Využití filtrů typu dolní propust při spalování biomasy

Quantities measured during biomass combustion experiments are heavily burdened with a considerable noise. Usage of common linear low-pass filters is able to smooth measured time rows but it also introduces typical dynamic delay of filtered data. The article presents comparison of three commonly used linear filters – Butterworth, Bessel and Chebishev. An effort to smooth measured data without introducing dynamic delay led us to use some of less common non-linear filters. The article further presents usage of Threshold and Gaussian weighted average non-linear filters and compares them with the linear ones.Veličiny sledované při experimentech se spalováním biomasy jsou zpravidla významně zatížené silným šumem. Použití běžných lineárních filtrů s dolní propustí sice dokáže vyhladit průběhy měřených veličin, ale zavádí do filtrovaného signálu typické dynamické zpoždění. Tento článek porovnává použití tří běžně používaných lineární filtrů - Butterworthův, Besselův a Čebiševův. Další snaha o vyhlazení měřených dat bez zatížení dynamickým zpožděním vedla autory k použití méně běžných nelineárních filtrů. Článek tedy dále srovnává použití prahového filtru a filtru založeném na Gaussovo křivkou váženém průměru

Constriction size distributions of granular filters: a numerical study

The retention capability of granular filters is controlled by the narrow constrictions connecting the voids within the filter. The theoretical justification for empirical filter rules used in practice includes consideration of an idealised soil fabric in which constrictions form between co-planar combinations of spherical filter particles. This idealised fabric has not been confirmed by experimental or numerical observations of real constrictions. This paper reports the results of direct, particle-scale measurement of the constriction size distribution (CSD) within virtual samples of granular filters created using the discrete-element method (DEM). A previously proposed analytical method that predicts the full CSD using inscribed circles to estimate constriction sizes is found to poorly predict the CSD for widely graded filters due to an over-idealisation of the soil fabric. The DEM data generated are used to explore quantitatively the influence of the coefficient of uniformity, particle size distribution and relative density of the filter on the CSD. For a given relative density CSDs form a narrow band of similarly shaped curves when normalised by characteristic filter diameters. This lends support to the practical use of characteristic diameters to assess filter retention capability

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

Breaking the Degeneracy: Optimal Use of Three-point Weak Lensing Statistics

We study the optimal use of third order statistics in the analysis of weak lensing by large-scale structure. These higher order statistics have long been advocated as a powerful tool to break measured degeneracies between cosmological parameters. Using ray-tracing simulations, incorporating important survey features such as a realistic depth-dependent redshift distribution, we find that a joint two- and three-point correlation function analysis is a much stronger probe of cosmology than the skewness statistic. We compare different observing strategies, showing that for a limited survey time there is an optimal depth for the measurement of third-order statistics, which balances statistical noise and cosmic variance against signal amplitude. We find that the chosen CFHTLS observing strategy was optimal and forecast that a joint two- and three-point analysis of the completed CFHTLS-Wide will constrain the amplitude of the matter power spectrum $\sigma_8$ to 10% and the matter density parameter $\Omega_m$ to 17%, a factor of ~2.5 improvement on the two-point analysis alone. Our error analysis includes all non-Gaussian terms, finding that the coupling between cosmic variance and shot noise is a non-negligible contribution which should be included in any future analytical error calculations.Comment: 27 pages, 13 figures, 3 table