Том 28, № 3 (2019)

In the present paper we analyzed a change in the density of states of a two-dimensional Ising model when a next-next-neighbor interaction is introduced. In other words, we examined two-dimensional lattices with diagonal connections. The same as in a three-dimensional model in this case each spin has 6 connections. Since the model is planar, we can calculate the free energy and other characteristics of the system using a polynomial algorithm. We performed computer simulations using the Kasteleyn–Fisher algorithm, which allowed us to study changes of critical values and the density of states when a long-range interaction is taken into account. From the obtained results it follows that the interactions of the type analyzed here result in quantitative changes of the system’s characteristics, but they did not change them qualitatively. In particular, we again obtained a logarithmic divergence of the heat capacity in the critical point.

An overview of known works in active vision area and our recent results on application of the foveal visual preprocessor to detect the head motion parameters are presented. In overview, the main directions of research and development in the field of artificial foveal active vision have been considered. It is justified that: (i) for a successful solution of complex problems in this area and creation of universal systems based on active foveal vision, it is necessary to develop new technologies and platforms for the experimental study of various aspects of active foveal vision in detail; (ii) at present, software implementations of the foveal principles of visual information processing already contribute to solving particular applied problems of computer vision. In computer simulation, detection of the initial moment of head motion was evaluated by means of foveal visual preprocessor. In this neural network, each pair of excitatory and inhibitory neurons has common center, different sizes of their receptive fields and time delay. To test network performance, synthetic video of facial image sequences from SYLAHP database monitoring the head motion were used. It was shown that the U^E amplitude and polarity qualitatively correspond to face motion amplitude and direction. The initial front of U^E changes corresponding to quick motion of head was equal to 12 ms in all cases (n = 46). Video of real face images with graduated turns was tested too to estimate quantitative relation between output function of excitatory neuron and turn degree. It was revealed that this relation is equal to 40 U^E/degree. Future steps of research in this direction have been shortly discussed.

This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations techniques to find out landscape such as agriculture, vegetation, land use/cover, water bodies and forest. Geographic Information System (GIS) technology is becoming an essential tool to combing multiple maps and information from different sources as satellite, field and socio-economic data. Landsat 8 and Sentinel-2 satellite data were used to generate NDVI time series from Sep. 2017 to Nov. 2018. This research work was the procedure by pre-processing, signal filtering and interpolation of monthly NDVI time series that represent a complete crop phonological cycle. NDVI method is applied according to its specialty range from –1 to +1. We divided whole agriculture area into five part according to NDVI Values such as no agriculture, low agriculture, medium agriculture, high agriculture and very high agriculture area. The simulation results show that the NDVI is highly useful in detecting the surface feature of the area, which is extremely beneficial for sustainable development of agriculture and decision making. The methodology of reform NDVI time series had been providing feasible to improve crop phenology mapping.

Arrival time of public vehicles to transport stops is a key point of information systems for passengers. Accurate information on the arrival time is important for travel arrangements since it helps to decrease the wait time at a stop and to choose an optimal alternate route. Recently, such information has been included to mobile navigation applications too. In the present paper, we analyze the abilities of the LSTM neural network to predict the arrival time of public vehicles. This model accounts for heterogeneous information about transport situation that directly or indirectly has an impact on the travel time prediction and includes statistical and real-time data of traffic flow. We examined the model experimentally using traffic data on bus routes in the city of Samara, Russia. The obtained results confirm that the predictions provided by our model are of a high quality and it can be used for real-time arrival time prediction of public transport in the case of a large-scale transportation network.