Security of Infocommunication Systems and Internet of Things
The journal Security of Infocommunication Systems and Internet of Things publishes manuscripts not previously published and not submitted for publication to any other publishing house, containing original results of experimental or theoretical research, as well as review articles in the context of studying new and improved existing methods, which should be taken into account in the development and operation of secure telecommunications systems and the IoT segment. Fields of interest: -Models, software and hardware solutions for infocommunication devices and systems -Modern component base for infocommunication devices -Sensors, distributed sensor networks, machine-to-machine communications -Features of the physical security of infocommunication devices and systems -Protection of data transfer protocols -Security of data, applications and cloud services, blockchain -Internet of things, artificial intelligence, cyber-physical systems
Yuriy Fedkovych Chernivtsi National University
Mathematical Modeling and Processing of Hydroacoustic Signals in Noise Environments
The article presents the results of research into methods for improving the efficiency of hydroacoustic signal transmission in complex noise environments through the use of wavelet analysis, adaptive filtering, and mathematical modeling. Hydroacoustic systems are a key tool for underwater communication, but their operation is significantly complicated by the effects of multipath propagation, water turbulence, and reflections from the surface and bottom, which lead to distortion and reduced information reliability. The work implements a structural-functional synthesis approach that combines a mathematical description of signal propagation processes, wavelet decomposition for analyzing the time-frequency structure, and adaptive filtering based on the criterion of minimizing the root mean square error. The proposed model of the hydroacoustic channel takes into account the impulse response of the medium and additive white Gaussian noise, which allows for an adequate reproduction of real transmission conditions. To improve the accuracy of information reconstruction, a combined algorithm has been developed: at the first stage, wavelet decomposition of the received signal is performed, then threshold processing of detailed coefficients is carried out according to the Donohue rule, after which Wiener or LMS-type adaptive filtering is applied. Thanks to the time-frequency localization of wavelets, effective isolation of information components is ensured even at low signal-to-noise ratios. The obtained mean square error and signal-to-noise ratio parameters demonstrate a significant improvement in reconstruction quality. The proposed approach can be used in practical hydroacoustic systems to improve their noise immunity, stability, and accuracy of information retrieval in complex underwater conditions. The combination of wavelet transformation and adaptive filtering forms the basis for the creation of intelligent signal processing systems capable of ensuring effective operation in conditions of stochastic noise and uneven propagation of acoustic waves.
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Method SYNERGY of Distributed Optimization and Coordinated Control in Fog/Edge Telecommunication Environments
The article presents the development of the SYNERGY (System-wide Energy and Risk Governance) method, designed for distributed optimization and coordinated control in Fog/Edge telecommunication environments. The purpose of developing SYNERGY is to ensure global stability and energy balance among independent coordinators operating under dynamic conditions with partial failures and stochastic disturbances. The scientific problem addressed involves achieving collective minimization of a global loss function under local resource constraints while preserving the autonomy of each node group. The proposed method is based on an analytical distributed optimization framework that combines the Alternating Direction Method of Multipliers with the Lagrange multiplier mechanism to achieve consensus among coordinators and harmonize decision-making processes in real time. The method’s architecture implements multi-level interactions among coordinators, where local self-organization processes are synchronized through analytical optimization modules, and the upper level formulates control policies based on accumulated statistical data. The SYNERGY algorithm has a cyclic structure that includes stages of parameter exchange, local state updating, convergence verification, and policy correction policy(t). Such a structure ensures continuity of control even under communication channel degradation, maintaining coordination consistency among all coordinators. Experimental modeling conducted in a distributed Fog/Edge environment confirmed the method’s efficiency and robustness. The results demonstrated that the convergence rate of the global loss function increased by 40 – 45%, the residual error decreased by more than 50%, and the average energy consumption dropped by 12 – 15%, reaching up to 20% under highly volatile channel conditions. Packet loss resilience improved by 18 – 22%, while the use of policy update cycles accelerated stabilization of the consensus state by 25 – 30%. The obtained results prove that the SYNERGY method is an effective tool for enhancing resilience, energy balance, and controllability of distributed Fog/Edge telecommunication environments operating under dynamic conditions.
