim is to improve an existing visual SLAM system by complementing the commonly used point features with straight line primitives. This enables more accurate localization in environments with few feature points, like corridors. As a foundation for the project, ScaViSLAM by Strasdat et al. is used, which is a state-of-the-art real-time visual SLAM framework. Since it currently only supports Stereo and RGB-D systems, implementing a Monocular approach will be researched as well as an integration of it as a ROS package in order to deploy it on a mobile robot. For the experimental results, the Care-O-bot service robot developed by Fraunhofer IPA will be used.

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification