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.

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.

We use motion context recognition to enhance the result of our infrastructure-free indoor navigation algorithm. Target applications are difficult navigation scenarios such as first responder, rescue, and tactical applications. Our navigation algorithm uses inertial navigation and visual navigation fusion. Random Forest classifier algorithm is taught with training data from Inertial Measurement Unit and visual navigation data to classify between walking, running and climbing. This information is used both in pedestrian navigation to do stationarity detection with adaptive threshold and in particle filter fusion to exclude visual data from during climbing. Methods are evaluated in an indoor navigation test where person wearing tactical equipment moves through a building. Results show improvement of positioning accuracy based on loop closure error on the test track especially when the movement is fast paced. The loop closure error was reduced on average 4 % in two tests when movement was slow and 14 % when movement was fast.

BIOGRAPHIES Aiden Morrison received his PhD degree in 2010 from the University of Calgary. Currently, he works as a research scientist at SINTEF Digital. His main research interests are in the areas of GNSS and multiuser collaborative navigation systems. Laura Ruotsalainen received her PhD in pervasive computing from Tampere University of Technology in 2013, and currently works as an associate professor at the University of Helsinki and as a part-time research professor at the Finnish Geospatial Research Institute. Maija Mäkelä received her MSc in Science and Engineering from the Tampere University of Technology in 2016 and is now pursuing a doctoral degree in the same university. She currently works as a research scientist in the Finnish Geospatial Research Institute, focusing on collaborative navigation methods and algorithms. Jesperi Rantanen received his M.Sc. (Tech.) degree in Geomatics from Aalto University School of Engineering, Finland, in 2015 and he is currently pursuing a doctoral degree at the University of Tampere. He works as a researcher at the Finnish Geospatial Research Institute focusing on developing adaptive navigation systems. Nadezda Sokolova received her PhD degree in 2011 from Norwegian University of Science and Technology (NTNU), where she worked on weak GNSS signal tracking and use of GNSS for precise velocity and acceleration determination. Currently, she works as a research scientist at SINTEF Digital, and adjunct associate professor at the Engineering Cybernetics Department, NTNU focusing on GNSS integrity monitoring and multi-sensor navigation.