Modeling a Fog Computing Network Architecture for Secure IoT Data Processing
This article presents a fog computing network architecture designed for secure processing of Internet of Things data. The proposed architecture consists of four layers: the cloud layer – a central server for analytics and long-term data storage; the proxy layer – responsible for caching, routing, and load balancing; four fog nodes that provide real-time data processing; and the device layer – representing Internet of Things endpoints. This structure enables flexible load distribution and enhances the system’s resilience to component failures. To verify the effectiveness of the developed architecture, simulations were performed in the iFogSim environment. Scenarios were created with different numbers of smart cameras (from 16 to 48). The modelling results showed that, with 16 cameras, the data processing latency of the proposed architecture was 286 ms, while in the traditional cloud-based architecture was 811 ms. These results demonstrate an overall 64.7% reduction in data processing latency in the developed architecture. The fog computing network architecture also achieved a 17-fold reduction in network resource utilization under minimum load (16 cameras) and a 4.3-fold reduction under maximum load (48 cameras). This translates to up to 90% savings in bandwidth and a significant decrease in the risk of network congestion. The proposed architecture ensures a high level of protection of users’ personal data through the local processing of video streams. Sensitive information is processed on fog nodes without being transmitted to external networks, which minimizes the risk of personal data leakage. The modeled fog computing network architecture provides a solid foundation for further development of fog computing technologies in the Internet of Things domain.
Organization of Structural Means for Generating Sequences of Pseudorandom Equiprobable Binary Sets
The paper presents the results of research and development of structural methods for hardware generation of pseudorandom equiprobable binary sequences. The features and limitations of existing approaches to the synthesis of pseudorandom sequence generators are analyzed, particularly those associated with fixed probability distributions or predetermined set cardinality. A new structural generation method based on cascade dichotomic decomposition of the number of possible combinations in the output sequence is proposed. This approach enables the formation of a complete set of equiprobable binary patterns of a given bit length while reducing hardware complexity. An algorithm for constructing the generator has been developed, which includes the formation of the decomposition tree, the synthesis and minimization of combinational circuits implementing parity functions, and the determination of transition probabilities between the vertices of the decomposition graph. The algorithm is presented in a formalized form, allowing for simplified implementation and analysis. It is shown that the proposed method allows calculating transition probabilities through simple ratios between decomposition components, significantly simplifying the circuit implementation of the generator. The proposed generator structure provides the formation of equiprobable pseudorandom sets regardless of the set’s cardinality. It is proven that the performance of such a generator depends not on the size of set N but on its binary logarithm, i.e., the bit width m of the generated code. The practical implementation of the method is illustrated by an example for, including the calculation of probabilities and construction of parity functions. The obtained results can be applied in the design of high-performance systems for digital device testing, modeling tools for fault-tolerant multiprocessor systems, and random data generators in digital computing technology.
Forecast-Oriented Method of Complex Signal Ensemble Permutations Based on the ARIMA Model
The article proposes a method for time-domain permutation of complex signal ensembles based on the Autoregressive Integrated Moving Average (ARIMA) model, referred to as ARIMA-permutation-based method. Unlike existing approaches, the method takes into account the temporal inertia of correlation variations, enabling real-time forecasting of ensemble dynamics and adaptive structural control under stochastic disturbances. The developed method and its implementation algorithm combine the ARIMA permutation-based method differencing operation with forecast-oriented selection of time-segment permutations, whose optimization is performed according to an integral criterion that considers correlation indicators, energy variation, and signal-to-noise ratio (SNR) stability. This integration ensures a balance between adaptability and convergence stability, allowing the forecast to rely on decorrelated signal increments rather than accumulated trends. Within the mathematical model, objective functions are formulated to describe the expected correlation, forecasting uncertainty, and energy consistency of the ensemble parameters. Minimization of integral criteria of ensemble configuration enables the selection of the optimal time-segment permutation and progressive refinement of the forecast during the iterative process. Experimental modeling was carried out for SNR values from 10 dB to 25 dB and time-segmentation parameters τ = 0.3 – 1.0, comparing three approaches: ARIMA-permutation-based method, the Markov model, and the LPT-τ permutation model. The results demonstrated a 20.2 % reduction in the mean absolute scaled error and a 15.3 % reduction in the mean scaled interval score compared with the Markov method, as well as an increase in residual stability by 47.1 % under signal fading conditions. It has been proven that the application of the ARIMA-permutation-based method effectively suppresses residual correlation, ensures short memory of differenced series, and improves the accuracy and robustness of forecasting under interference. The proposed approach can be applied to the optimization of signal formation and processing in cognitive telecommunication environments, particularly in the design of dynamic spectrum monitoring systems, distributed communication networks, and energy-efficient data transmission protocols.
Method for Multi-Objective Optimization of Complex Signal Ensembles Based on the Evolutionary Algorithm E-LPT-MOEA/D
The article proposes a method of multi-objective optimization of complex signal ensembles based on the evolutionary algorithm E-LPT-MOEA/D, which combines logarithmic time-segment permutations (LPT) with the task decomposition principle in a multi-objective evolutionary optimization framework. Unlike existing approaches, the method introduces adaptive interaction between the working population and the external Pareto archive, ensuring consistent updating of the solution set and convergence stability under stochastic perturbations. A modified genetic algorithm has been developed that incorporates entropy-weighted adjustment of weighting coefficients, flexible task delegation, and dynamic mutation control. This integration maintains a balance between exploration and exploitation, prevents premature convergence, and preserves the diversity of signal ensembles. The mathematical model includes objective functions representing the mean cross-correlation coefficient, integrated side-lobe level, variance of signal energy, and structural uniformity index. The optimization quality was evaluated using hypervolume (IH), inverted generational distance (IGD), and correlation deviation (Δρ) indicators. Experimental simulations were conducted in both normalized and absolute modes for various signal-to-noise ratios (10 – 25 dB) and time-segmentation parameters (τ = 0.3 – 1.0). The obtained results confirm the advantages of the proposed method, including a 20 – 30 % improvement in convergence speed, a 15 – 25 % increase in stability, and a 30 – 40 % reduction in the amplitude of hypervolume difference (ΔH) oscillations between the archive and the population. It has been proven that integrating the external archive mechanism with internal time-domain signal permutation ensures more uniform Pareto front coverage and enhances the structural balance of signal ensembles. As a result, the E-LPT-MOEA/D algorithm provides rapid adaptation to changing optimization conditions, resistance to interference, and scalability with increasing problem dimensionality. The proposed method can be effectively applied to the optimization of signal formation and processing processes in cognitive telecommunication environments, particularly in the development of dynamic spectrum monitoring systems, distributed communication networks, and energy-efficient data transmission protocols.
Information Technology for Assessing and Ensuring Cybersecurity of Large Language Models
The rapid evolution of large language models (LLMs) and their incredible ability to work with natural language is generating interest within an increasing number of human activities. Modern language models are no longer limited to simple text generation. They can perform the following complex operational processes: reasoning and planning, content generation and big data processing, programming, and information retrieval. LLMs bring significant benefits to various industries, including finance, education, and the public sector. However, in addition to the significant advantages of using these models, there are certain security challenges that must be taken into account when developing and using LLMs. These challenges include generating incorrect answers (hallucinations), creating forbidden content, and generating responses that contain confidential data. This study presents a software tool and technology for assessing and ensuring the cybersecurity of LLMs against the generation of forbidden content. The main goal of this tool is to improve the accuracy of security assessment and the level of protection of LLMs against this threat. A set of basic data required for the software tool was identified, which includes exploits, prompts for checking the model’s output, and countermeasures for its protection. A procedure for collecting, converting, storing, and potentially extending and adapting this data to the individual requirements of the tool’s users is proposed. A functional model of the technology was developed, which consists of the following stages: environment setup (verification of configuration options, verification of connection with models); analysis of system vulnerabilities by simulating attacks on it and verification of the results of its work; analysis of threats, effects, and criticality of attacks on the system using the IMECA (Intrusion Modes Effects Criticality Analysis) method of assessing LLMs; choice of countermeasures (CM) to ensure the cybersecurity of the system. A test of the software tool was conducted, confirming its effectiveness in increasing the security of LLMs due to more complete and trustworthy assessing effects of attacks on vulnerabilities and choice of justified CM set. Directions for future research on increasing the flexibility and usability of the software tool and technology as a whole were proposed, specifically, managing its settings and extending and adapting the basic dataset to the individual requirements of users.
Research on Switched-mode Power Supplies Influence on a Data Rate Over Home Electrical Wiring Networks
The rapid development of the Internet of Things, one of the most dynamically expanding technological domains worldwide, is the reason for ongoing search for solutions that combine high bandwidth, flexibility, scalability, and cost efficiency for the implementation of such concepts as Smart Grid, Smart Home, and Smart City. Despite the significant progress of wireless technologies and their widespread deployment in the Internet of Things sector, their application cannot always address all challenges, particularly with regard to ensuring a stable signal level in shadow coverage zones. In such cases, the application of Power Line Communication technology is considered appropriate, as it enables broadband access through home electrical wiring network. Data transmission via high-frequency signals is not considered during the design of home electrical wiring network. Therefore, it is an urgent issue to investigate both the propagation characteristics of the electrical wiring medium and the effects of various loads connected to branches on home electrical wiring network parameters. This article is devoted to researching the influence of switch-mode power supplies of different power rates on the data rate of data transmitting systems Broadband over Power Line Communication operating over home electrical wiring network. Calculations of the maximum achievable data rate for a fragment of home electrical wiring network consisting of a single branch formed by segments of ShVVP, a common domestic wire in Ukraine, with copper conductors of 1.5 mm² cross-sectional area were performed. The influence of the input filter parameters and the noise induced by the alternating magnetic field of the SMPS transformer on the maximum achievable data transmission rate in home electrical wiring network was analyzed. The research was performed within a frequency range of 0 to 30 MHz, which corresponds to the 25 MHz–PB band in accordance with ITU-T Recommendation G.9964.
Overview of Methods and Technologies for Obtaining Knowledge About Financial Proposals
In the contemporary world of digital finance and data-driven decision-making, the ability to effectively obtain, analyze and interpret knowledge about financial propositions (FPs) has become a critical challenge. FPs often contain critical information about investments, risk assessments, forecasts, and strategic intent. However, this data is not always complete and is often presented in semi-structured or unstructured forms, which limits its direct applicability. The motivation for this study is the exponential growth in both the volume and complexity of financial documentation, which requires more advanced and efficient approaches to context identification and knowledge obtaining (KO). By using natural language processing (NLP) techniques, machine learning (ML) algorithms, semantic analysis, and other innovative tools, public-private companies can increase the accuracy and efficiency of financial decision-making, ensure regulatory compliance, and minimize human error. The concept of a financial system is also formalized by defining its main components and describing it as a system with distinct properties and relationships. Such a systematic approach allows for a deeper understanding of the relationships between budgeting, forecasting and risk assessment, which is critical for obtaining the "right" knowledge. Based on the formalization of the FP, a conceptual scheme for KO about the FP is proposed, where the FP is considered as a multimodal information object from which structured knowledge is obtained using various methods. This knowledge is further used for analytical support of decision-making, but has significant limitations that require the involvement of experts and additional models. The article explores modern methods and technologies aimed at automating and improving the process of KO about such FPs, and also offers suggested directions for research in this area. Among them, it is planned to study the latest technologies and advanced approaches to further improve the automation and accuracy of KO about FPs, which will contribute to more informed and final decision-making in the financial sector.
Formation of a Three-dimensional Relief Model Based on a 2D Image
This paper introduces an automated pipeline for generating high-quality 3D bas-relief models directly from single 2D images. Our method replaces laborious manual height‐map editing with ZoeDepth, a state-of-the-art neural network that outputs dense depth maps without camera calibration or manual annotations. Input photographs, whether of architectural facades, artwork reproductions, or industrial scenes, are first contrast-normalized and preprocessed prior to depth estimation. The raw depth output then undergoes metric‐attractor correction, which refines depth-bin centers using multiple predicted attractor points per pixel to improve continuity and reduce quantization artifacts. A custom depth-aware triangulation algorithm subsequently converts the refined depth field into a surface mesh, with user-controlled parameters for real-world scale, spatial resolution, and triangulation density. The final mesh is exported as an STL file, enabling immediate compatibility with CAD software and 3D printers. The performance, robustness and fidelity of the elaborated pipeline were evaluated on a quad-core Intel i5 CPU and a variety of image domains. It turned out that depth inference for an 800 × 800 pixel image took around 120 seconds, while mesh generation and STL export took approximately 110 seconds. These times scale linearly with image resolution. Quantitative assessment yields a mean relative error below 7.7 % and threshold accuracy above 95.3 %, indicating that over 95 % of pixel depth estimates fall within 25 % of true values. A qualitative inspection has confirmed that the obtained reliefs preserve critical geometric details and maintain surface smoothness, even on previously unnoticeable inputs. Comparative analysis highlights significant reductions in manual effort and total modeling time versus traditional Blender-based sculpting workflows, without sacrificing mesh quality.
