11,912 research outputs found
The fused Kolmogorov filter: A nonparametric model-free screening method
A new model-free screening method called the fused Kolmogorov filter is
proposed for high-dimensional data analysis. This new method is fully
nonparametric and can work with many types of covariates and response
variables, including continuous, discrete and categorical variables. We apply
the fused Kolmogorov filter to deal with variable screening problems emerging
from a wide range of applications, such as multiclass classification,
nonparametric regression and Poisson regression, among others. It is shown that
the fused Kolmogorov filter enjoys the sure screening property under weak
regularity conditions that are much milder than those required for many
existing nonparametric screening methods. In particular, the fused Kolmogorov
filter can still be powerful when covariates are strongly dependent on each
other. We further demonstrate the superior performance of the fused Kolmogorov
filter over existing screening methods by simulations and real data examples.Comment: Published at http://dx.doi.org/10.1214/14-AOS1303 in the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Refractometry of organosilica microspheres
The refractive index of novel organosilica (nano/micro)material is determined
using two methods. The first method is based on analysis of optical extinction
efficiency of organosilica beads versus wavelength, which is obtained by a
standard laboratory spectrometer. The second method relies on the measurable
trapping potential of these beads in the focused light beam (laser tweezers).
Polystyrene beads were used to test these methods, and the determined
dispersion curves of refractive index values have been found accurate. The
refractive index of organosilica beads has been determined to range from
1.60-1.51 over the wavelength range of 300-1100 nm.Comment: 9 pages, 8 figure
Information-based Preprocessing of PLC Data for Automatic Behavior Modeling
Cyber-physical systems (CPS) offer immense optimization potential for
manufacturing processes through the availability of multivariate time series
data of actors and sensors. Based on automated analysis software, the
deployment of adaptive and responsive measures is possible for time series
data. Due to the complex and dynamic nature of modern manufacturing, analysis
and modeling often cannot be entirely automated. Even machine- or deep learning
approaches often depend on a priori expert knowledge and labelling. In this
paper, an information-based data preprocessing approach is proposed. By
applying statistical methods including variance and correlation analysis, an
approximation of the sampling rate in event-based systems and the utilization
of spectral analysis, knowledge about the underlying manufacturing processes
can be gained prior to modeling. The paper presents, how statistical analysis
enables the pruning of a dataset's least important features and how the
sampling rate approximation approach sets the base for further data analysis
and modeling. The data's underlying periodicity, originating from the cyclic
nature of an automated manufacturing process, will be detected by utilizing the
fast Fourier transform. This information-based preprocessing method will then
be validated for process time series data of cyber-physical systems'
programmable logic controllers (PLC)
Fusion based analysis of ophthalmologic image data
summary:The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of ophthalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis approaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications
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