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The article presents the intermediate results of the development of a control system for a remotely piloted agricultural aircraft (RPAA). The concept of using an automated complex for performing aerochemical work (ACW) designed for processing fields, water areas, forests with the purpose of protection from pests of plants, fertilization is developed. The basic component of the complex is a manned agricultural aircraft MV-500 developed by LLC “Firm “MVEN” (Kazan). The use of the aircraft in unmanned mode will provide an increase in the productivity of the aircraft, will increase the payload.

The article defines the composition of the complex for automation of ACW: aircraft, ground control center, onboard equipment for automated control of the aircraft and the formation of a map of the heights of the section being processed, and the satellite precise positioning system necessary to automate the control of the aircraft. The aircraft is equipped with an automated control system that provides remote control of take-off and landing and automatic control of the flight trajectory at extremely low altitude when performing ACW and performing spatial turns at the boundaries of the treated areas. It is proposed to take off, landing, dropping an aircraft into the ACW exercise area by means of a pilot operator from a ground control station. The ground control point should provide reception and display on the operator's screen of flight information and several types from the aircraft. The operator can control alternately several aircraft during these phases of flight with the help of ground control authorities. In the future, it is planned to automate these stages of flight, leaving behind the pilot-operator control functions and remote control capabilities in special cases. For the navigation of the aircraft, when performing ACW on board, RTK (Real Time Kinematic) equipment is installed, providing a measurement with centimeter accuracy of coordinates and aircraft heights relative to the base station installed in the ground control station. Before the implementation of ACW, a three-dimensional digital map of the processed area is built by adding existing cadastral maps with measurements of the elevations of the section carried out with the help of on-board radio and optical altimeters of the same aircraft.

To date, the following system components have been manufactured and tested: a remotely controlled model of the MV-500 aircraft at a scale of 1:5, a satellite positioning system; system for obtaining images and telemetry information from the board model; autopilot; methods of obtaining three-dimensional digital maps of sections and planning flight trajectories for ACW.

Image denoising is one of the fundamental problems in digital image processing. This problem usually refers to the reconstruction of an image from an observed image degraded by noise. There are many factors that cause this degradation such as transceiver equipment, or environmental influences, etc. In order to obtain higher quality images, many methods have been proposed for image denoising problem. Most image denoising method are based on total variation (TV) regularization to develop efficient algorithms for solving the related optimization problem. TV-based models have become a standard technique in image restoration with the ability to preserve image sharpness.

In this paper, we focus on Poisson noise usually appearing in photon-counting devices. We propose an effective regularization model based on combination of first-order and fractional-order total variation for image reconstruction corrupted by Poisson noise. The proposed model allows us to eliminate noise while edge preserving. An efficient alternating minimization algorithm is employed to solve the optimization problem. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

The program for automatic revealing of threshold values for characterizing physiological state of vessels and detection of early stages of retina pathology is offered. The algorithm is based on checking character of crossing sites of vessel images with the "mask" consisting of concentric circumferences (the first circumference is imposed directly on the sclera capsules of an optic nerve disk). The new method allows revealing of a network of blood vessels and flanking zones and detection of initial stage of pathological changes in a retina by digital images.

Segmentation of medical image is one of the most challenging tasks in analysis of medical image. It classifies the organs pixels or lesions from medical images background like MRI or CT scans, that is to provide critical information about the human organ’s volumes and shapes. In scientific imaging field, medical imaging is considered one of the most important topics due to the rapid and continuing progress in computerized medical image visualization, advances in analysis approaches and computer-aided diagnosis. Digital image processing becomes more important in healthcare field due to the growing use of direct digital imaging systems for medical diagnostics. Due to medical imaging techniques, approaches of image processing are now applicable in medicine. Generally, various transformations will be needed to extract image data. Also, a digital image can be considered an approximation of a real situation includes some uncertainty derived from the constraints on the process of vision. Since information on the level of uncertainty will influence an expert’s attitude. To address this challenge, we propose novel framework involving interval concept that consider a good tool for dealing with the uncertainty, In the proposed approach, the medical images are transformed into interval valued representation approach and entropies are defined for an image object and background. Then we determine a threshold for lower-bound image and for upper-bound image, and then calculate the mean value for the final output results. To demonstrate the effectiveness of the proposed framework, we evaluate it by using synthetic image and its ground truth. Experimental results showed how performance of the segmentation-based entropy threshold can be enhanced using proposed approach to overcome ambiguity.

Data hiding in digital images is a promising direction of cybersecurity. Digital steganography methods provide imperceptible transmission of secret data over an open communication channel. The information embedding efficiency depends on the embedding imperceptibility, capacity, and robustness. These quality criteria are mutually inverse, and the improvement of one indicator usually leads to the deterioration of the others. A balance between them can be achieved using metaheuristic optimization. Metaheuristics are a class of optimization algorithms that find an optimal, or close to an optimal solution for a variety of problems, including those that are difficult to formalize, by simulating various natural processes, for example, the evolution of species or the behavior of animals. In this study, we propose an approach to data hiding in the hybrid spatial-frequency domain of digital images based on metaheuristic optimization. Changing a block of image pixels according to some change matrix is considered as an embedding operation. We select the change matrix adaptively for each block using metaheuristic optimization algorithms. In this study, we compare the performance of three metaheuristics such as genetic algorithm, particle swarm optimization, and differential evolution to find the best change matrix. Experimental results showed that the proposed approach provides high imperceptibility of embedding, high capacity, and error-free extraction of embedded information. At the same time, storage of change matrices for each block is not required for further data extraction. This improves user experience and reduces the chance of an attacker discovering the steganographic attachment. Metaheuristics provided an increase in imperceptibility indicator, estimated by the PSNR metric, and the capacity of the previous algorithm for embedding information into the coefficients of the discrete cosine transform using the QIM method [Evsutin, Melman, Meshcheryakov, 2021] by 26.02% and 30.18%, respectively, for the genetic algorithm, 26.01% and 19.39% for particle swarm optimization, 27.30% and 28.73% for differential evolution.