Investigation of the Influence of Temperature on the Characteristics of Fibre Bragg Gratings
The article presents the results of a fibre Bragg grating (FBG) investigation. Two types of FBGs, uniform and tilted, were fabricated. They were inscribed in the cores of fibres using a UV excimer laser KrF Coherent Bragg Star M and the phase mask method. Photosensitive optical fibres GF1 were chosen for FBG fabrication. The properties and characteristics of the fabricated FBGs were investigated in order to design fibre-optic bending sensors. In this case, the analysis of FBG spectral characteristics and temperature sensitivity dependencies is crucial. The setup including a climatic chamber, an AQ6370D Optical Spectrum Analyser, an S5FC1550S-A2 SM Benchtop SLD Source and a computer was used for FBG spectral characteristics and temperature sensitivity measurement. Spectral characteristics of uniform and tilted FBGs were investigated within the temperature range from -40.6°C to 181.8°C. The following peaks in the spectral characteristics in the above-mentioned temperature range were chosen for analysis: the Bragg peak for uniform FBGs and such peaks as Bragg peak, Ghost peak and three peaks of certain wavelengths for tilted FBGs. The shifts of the analysed peaks in the spectral characteristics with temperature were observed. Temperature dependencies of wavelengths of the analysed peaks were obtained. It was shown that there is an approximately linear temperature dependence of the Bragg wavelength and other above-mentioned analysed peaks of certain wavelengths. The linear character of the investigated temperature dependencies of the fabricated FBGs allows for their use in sensor application. It was shown that temperature sensitivities of uniform and tilted FBGs were dependent on the temperature range chosen. Temperature sensitivities approximately equal to 11.1 – 11.4 pm / °C in a temperature range of 120.8 – 181.8 °C and approximately equal to 8.5 – 8.9 pm / °C in a temperature range of -40.6 – 20.7 °C for uniform and tilted FBGs were determined. Temperature sensitivities were dependent on certain wavelength and changed from 8.4 pm / °C to 10.1 pm / °C for particular wavelengths.
Face Detection and Identification Using Convolutional Neural Network and MobileNetV3 Model
This paper presents the results of a study of the effectiveness of applying the transfer learning methodology to the task of face detection and recognition, with a focus on processing images containing only one face. MobileNetV3 was chosen as the basic neural network architecture, which provides high performance with limited computing resources. The model was trained in two consecutive stages: the first is face recognition (detection) in photos, and the second is face identification from a face image. To ensure the unambiguity and purity of the training data, only images with one face were used. The training process combined the use of an open dataset for the initial stage of face detection with the proprietary photo set of students of Yuriy Fedkovych Chernivtsi National University for the identification phase. The model was trained and tested in the Google Colab cloud environment using an Nvidia Tesla T4 GPU. The neural network was implemented using the modern deep learning framework TensorFlow and our own program code written in Python. The model parameters were optimized by minimizing the loss function, which is the sum of the binary cross-entropy and the negative logarithm of the Intersection over Union metric, which characterizes the accuracy of determining the location of an object in an image. The built model was compared with previous approaches to face detection implemented on the basis of the OpenCV library. A comparative analysis by the metrics of recognition accuracy and processing time demonstrated the superiority of the developed system. The results obtained are of interest to researchers in the field of computer vision, automated recognition systems, and technologies for intelligent visual data processing.
