Accurate transfer function estimation of linear, noise-free, dynamic systems is an easy task for SigLabTM. Often, however, the system being analyzed is noisy or not perfectly linear. All realworld systems suffer from these deficiencies to some degree, but control systems are usually the worst offenders. Obtaining an accurate transfer function estimation from a noisy and nonlinear system require...

We show how to optimize the shape of the transfer function of a linear time invariant (LTI) single-input-single-output (SISO) system. Since any transfer function is rational, this can be formulated as an optimization problem for the coefficients of polynomials. After characterizing the cone of polynomials which are nonnegative on intervals, we formulate this problem using semidefinite programmi...

The transient motion and the heat transfer of a viscous incompressible flow contained between two vertically eccentric spheres maintained at different temperatures and rotating about a common axis with different angular velocities is numerically considered when the angular velocities are an arbitrary functions of time. The resulting flow pattern, temperature distribution, and heat transfer char...

This paper presents an exact analytical solution for two-dimensional conductive heat transfer in spherical composite pressure vessels .The vessels are in spherical shape and fibers are winded in circumferential direction. The analytical solution is obtained under the general boundary conditions which consist of convection, conduction and radiation inside/outside of vessel. The heat transfer equ...

Several fuzzy connectives, including those proposed by Lotfi Zadeh, can be seen as linear extensions of the Boolean connectives from the scale ${0,1}$ into the scale $[0,1]$. We discuss these extensions, in particular, we focus on the dualities arising from the Boolean dualities. These dualities allow to transfer the results from some particular class of extended Boolean functions, e.g., from c...

Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide flexible decision borders. Networks based on such transfer functions should be small and accurate. Several possibilities of using transfer functions of different types in neural models are discussed, including enhancement of i...

Most direct volume renderings produced today employ onedimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract materials and their boundaries for both scalar and multivariate data...