RESUMEN
The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF), which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf) in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is tested via two experiments, one at a university's premises and another in realistic tactical conditions. The results show significant improvement on the horizontal localization when the measurement errors are carefully modelled and their inclusion into the particle filtering implementation correctly realized.
RESUMEN
Rapeseed and pine bark are rich sources of phenolic compounds that have in previous studies been shown to exhibit antioxidant and anti-inflammatory properties. In this study, the antioxidant effect of rapeseed and pine bark phenolics in inhibiting the oxidation of lipids and proteins in meat was tested as a possible functional food application. The cooked pork meat with added plant material was oxidized for 9 days at 5 degrees C under light. The suitable level of plant material addition was first screened by following lipid oxidation only. For further investigations plant materials were added at a level preventing lipid oxidation by >80%. The oxidation was followed by measuring the formation of hexanal by headspace gas chromatography and the formation of protein carbonyls by converting them to 2,4-dinitrophenylhydrazones and measured by spectrophotometer. It was shown that rapeseed and pine bark were excellent antioxidants toward protein oxidation (inhibitions between 42 and 64%). These results indicate that rapeseed and pine bark could be potential sources of antioxidants in meat products.