FPGA Platforms and Their Use in Edge Computing
The article examines the role and future prospects of programmable logic devices (FPGAs) and system-on-chip FPGA (SoC FPGA) platforms within the edge computing paradigm. Particular attention is given to the combination of adaptability, fine-grained power consumption control, and high degrees of parallelism, which are critical characteristics for modern edge platforms. Additionally, the current state of FPGA adoption in practical edge scenarios is analyzed, ranging from video analytics in transportation systems to industrial vibration diagnostics and acceleration of telecommunications functions. Examples of both conventional FPGA-based solutions and hybrid SoC FPGA architectures are discussed, where programmable logic is tightly integrated with ARM-based processors to achieve balanced workload distribution between software and hardware components. It is demonstrated that such systems can significantly reduce processing latency, optimize energy consumption, and enable autonomous operation even under remote or unstable network conditions. The role of embedded operating systems is also examined, particularly in the context of SoC FPGA platforms, where Linux-based environments enable separation between control logic and hardware-accelerated data paths. The paper discusses how embedded operating systems influence system flexibility, software reuse, update mechanisms, and long-term maintainability of FPGA-based edge devices. In addition, the work addresses technical and organizational barriers that limit large-scale deployment of FPGA-based edge systems, including the lack of unified high-level synthesis toolchains, the steep learning curve associated with HDL-based design, and limited support for infrastructure-oriented workflows in FPGA-centric environments. Approaches for dynamic logic reconfiguration aimed at improving adaptability of local IoT systems are considered, along with challenges related to logic testing, system configuration, and scalability when adapting platforms to new application requirements. Modern development tools and frameworks for flexible system design, including cloud-based services and high-level programming environments such as Vitis HLS and Intel oneAPI, are also discussed in the context of reducing development complexity and accelerating design iterations.
Precision Photodiode Current Amplifier With Output Signal Scaling Capability
A precision multi-range photodiode current amplifier with programmable gain scaling and compensation for both offset voltage and dark current is proposed. The relevance of the work lies in the need for accurate measurement of weak optical signals in photometric, spectroscopic, and other optoelectronic systems, where conventional fixed-gain amplifier circuits fail to provide a sufficient dynamic range. The introduction outlines the main limitations of traditional transimpedance amplifiers, particularly their sensitivity to temperature drift and limited adaptability to varying input signals. A literature review is conducted, covering modern solutions aimed at improving the accuracy of photodiode amplifier designs, including commercial systems with zero-adjustment and temperature compensation. The methodology section describes the structural and circuit design of the developed device, which consists of four functional blocks: an input transimpedance stage based on a low-bias-current operational amplifier; a programmable gain module implemented using switchable resistors that provide discrete gain scaling by orders of magnitude; an offset compensation circuit with a stable bias voltage generator; and an output buffer that ensures proper interfacing with a high-resolution analog-to-digital converter. Special attention is given to the implementation of thermally stable dark current compensation by introducing a negative voltage bias, which significantly reduces the drift of the zero-output level. Experimental results demonstrate a high level of precision and stability of the amplifier under varying photodiode current levels. The amplifier’s noise characteristics, linearity, and temperature dependence are evaluated. The functionality of the proposed solution is compared to existing commercial alternatives, highlighting advantages in versatility, scalability, and accuracy. Prospects for further development are discussed, including the implementation of automatic gain range control and the reduction of parasitic currents in electronic switches.
One of the promising areas in the development of artificial intelligence is the creation of computer vision technology – a system that enables computerized systems to acquire, analyze, and interpret information from photos, videos, or digital images. This opens up extensive opportunities for process automation across various fields, including robotics, autonomous transportation, industry, and medicine. One of the key challenges in computer vision research is the problem of visual position recognition – assessing a robot’s coordinates and orientation based on video or photo data obtained from its cameras. In robotic systems, precise position recognition is critical for navigation, adaptation to environmental changes, and interaction with objects. The article attempts to formulate the problem in terms of approximating the probability density function of the robot’s states within the space of input data. In addition to theoretical aspects, the study examines a set of algorithms currently in use – both classical approaches and neural network-based models – their universality, and their integration potential with other computer vision technologies. The interpretation of these algorithms is presented from the perspective of dimensionality reduction in the input data space during localization. Furthermore, a list of relevant datasets for training and testing visual position recognition models is provided, along with key metrics for evaluating their performance. Thus, the study not only summarizes modern approaches to solving this problem but also outlines directions for further technological advancements that can ensure more efficient and accurate robot localization in space.
