Multitarget Tracking Using Orientation Estimation for Optical Belt Sorting

In optical belt sorting, accurate predictions of the bulk material particles’ motions are required for high-quality results. By implementing a multitarget tracker tailored to the scenario and deriving novel motion models, the predictions are greatly enhanced. The tracker’s reliability is improved by also considering the particles’ orientations. To this end, new estimators for directional quantities based on orthogonal basis functions are presented and shown to outperform the state of the art

Zuzahlungen nach dem GKV-Modernisierungsgesetz (GMG) unter Beruecksichtigung von Haertefallregelungen

The law for the Modernisation of the Social Health Insurance System 2003 ("GKV-Modernisierungsgesetz – GMG" 2003) provides, among other measures, for a noticeable increase in co-payments and also for a reduction of possibilities for claiming exemptions from co-payments. Against this backdrop, the authors of the paper present, at the start, the varying news on co-insurance payments held by different political groups. Thereafter, the theoretical foundations of co-payments are described. Finally, on the basis of extensive empirically founded computations, the effects of the new co-payment rules (together with relevant new exemptions) are compared with the effects of the rules still in force.Social Health Insurance System, co-payment, public finance

Multimodal Circular Filtering Using Fourier Series

Recursive filtering with multimodal likelihoods and transition densities on periodic manifolds is, despite the compact domain, still an open problem. We propose a novel filter for the circular case that performs well compared to other state-of-the-art filters adopted from linear domains. The filter uses a limited number of Fourier coefficients of the square root of the density. This representation is preserved throughout filter and prediction steps and allows obtaining a valid density at any point in time. Additionally, analytic formulae for calculating Fourier coefficients of the square root of some common circular densities are provided. In our evaluation, we show that this new filter performs well in both unimodal and multimodal scenarios while requiring only a reasonable number of coefficients

The State Space Subdivision Filter for Estimation on SE(2)

The [Formula: see text] domain can be used to describe the position and orientation of objects in planar scenarios and is inherently nonlinear due to the periodicity of the angle. We present a novel filter that involves splitting up the joint density into a (marginalized) density for the periodic part and a conditional density for the linear part. We subdivide the state space along the periodic dimension and describe each part of the state space using the parameters of a Gaussian and a grid value, which is the function value of the marginalized density for the periodic part at the center of the respective area. By using the grid values as weighting factors for the Gaussians along the linear dimensions, we can approximate functions on the [Formula: see text] domain with correlated position and orientation. Based on this representation, we interweave a grid filter with a Kalman filter to obtain a filter that can take different numbers of parameters and is in the same complexity class as a grid filter for circular domains. We thoroughly compared the filters with other state-of-the-art filters in a simulated tracking scenario. With only little run time, our filter outperformed an unscented Kalman filter for manifolds and a progressive filter based on dual quaternions. Our filter also yielded more accurate results than a particle filter using one million particles while being faster by over an order of magnitude