Swedish Defence Research Agency

About: Swedish Defence Research Agency is a government organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Radar & Laser. The organization has 1413 authors who have published 2731 publications receiving 56083 citations. The organization is also known as: Totalförsvarets forskningsinstitut.

Investigations of surface coatings to reduce memory effect in plastic scintillator detectors used for radioxenon detection

Abstract: In this work Al2O3 and SiO2 coatings are tested as Xe diffusion barriers on plastic scintillator substrates. The motivation is improved beta–gamma coincidence detection systems, used to measure atmospheric radioxenon within the verification regime of the Comprehensive Nuclear-Test-Ban Treaty. One major drawback with the current setup of these systems is that the radioxenon tends to diffuse into the plastic scintillator material responsible for the beta detection, resulting in an unwanted memory effect. Here, coatings with thicknesses between 20 and 900 nm have been deposited onto plastic scintillators, and investigated using two different experimental techniques. The results show that all tested coatings reduce the Xe diffusion into the plastic. The reduction is observed to increase with coating thickness for both coating materials. The 425 nm Al2O3 coating is the most successful one, presenting a diffusion reduction of a factor 100, compared to uncoated plastic. In terms of memory effect reduction this coating is thus a viable solution to the problem in question.

Learning to classify emotional content in crisis-related tweets

Abstract: Social media is increasingly being used during crises. This makes it possible for crisis responders to collect and process crisis-related user generated content to allow for improved situational awareness. We describe a methodology for collecting a large number of relevant tweets and annotating them with emotional labels. This methodology has been used for creating a training data set consisting of manually annotated tweets from the Sandy hurricane. Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets. Results show that a support vector machine achieves the best results (60% accuracy on the multi-classification problem).

3D laser sensing at FOI: overview and a system perspective

Abstract: This paper wil give an overview of 3D laser sensing and related activities at the Swedish Defence Research Agency (FOI) in the view of system needs and applications. Our activites include data collection of laser signatures for target and backgrounds at various wavelengths. We will give examples of such measurements. The results are used in building sythetic environments, modellin of laser radar systems and as training sets for development of algorithms for target recognition and weapon applications. Present work on rapid environment assessment includes the use of data from airborne laser for terrain mapping and depth sounding. Methods for automatic target detection and object classification (buildings, trees, man-made objects etc.) have been developed together with techniques for visualisation. This will be described in more detail in a separate paper. The ability to find and correctly identify "difficult" targets, being either at very long ranges, hidden in the vegetation, behind windows or under camouflage, is one of the top priorities for any military force. Example of such work will be given using range gated imagery and 3D scanning laser radars. Different kinds of signal processing approaches have been studied and will be presented more in two separate papers. We have also developed modeling tools for both 2D and 3D laser imaging. Finally we will discuss the use of 3D laser radars in some system applications in the light of new component technology, processing needs and sensor fusion.

A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

Abstract: This paper addresses one of the main challenges in physical activity monitoring, as indicated by recent benchmark results: The difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. This algorithm is a variant of the AdaBoost.M1 that incorporates well-established ideas for confidence-based boosting. ConfAdaBoost.M1 is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository. Moreover, it is evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks. Finally, two empirical studies are designed and carried out to investigate the feasibility of ConfAdaBoost.M1 for physical activity monitoring applications in mobile systems.