Analysis of the influence of the plasma thermodynamic regime in the spectrally resolved and mean radiative opacity calculations of carbon plasmas in a wide range of density and temperature

In this work the spectrally resolved, multigroup and mean radiative opacities of carbon plasmas are calculated for a wide range of plasma conditions which cover situations where corona, local thermodynamic and non-local thermodynamic equilibrium regimes are found. An analysis of the influence of the thermodynamic regime on these magnitudes is also carried out by means of comparisons of the results obtained from collisional-radiative, corona or Saha–Boltzmann equations. All the calculations presented in this work were performed using ABAKO/RAPCAL code.

Parametrization of the average ionization and radiative cooling rates of carbon plasmas in a wide range of density and temperature

In this work we present an analysis of the influence of the thermodynamic regime on the monochromatic emissivity, the radiative power loss and the radiative cooling rate for optically thin carbon plasmas over a wide range of electron temperature and density assuming steady state situations. Furthermore, we propose analytical expressions depending on the electron density and temperature for the average ionization and cooling rate based on polynomial fittings which are valid for the whole range of plasma conditions considered in this work.

Classification of natural flow regimes in the Ebro basin (Spain) by using a wide range of hydrologic parameters

Clasificación de tipos de régimenes naturales de caudales a partir de parámetros de tres componentes del régimen fluvial: magnitud, frecuencia y duración.

Depth-based face recognition using local quantized patterns adapted for range data

A depth-based face recognition algorithm specially adapted to high range resolution data acquired by the new Microsoft Kinect 2 sensor is presented. A novel descriptor called Depth Local Quantized Pattern descriptor has been designed to make use of the extended range resolution of the new sensor. This descriptor is a substantial modification of the popular Local Binary Pattern algorithm. One of the main contributions is the introduction of a quantification step, increasing its capacity to distinguish different depth patterns. The proposed descriptor has been used to train and test a Support Vector Machine classifier, which has proven to be able to accurately recognize different people faces from a wide range of poses. In addition, a new depth-based face database acquired by the new Kinect 2 sensor have been created and made public to evaluate the proposed face recognition system.