Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Computer science & Fracture mechanics. The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

Engineering properties of concrete containing natural zeolite as supplementary cementitious material: Strength, toughness, durability, and hygrothermal performance

Abstract: A complex analysis of engineering properties of concrete containing natural zeolite as supplementary cementitious material in the blended Portland-cement based binder in an amount of up to 60% by mass is presented. The studied parameters include basic physical characteristics, mechanical and fracture–mechanics properties, durability characteristics, and hygric and thermal properties. Experimental results show that 20% zeolite content in the blended binder is the most suitable option. For this cement replacement level the compressive strength, bending strength, effective fracture toughness, effective toughness, and specific fracture energy are only slightly worse than for the reference Portland-cement concrete. The frost resistance, de-icing salt resistance, and chemical resistance to MgCl2, NH4Cl, Na2SO4, and HCl are improved. The hygrothermal performance of hardened mixes containing 20% natural zeolite, as assessed using the measured values of water absorption coefficient, water vapor diffusion coefficient, water vapor sorption isotherms, thermal conductivity, and specific heat capacity, is satisfactory.

Simplified description of optical forces acting on a nanoparticle in the Gaussian standing wave

Abstract: We study the axial force acting on dielectric spherical particles smaller than the trapping wavelength that are placed in the Gaussian standing wave. We derive analytical formulas for immersed particles with relative refractive indices close to unity and compare them with the numerical results obtained by generalized Lorenz-Mie theory (GLMT). We show that the axial optical force depends periodically on the particle size and that the equilibrium position of the particle alternates between the standing-wave antinodes and nodes. For certain particle sizes, gradient forces from the neighboring antinodes cancel each other and disable particle confinement. Using the GLMT we compare maximum axial trapping forces provided by the Gaussian standing-wave trap (SWT) and single-beam trap (SBT) as a function of particle size, refractive index, and beam waist size. We show that the SWT produces axial forces at least ten times stronger and permits particle confinement in a wider range of refractive indices and beam waists compared with those of the SBT.

Pairwise Discriminative Speaker Verification in the ${\rm I}$ -Vector Space

Abstract: This work presents a new and efficient approach to discriminative speaker verification in the i-vector space. We illustrate the development of a linear discriminative classifier that is trained to discriminate between the hypothesis that a pair of feature vectors in a trial belong to the same speaker or to different speakers. This approach is alternative to the usual discriminative setup that discriminates between a speaker and all the other speakers. We use a discriminative classifier based on a Support Vector Machine (SVM) that is trained to estimate the parameters of a symmetric quadratic function approximating a log-likelihood ratio score without explicit modeling of the i-vector distributions as in the generative Probabilistic Linear Discriminant Analysis (PLDA) models. Training these models is feasible because it is not necessary to expand the i -vector pairs, which would be expensive or even impossible even for medium sized training sets. The results of experiments performed on the tel-tel extended core condition of the NIST 2010 Speaker Recognition Evaluation are competitive with the ones obtained by generative models, in terms of normalized Detection Cost Function and Equal Error Rate. Moreover, we show that it is possible to train a gender-independent discriminative model that achieves state-of-the-art accuracy, comparable to the one of a gender-dependent system, saving memory and execution time both in training and in testing.