Microfluidic flow assays (MFA) that measure shear dependent platelet function have potential clinical applications in the diagnosis and treatment of bleeding and thrombotic disorders. As a step towards clinical application, the objective of this study was to measure how phenotypic and genetic factors, as well as experimental conditions, affect the variability of platelet accumulation on type 1 collagen within a MFA. Whole blood was perfused over type 1 fibrillar collagen at wall shear rates of 150, 300, 750 and 1500 s−1 through four independent channels with a height of 50 µm and a width of 500 µm. The accumulation of platelets was characterized by the lag time to 1% platelet surface coverage (LagT), the rate of platelet accumulation (VPLT), and platelet surface coverage (SC). A cohort of normal donors was tested and the results were correlated to plasma von Willebrand factor (VWF) levels, platelet count, hematocrit, sex, and collagen receptors genotypes. VWF levels were the strongest determinant of platelet accumulation. VWF levels were positively correlated to VPLT and SC at all wall shear rates. A longer LagT for platelet accumulation at arterial shear rates compared to venous shear rates was attributed to the time required for plasma proteins to adsorb to collagen. There was no association between platelet accumulation and hematocrit or platelet count. Individuals with the AG genotype of the GP6 gene had lower platelet accumulation than individuals with the AA genotype at 150 s−1 and 300 s−1. Recalcified blood collected into sodium citrate and corn trypsin inhibitor (CTI) resulted in diminished platelet accumulation compared to CTI alone, suggesting that citrate irreversibly diminishes platelet function. This study the largest association study of MFA in healthy donors (n = 104) and will likely set up the basis for the determination of the normal range of platelet responses in this type of assay.

The main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity.

Abstract-In urban areas, GNSS localization quality is often degraded due to signal blockage and multi-path reflections. When several GNSS signals are blocked by buildings, the remaining unblocked GNSS satellites are typically in a poor geometry for localization (nearly collinear along the street direction). Multi-path reflections result in pseudo range measurements that can be significantly longer than the line of sight path (true range) resulting in biased geolocation estimates. If a 3D map of the environment is available, one can address these problems by evaluating the likelihood of GNSS signal strength and location measurements given the map. We present two approaches based on this observation. The first is appropriate for cases when network connectivity may be unavailable or undesired and uses a particle filter framework that simultaneously improve both localization and the 3D map. This approach is shown via experiments to improve the map of a section of a university campus while simultaneously improving receiver localization. The second approach which may be more suitable for smartphone applications assumes that network connectivity is available and thus a software service running in the cloud performs the mapping and localization calculations. Early experiments demonstrate the potential of this approach to significantly improve geo-localization accuracy in urban areas.

Abstract-A novel approach is proposed to achieve simultaneous localization and mapping (SLAM) based on the signal-tonoise ratio (SNR) of global navigation satellite system (GNSS) signals. It is assumed that the environment is unknown and that the receiver location measurements (provided by a GNSS receiver) are noisy. The 3D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. The posterior distribution of the map and poses is shown to represent a factor graph, on which Loopy Belief Propagation is used to efficiently estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location. Experimental results demonstrate our algorithm's ability to coarsely map (in three dimensions) a corner of a university campus, while also correcting for uncertainties in the location of the GNSS receiver.

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