Context. Automated wood species recognition from macroscopic sections must combine high accuracy with stringent computational and memory requirements typical of peripheral devices and UAV onboard platforms. The heterogeneous, partially self-similar structure of wood texture, combining self-similar mid-level fluctuations with locally high-contrast elements and a large spread of leaders reflecting multifractality, motivates the creation of a fractal neural network architecture that can encode multiscale patterns while remaining computationally efficient.Objective. The goal is to develop and validate fractal neural network models that achieve high recognition quality at reduced computational cost, enabling practical deployment on systems with limited resources.Method. A fractal neural architecture (FractalNet) is employed to realize a self similar multi branch topology that forms an ensemble of receptive fields from fine to coarse scales, coherently modeling the spectrum of local regularities captured by leaders. The approach is benchmarked against ResNet50 and VGG16 with and without data augmentation on a 12 class macroscopic image task. Evaluation includes per class precision, recall, and F1, macro/weighted aggregates, confusion matrices, and analysis of numerical complexity in terms of trainable parameters and layer depth to assess deployability.Results. The FractalNet combined with augmentation procedure attains the best overall performance, reaching macro F1 = 0.80, weighted F1 = 0.81, and accuracy = 0.81, outperforming ResNet50 (macro F1 = 0.57) and VGG16 (macro F1 = 0.71). Confusion matrices exhibit reduced cross class confusions, indicating more uniform gains across species. Despite superior accuracy, FractalNet contains ~0.37 M parameters versus 23.6 M in ResNet50 and 65.1 M in VGG16, yielding a markedly smaller memory footprint and lower inference latency.Conclusions. The alignment between multifractal texture properties of wood and the self similar design of FractalNet produces a favorable accuracy-efficiency trade off. The method delivers state of the art recognition quality while preserving computational frugality, thus enabling reliable use in resource constrained scenarios, including UAV based and other edge deployments
Context. Optimizing search in multidimensional catalogs of radio-electronic components (sensors, microcontrollers, communication modules) is a crucial task for computer-aided design systems, electronic component logistics management, and intelligent technical support systems. The complexity arises due to the high dimensionality of the parameter space (operating frequencies, power consumption, temperature ranges, etc.), data heterogeneity, and the high frequency of complex queries combining numerical constraints with categorical filters. Classic indexing algorithms from relational database management systems are inefficient for this specific domain, which slows down the performance of real-time information systems. Modern indexing technologies for searching multi-attribute catalogs of technical components are moving away from the paradigm of universal one-dimensional structures in favor of specialized and hybrid approaches tailored to the nature of technical data. The focus has shifted from fast single-key search to efficient pruning of multidimensional parameter space. To achieve this, spatial indexes are actively used, interpreting each component as an object in an N-dimensional space, where each technical parameter is a separate axis. This allows a single query to the index to find all entries that fall within a specified multidimensional hyperrectangle. Simultaneously, technologies that treat search as an information retrieval problem are evolving. Categorical attributes, such as interface type or manufacturer, are indexed using inverted indexes or compressed bitmap indexes, which provide ultra-fast execution of AND/OR operations over large sets. A separate direction involves the use of vector representations (embeddings) of technical characteristics, obtained using machine learning models, followed by indexing using specialized structures for nearest neighbor search. This enables semantic search by technical description or finding analogous components. A key issue in indexing for search within multi-attribute catalogs of technical components is the selection and combination of data structures that effectively prune the search space across all relevant dimensions simultaneously, minimizing the retrieval of iirrelevant data in the early stages of query execution.Therefore, the development of a new method that systematically combines the strengths of modern approaches within a unified adaptive architecture is a relevant scientific and technical problem. Its solution will significantly reduce the execution time of complex queries in key information systems for the fields of radio electronics, telecommunications, and automated engineering, addressing the challenges of production digitalization and intelligent data processing.Objective. Development of a combined indexing method for efficient execution of complex search queries in multi-dimensional catalogs of technical components.Method. A combined indexing method is proposed, which combines R-tree for multidimensional filtering of numericalparameters and inverted indexes for categorical features. The method for improving search efficiency is based on an adaptive query planner that dynamically chooses the optimal execution strategy (Index-First, Parallel-Merge or Full-Scan) based on an assessment of the selectivity of the conditions.Results. The problem was formulated, and a combined indexing method was developed for multidimensional catalogs of radioelectronic components. In the course of the study, a formalized mathematical search model was created that takes into account the selectivity of numerical and categorical conditions, and algorithms for automatic distribution of attributes between index types and dynamic selection of the query execution strategy were developed. A hybrid index architecture was proposed that combines an R-tree for indexing numerical parameters and inverted indexes for categorical features, as well as mathematical models for assessing selectivity and optimizing the query execution plan. Experimental studies on a synthetic data set confirmed the effectiveness of the developed method, demonstrating a reduction in the execution time of complex queries compared to basic indexing based on B-trees.Conclusions. The work develops a combined indexing method for efficient execution of complex multi-attribute queries in radioelectronic component catalogs. The method is based on a formalized mathematical search model, which allowed building a hybrid index architecture. This architecture integrates an R-tree for filtering numerical parameters, inverted indexes for categorical features, and an adaptive planner that dynamically selects the optimal query execution strategy (Index-First, Parallel-Merge, FullScan) based on an assessment of the selectivity of conditions. An algorithm for the automatic distribution of attributes between index types is developed. Experimental modeling confirmed the effectiveness of the method, showing a 35–55% reduction in the execution time of complex queries compared to traditional B-tree-based indexing, especially for queries typical of engineering component selection. The results obtained prove the feasibility of using combined indexing to increase the productivity of information systems working with multidimensional technical catalogs
Context. The relevance of the article is due to the need for further development of simple algorithms for analyzing the structural connectivity of network objects, in reducing the computational complexity and increasing the functional capabilities of such algorithms. The linear algorithm proposed in the article can be applied: in urban and rural transport route planning systems; in solving various types of problems related to carrying out restoration work on network objects after their destruction and the loss of certain connections between the elements of such objects (power supply systems, communication systems, transport networks, etc.); in developing options for effectively destroying key objects of the enemy’s critical infrastructure; in analyzing social networks for grouping individuals into groups by interest, etc.Objective. The goal of the work is to develop a linear algorithm for searching for strongly connected components in the structure of network objects, which is resistant to changes in the structure of objects and has a polynomial computational complexity.Method. The physical object to be studied is modeled by a connected directed graph. To determine the structure of the model graph components of strong connectivity, the idea of analyzing the presence of direct and reverse transitive closures between all pairs of vertices of the model graph was applied. Since there is a route (or arc) connecting them between all pairs of vertices included in any component of strong connectivity, then in the matrix of transitive closures of binary relations of such a graph there will always be unit blocks (transitivity compactions). Individual elements of these blocks in the matrix of transitive closures are scattered in a certain way. By analyzing the matrix for the presence of identical rows and identical columns, it is possible to establish their belonging to certain unit blocks. After such analysis and grouping the corresponding rows and columns into unit blocks, it is possible to determine the number of components of strong connectivity, as well as the index (vertex) composition of each component.To construct a matrix of transitive closures of binary relations, any of the known algorithms can be used, as discussed in the article. Therefore, the linear algorithm proposed in this article is conventionally divided into two main stages: the first is the search for transitive closures of binary relations of a model directed graph; the second is the determination of the matrix of inverse reachability of the graph, the determination of the matrix of mutual reachability, and the search for unit blocks in the latter by lining them up on the main diagonal of the matrix.Results. 1) A theoretical basis for analyzing the matrix of transitive closures of binary relations of a model directed graph has been developed in order to determine the quantitative and index composition of strongly connected components in its composition; 2) In terms of graph theory, the search for strongly connected components in the structure of a network object is formalized; 3) The developed algorithm was verified for its ability to determine strongly connected components in the structure of a directed graph.Conclusions. Theoretical studies and a number of experiments confirm the possibility of using the proposed algorithm in the tasks of structural analysis of network objects. Since the well-known provisions of graph theory were used in the theoretical studies, which are interpreted unambiguously in this theory, the absence of any probabilistic processes allows us to consider the developed algorithm accurate. Polynomial estimates of its computational complexity allow it to be used in real-time scale
Context. Ensuring robust, adaptable, and compute-efficient object detection in UAV aerial imagery under distribution shifts, structured and unstructured noise, and strict onboard latency/energy budgets is an urgent scientific task. A compute-aware detector and a complementary training/adaptation method that integrate a dynamic transformer backbone with gate units, parameter-efficient adapters, and resource-bounded test-time adaptation to sustain accuracy under realistic perturbations and domain shift.Objective. Development of a model and method for object detection in aerial imagery that jointly provide robustness and adaptability while meeting embedded compute and real-time constraints typical of onboard UAV systems.Methods. The approach combines dynamic neural networks with Gumbel-Softmax gate units over a ViT-T/16 backbone, a Simple FPN and a RetinaNet-like one-stage head, budget-aware losses that target a desired dynamic compression rate, structured procedural noise (Perlin, Gabor, Worley) for robustness training, LeakyReLU6 with a straight-through estimator for stable gradients, and test-time adaptation via objectness-weighted marginal-entropy minimization on lightweight adapters.Results. On VEDAI, a gated ViT-T/16 detector reaches mAP@0.5 of 0.77 at ~5.0 GFLOPs and 17.8 FPS, rising to 0.79 with adapters and TTA, whereas a static counterpart attains 0.74 at 9.6 GFLOPs and 10.6 FPS; pretraining with procedural noise lifts accuracy further to 0.80 (gated) and 0.82 (gated+TTA) with minimal compute overhead. Under domain shift (trained on VisDrone, evaluated on VEDAI), dynamic gating and TTA improve mAP from 0.54 to 0.60 without noise pretraining and up to 0.66 with it, sustaining ~5.4–5.6 GFLOPs and ~16–17 FPS within an 8–10 GFLOPs budget on 4×A76 CPUs.Conclusions. The proposed object detection model and method – combining dynamic gating, perturbation-aware training, andbudgeted test-time adaptation – reduce average compute while increasing robustness and adaptability, yielding a superior accuracythroughput trade-off for UAV onboard deployment under real-world disturbances and distribution shifts.
Context. The modern information society, with its rapidly expanding means of information exchange and globalized data, offers the opportunity to study information dissemination, investigate its impact, and create information security systems. Mathematical modeling methods are widely used in the development of information dissemination systems and technologies. Thermal, diffusion, and mechanical processes – that is, processes occurring in a continuous medium – are considered fundamental. Among the various modeling approaches, equations of mathematical physics, which formalize the fundamental laws of substance transfer, are often used.Objective. The aim of this paper is to develop a comprehensive, formalized approach to numerically modeling the dynamics of information dissemination processes in social networks based on the principles of cellular automata and diffusion models. The subject of the study is the analysis of the dynamics of the level of information generated based on the internal behavior of each network cell, assuming that its initial state is formed as a result of the influence of certain processes outside the information communities.Method. This paper proposes a hybrid approach to modeling information dissemination processes based on cellular automata, which observes the internal and external dynamics of individual cells. The state change of the automata model is described by a specified transition function and rules for generating output signals, while a diffusion approach based on heat conduction principles is used to formalize internal changes in the cell’s state. Computational experiments were conducted to model the dynamics of information dissemination processes, taking into account various types of external influences from others, and calculations of information dissemination dynamics indicators in social network communities are presented.Results. The proposed hybrid model allows us to describe and analyze information dissemination processes within a social group, which is formed, for example, from social network subscribers. The group is divided into subgroups that are relatively homogeneous in terms of specific indicators in the form of online communities.Information processes were modeled taking into account various external influences from surrounding environments (using von Neumann and Moore neighborhoods as examples) with explicitly defined transition and exit functions. Internal information dissemination processes in specific cells were formalized using scalar heat equations. Numerical calculations of information dissemination dynamics in each community and the group as a whole were obtained, and a solution to information content problems (participants’ attitudes toward a specific problem) in subgroups was proposed based on determining the locations of centers of iinfluence.Conclusions. This article examines the application of cellular automata principles to modeling the dynamics of information dissemination processes. A new approach is proposed that, in addition to the automata model, considers the internal behavior of each cell, assuming that its initial state is formed as a result of certain intracellular processes at each time interval. In other words, a hybrid version of a cellular automaton for observing the internal and external dynamics of individual cells is considered, whereby the change in the states of the automaton model is described by a given transition function with certain rules for generating output signals, and a “mechanistic” approach based on the principles of thermal conductivity is used to formalize internal changes in the state of the cell.
