The problem of finding all shortest paths between vertices in a graph (APSP) has real-life applications in planning, communication, economics and many other areas. APSP problem can be solved using various algorithms, starting from Floyd-Warshall’s algorithm and ending with advanced, much faster blocked algorithms like Heterogeneous Blocked All-Pairs Shortest Path Algorithm designed to fully utilize underlying hardware resources and utilize inter-data relationships. In the paper, we propose a novel Blocked all-pairs Shortest Paths algorithm for Clustered Graphs (BSPCG) (in sequential and parallel forms) which utilizes the graph clustering information to significantly reduce the number of calculations by performing shortest paths search only though bridge vertices between clusters. We performed a set of comparing experiments for BSPCG and standard Blocked All-Pairs Shortest Path (BFW) algorithm on four randomly generated graphs of 4800 and 9600 vertices with different cluster configurations to determine the efficiency of calculation of paths passing through bridge vertices. All experiments were executed on a computer with two Intel Xeon E5-2620v4 processors (8 cores, 16 hardware threads and shared 20 MB L3 cache). In all the experiments the novel BSPCG algorithm outperformed the standard BFW algorithm. In single-threaded scenarios, BSPCG outperformed BFW up to 4.6 times on graphs of 4800 vertices and up to 2.7 times on graphs of 9600 vertices. In the multi-threaded scenarios, BSPCG also outperformed BFW up to times on graphs of 4800 vertices and up to 2.7 times on graphs of 9600 vertices. The proposed algorithm can be used in scenarios where clustering information stays intact or slightly modified based on the changes in graph and can be reused for future calculation of all-pairs shortest paths in the graph.

The paper discusses the key factors and trends in the design and production of printed circuit boards (PCB), which determine the state of the art of the automatic PCB inspection. To search for and classify defects, it is proposed to use the method of detecting defects in images based on the YOLO family of object detection models. The model was trained on a public set of images of PCB with 6 classes of defects, and the accuracy was assessed using generally accepted metrics. On the test dataset, the average accuracy according to the mAP50 metric is 0.98.

The article analyzes various methods for constructing motion trajectories of unmanned aerial vehicles (UAVs) when flying over specified areas of space. The analytical results of the studies are presented in the form of qualitative illustrations of the methods under consideration, as well as the proposed methodology for forming an optimal UAV flight trajectory along the boundaries of a given area of space, taking into account the corresponding processes of changing the control acceleration and speed of the UAV and allowing the formation of requirements for the UAV control system at the preliminary development stage. An analysis of methods for forming UAV trajectories is carried out, on the basis of which a conclusion is made about the operability of the proposed methodology for forming the optimal UAV trajectory when flying over a given area of space and the feasibility of its further use as the basis for the initial stage of synthesis of the UAV control system.

An optical-electronic correlation-extreme navigation system for an unmanned aerial vehicle has been developed. The proposed navigation system is characterized by the use of a small-sized and affordable digital camera and computer, which is critical for small military unmanned aerial vehicles. The developed algorithmic support for the system is distinguished by the use of algorithms for reconstructing a three-dimensional digital terrain model using optical digital images from an on-board digital camera, as well as algorithms for correlation processing of current and reference models, which ensures the possibility of accurate autonomous air navigation using view information. The requirements for the flight parameters of a small unmanned aerial vehicle with an optical-electronic correlation-extreme navigation system are substantiated. The values of the true speed of an unmanned aerial vehicle and the correction period of the on-board strapdown inertial navigation system are substantiated according to the data of the proposed optical-electronic correlation-extreme navigation system. The results of the developed optical-electronic correlation-extremal navigation system accuracy studies depending on the shooting parameters and the quality of the reference models are presented.

The widespread use of risograph printing in printing houses requires an increase in the speed of equipment while maintaining the printing quality of the original layouts. The problem of risograph printing is not always obtaining high-quality images, since the risographs used in the standard driver (periodic, non-periodic) do not always allow you to get the printed image the same as in the original. This is reflected in the insufficiency of parameters such as image clarity and sharpness. Problems of poor quality of risograph printing can be solved by changing the settings of the risograph driver for the original layout with additional use of digital filtering and image rasterization. This will require the creation of additional image rasterization methods to improve the initial indicators of the original layout in contrast, clarity, and the correctness of the transmission of halftone images and will improve the quality of printed images, as well as ensure the efficiency of the printing process for a wide range of original layouts.

The stages of imitating modeling are described in the article. The stages detail the following: construction of logical-mathematical model of investigated system, working out of the modeling algorithm that describes process of its functioning, working out of the program that realizes the algorithm, carrying out of experiments with imitating model. The authors suggest the description of the realization of the imitating modeling principles of in the decision-making support system of the organization of the remote sounding of the Earth. The features of the realization of the models of the casual choice of the modes of the remote sounding of the Earth, space vehicle, land objects are considered in the article.

The purpose of this work is to develop an IT diagnostic system for Parkinson's disease (PD) with remote access based on the Internet of Things (IoT) network.

Methods. The authors have developed a method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known datasets. In the architecture of the Internet of Things network, a smartphone is the point of initial data collection and preprocessing, including extracting features from an audio recording of the patient's voice and his motor activity. Data is transmitted via a local Flask server, which acts as a channel for sending functional data to the Open Semantic Technology for Intelligent Systems (OSTIS) server. The OSTIS server processes the data received from the local Flask server and uses a neural network prediction agent to recognize BP. This agent downloads features and makes predictions based on a trained neural network, linking these predictions with knowledge in the OSTIS system, and stores them in a database.

The result of the study is the architecture and algorithms of the IoT network. The workflow of the entire system includes data collection and preprocessing by the Internet of Things device, subsequent data transfer to the local Flask server, further forwarding to the OSTIS server, processing of the neural network model by a neural network predictor agent and, ultimately, linking the processed results to the knowledge graph and storing them in the system.

The BP remote IT diagnostics system provides real-time processing of patient data, recognition of disease signs on the Internet of Things, support for advanced analysis and decision-making for further treatment.