The combined effect on the variation of the in-situ deposition temperature and the variation of the applied power on the deposition rate (DR), gap energy (E_g), and resistivity (ρ) in barium strontium titanate thin films, deposited into RF (radio frequency)–magnetron cosputtering equipment, are presented in this research. The simultaneous action of two magnetrons (BaTiO₃ and SrTiO₃) is explained using the first and second derivative of Boltzmann’s sigmoidal equation. This found that a deposition gradient is a very novel analysis. Using the color-code lines built through MATLAB^® and analyzing the trend information, taking into account the influence of the calculated “x” parameter, by means of the Boltzmann’s sigmoidal equation fit, we propose a method to set up an RF–magnetron cosputtering system to predict the DR(x,T), E_g(x,T), and ρ(x,T) values of Ba_xSr_1−xTiO₃ solid solutions with 0 ≤ x ≤ 1 for amorphous and crystalline phases. This method can be a versatile tool to optimize the deposition process with, or without, in situ deposition temperature.

This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages --estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).

The Artificial Neural Network (ANN) concept is familiar in methods whose task is, for example, the identification or approximation of the outputs of complex systems difficult to model. In general, the objective is to determine online the adequate parameters to reach a better point-to-point convergence rate, so that this paper presents the parameter estimation for an equivalent ANN (EANN), obtaining a recursive identification for a stochastic system, firstly, with constant parameters and, secondly, with nonstationary output system conditions. Therefore, in the last estimation, the parameters also have stochastic properties, making the traditional approximation methods not adequate due to their losing of convergence rate. In order to give a solution to this problematic, we propose a nonconstant exponential forgetting factor (NCEFF) with sliding modes, obtaining in almost all points an exponential convergence rate decreasing. Theoretical results of both identification stages are performed using MATLAB® and compared, observing improvement when the new proposal for nonstationary output conditions is applied.

Un factor esencial en el desarrollo del proceso de enseñanza-aprendizaje mediante el cual el profesor suscita contenidos a un alumno en función de unos objetivos y dentro de un contexto, y donde se da la relación alumno-profesor, es el encuadre espacio-temporal. En este el proceso de enseñanza se desarrolla a través de las clases o tutorías dirigidas, cuya dimensión espacio-temporal es la misma para el alumno y el profesor. En este artículo se aborda el desarrollo de una alternativa remota a través del uso de tecnología inalámbrica y el empleo de dispositivos IoT y dispositivos wearables con el uso de plataformas MOOC. Los resultados experimentales obtenidos demuestran que este tipo de modalidad digital ofrece los mismos resultados que una clase presencial, sin perder la esencia del escenario del proceso de enseñanza-aprendizaje tradicional.

Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal.