The review of the main methods for lossless images compression that can be used in remote sensing tasks are given. The major advantages and disadvantages of image compression methods in terms of implementation on FPGA are shown. The recommendations are made for the use of compression techniques in the construction of onboard systems for remote sensing.

This article considers the task of developing mathematical models and their computer implementation that would establish the dependence of pH (acidity of the environment) on the volume and structure of raw materials for daily loading, as well as on the operating parameters of temperature and humidity based on the interval analysis of experimental data obtained during BGP research of a given type. In the process of research, based on the developed interval models, it was established that this indicator depends on the volume and structure of raw materials, as well as on the temperature and humidity of the substrate in the bioreactor. To build this mathematical model, it is proposed to use the method of interval data analysis and the method of identification of model parameters based on multidimensional optimization. The results of experimental studies for a specific type of biogas plant are given, and interval models with guaranteed prognostic properties that characterize the pH of the environment depending on the specific type of bio-raw material of solid and liquid fractions, temperature, and humidity are obtained. Based on the use of different types of raw materials, the developed models, based on experimental data, describe different configurations of the structure and volumes of raw materials for daily loading. The obtained mathematical models are an algebraic nonlinear equation that can be applied to control the level of pH of the environment in the bioreactor by determining the optimal volumes of raw materials of each type during the loading period depending on the temperature and humidity of the substrate in the bioreactor.

An informativeness of the data has always been of great interest within a machine learning community. Nowadays, with a skyrocketing advancement of the artificial intelligence and massive volumes of noisy data, it becomes even more essential to develop a robust and effective methods for training data optimization. Existing approaches are mostly based on empirical trials and errors, with either stochastic or deterministic data reduction strategies. The key limitation of such solutions is that they do not consider the overall informativeness of the resulting training dataset. In this paper, we propose a novel approach for a quality aware dataset optimization by initial assessment of its informativeness. As a metric of informativeness, we use entropy values calculated over the target dataset. To alleviate the computational complexity, we perform initial clustering of the dataset into many subsets and calculate the entropy of each cluster independently. The dataset is then optimized by dynamic programming to find a sequence of subsets with low overall entropy according to imposed size limitations. The experimental evaluation shows the that proposed approach shows improvement over current best alternatives in terms of accuracy, precision, recall and F1-score metrics. Moreover, the proposed approach provides an excellent interclass discrimination even for a large number of classes.

An overview of the features of the design and development of the Virtual Library information system was conducted. A new approach is proposed for designing and developing the Virtual Library information system for preservation and development of e-books in the format of MARC 21. The model of information system Virtual Library is proposed.