Dan Imre

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We describe a system designed to measure the size, composition and density of individual spherical particles in real-time. It uses a Differential Mobility Analyzer (DMA) to select a monodisperse particle population and the single particle mass spectrometer to measure individual particle aerodynamic diameter. Together the mobility and aerodynamic diameters(More)
Cluster analysis (CA) is a powerful strategy for the exploration of high-dimensional data in the absence of a-priori hypotheses or data classification models, and the results of CA can then be used to form such models. But even though formal models and classification rules may not exist in these data exploration scenarios, domain scientists and experts(More)
Government's right to retain a non exclusive, royalty-free license in and to any copyright covering this paper. Abstract. Radiative transfer models consistently overestimate surface diffuse downward irradiance in cloud-free atmospheres by 9 to 40% at two low altitude sites while correctly calculating direct-normal Solar irradiance. For known systematic and(More)
Although the euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging intercluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multidimensional scaling (MDS) where one can often observe nonintuitive(More)
Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multi-field visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate(More)
Single particle mass spectrometers are sophisticated instruments designed to measure the sizes and compositions of a wide range of individual particles in situ, in real-time. They characterize hundreds of thousands or millions of particles, generating vast amounts of rich and complex data, the proper mining of which requires dedicated state of the art(More)
Clustering is an important preparation step in big data processing. It may even be used to detect redundant data points as well as outliers. Elimination of redundant data and duplicates can serve as a viable means for data reduction and it can also aid in sampling. Visual feedback is very valuable here to give users confidence in this process. Furthermore,(More)
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