The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main difficulties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most effective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a fixed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the fixed quantity of mel-cepstral coefficients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number of computer experiments confirmed expediency of the fact that the use of the offered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the field of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the offered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.

Position time series from permanent Global Navigation Satellite System (GNSS) stations are commonly used for estimating secular velocities of discrete points on the Earth’s surface. An understanding of background noise in the GNSS position time series is essential to obtain realistic estimates of velocity uncertainties. The current study focuses on the investigation of background noise in position time series obtained from thirteen permanent GNSS stations located in Nepal Himalaya using the spectral analysis method. The power spectrum of the GNSS position time series has been estimated using the Lomb–Scargle method. The iterative nonlinear Levenberg–Marquardt (LM) algorithm has been applied to estimate the spectral index of the power spectrum. The power spectrum can be described by white noise in the high frequency zone and power law noise in the lower frequency zone. The mean and the standard deviation of the estimated spectral indices are −1.46±0.14,−1.39±0.16 and −1.53±0.07 for north, east and vertical components, respectively. On average, the power law noise extends up to a period of ca. 21 days. For a shorter period, i.e. less than ca. 21 days, the spectra are white. The spectral index corresponding to random walk noise (ca. –2) is obtained for a site located above the base of a seismogenic zone which can be due to the combined effect of tectonic and nontectonic factors rather than a spurious monumental motion. Overall, the usefulness of investigating the background noise in the GNSS position time series is discussed.

In this study, the temperature influence on the spectral responsivity of a Light Emitting Diode (LED) used as a photoreceptor, combined to light source spectrum is correlated to electrical characteristics in order to propose an alternative method to estimate LED junction temperature, regardless of the absolute illumination intensity and based on the direct correlation between the integral of the product of two optical spectra and the photo-generated currents. A laboratory test bench for experimental optical measurements has been set in order to enable any characterizing of photoelectric devices in terms of spectral behaviour, in a wavelength range placed between 400–1000 nm, and of current-voltage characteristics as function of temperature by using two different illumination sources. The temperature is analysed in a range from 5°C up to 85°C, so as to evaluate thermal variation effects on the sensor performance. The photo-generated current of two LEDs with different peak wavelengths has been studied. Research has observed and mathematically analysed what follows: since the photo-generated current strictly depends on the combination between the spectral response of the photoreceptor and the lighting source response, it becomes possible to estimate indirectly the junction temperature of the LEDs by considering the ratio between the photogenerated currents obtained by using two different illumination sources. Such results may for one thing increase knowledge in the fields where LEDs are used as photo-detectors for many applications and for another, they could be extended to generic photodetectors, thus providing useful information in photovoltaic field, for instance.

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time−temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user−independent. We implemented and evaluated 3 different 3D time−series registration methods: 1. Linear affine, 2. Non−linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons’ registration method outperforms also with the best breast alignment and non−negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons’ registration as an effective technique for time−series dynamic infrared registration, to stabilize the local temperature oscillation.