Starting with Wankel's classification of volume changing devices that are suitable for positive displacement type compressors, pumps or engines it is argued that other ways of classifying should also be explored. As a possible useful classification aid, the concept of generalized linkages is proposed. Using examples, it is illustrated how this concept aids the designer in obtaining inversions o...

The Tsallis $q$-Gaussian distribution is a powerful generalization of the standard Gaussian and commonly used in various fields, including non-extensive statistical mechanics, financial markets image processing. It belongs to $q$-distribution family, which characterized by non-additive entropy. Due their versatility practicality, $q$-Gaussians are natural choice for modeling input quantities me...

How to accelerate the convergence speed and avoid computing inversion of a Jacobian matrix is important in solution nonlinear algebraic equations (NAEs). This paper develops an approach with splitting-linearizing technique based on term reduce effect terms. We decompose terms NAEs through splitting parameter then linearize around values at previous step linear system. Through maximal orthogonal...

In this paper we present an inversion procedure for electromagnetic scattering, based on the coupled-dipole method (CDM) combined with an inversion algorithm making use of the singular value decomposition procedure associated with a regularization factor. This method permits to obtain images of non-homogeneous dielectric objects whose dimensions are comparable to the incident wavelength. The fe...

What we believe to be a new inversion procedure for multi- and hyperspectral data in shallow water, represented by the subsurface irradiance and remote sensing reflectance spectra, was developed based on analytical equations by using the method of nonlinear curve fitting. The iteration starts using an automatic determination of the initial values of the fit parameters: concentration of phytopla...

We examine how the recently explored class of linear transductions relates to finite-state models. Linear transductions have been neglected historically, but gainined recent interest in statistical machine translation modeling, due to empirical studies demonstrating that their attractive balance of generative capacity and complexity characteristics lead to improved accuracy and speed in learnin...