Control of TDS Parameter Using IoT Technologies as a Factor in Improving Equipment Reliability and Cost-Efficiency
This article presents a comprehensive Internet of Things (IoT) based solution for monitoring the efficiency of filtration systems based on the analysis of Total Dissolved Solids (TDS). The proposed system integrates a measuring device, a cloud-based IoT platform, and algorithms for filter condition assessment. The device performs TDS measurements before and after the filtration unit using a modified Gravity Analog TDS sensor, which operates by measuring the electrical conductivity of water using alternating current. Multiple measurements collected throughout the day are averaged and transmitted to the ThingsBoard platform, where the processing logic is implemented, including residual filter life estimation, efficiency evaluation, and automatic alert generation. The system architecture enables not only real-time water quality monitoring but also adaptive response to changes in operating conditions, improving the accuracy of filter degradation forecasting. This approach differs from traditional maintenance models that rely on fixed time intervals or consumption volumes, offering reduced operational costs, prevention of premature failures, and enhanced equipment reliability. A key feature of the system is the transition from local to centralized cloud-based data processing, which simplifies the scalability of the solution for large-scale, distributed infrastructures. The proposed design is particularly relevant for commercial equipment and industrial water treatment systems, where stable water quality and timely replacement of filtration components are critical.
Review of Nuclear Quadrupole Resonance Signal Processing and Analysis Methods
This article presents a structured and comprehensive review of signal processing and analysis methods used in nuclear quadrupole resonance (NQR), with a focus on their performance under real-world operating conditions, where signals are typically weak, noisy, and highly sensitive to environmental influences. Classical approaches, including Fourier analysis, wavelet transforms, adaptive and matched filtering, as well as high-resolution spectral estimation techniques, are considered with respect to their ability to enhance signal detectability and frequency resolution. Particular attention is devoted to modern machine learning-based methods, including deep neural networks and hybrid architectures, which enable automatic feature extraction and demonstrate high robustness in low signal-to-noise ratio conditions. Each group of methods is systematically analyzed according to a set of practical criteria, including noise immunity, computational complexity, adaptability to nonstationary environments, sensitivity to parameter variations, and suitability for implementation in portable or resource-constrained NQR systems. The review highlights key challenges in current research, such as the lack of standardized and representative training datasets, limited interpretability of learning-based models, and difficulties associated with deploying computationally intensive algorithms in compact instrumentation. It is shown that no single signal processing approach provides a universally optimal solution across all operating scenarios. Instead, the most promising direction lies in the development of hybrid signal processing frameworks that combine classical preprocessing techniques with data-driven machine learning models. The presented analysis provides a methodological basis for the informed selection and design of robust, adaptive NQR systems intended for practical applications in security screening, material characterization, and chemical substance identification under diverse and challenging conditions.
An Adaptive Benchmark Testing Method for Evaluating Arithmetic Precision
Floating-point arithmetic is inherently prone to precision errors, which can accumulate over time and significantly influence the outcomes of numerical computations. This work presents a method designed to systematically assess and compare the accuracy of various arithmetic implementations by adaptively refining test inputs in response to observed computational inaccuracies. In contrast to conventional approaches that use either fixed sets of numerical values or random sampling techniques, the method introduced here continuously updates the test set. It does so by identifying areas in the numerical domain where computational errors tend to be the most significant. The refinement process is iterative and guided by statistical analysis of previous results, ensuring that regions with elevated error levels receive more focused attention in subsequent testing phases. At the heart of the method is an adaptive process for determining which numerical values require further examination. This is achieved by analyzing the distribution of previously recorded errors and updating a decision criterion based on those findings. Specifically, thresholds for acceptable accuracy are recalculated using statistical measures such as quantiles, which reflect the severity and frequency of encountered errors. This ensures that the refinement of test inputs is driven by actual data, rather than relying on predetermined heuristics. The method begins with the generation of a diverse collection of numerical inputs that spans a broad spectrum of floating-point values, including those known to cause instability in calculations – such as extremely small or large values and those located at the boundaries of numerical precision. These inputs are then used to perform arithmetic operations including addition, subtraction, multiplication, and division. Two different arithmetic implementations are evaluated: the standard arithmetic used in a widely adopted programming language and an alternative, custom-developed arithmetic designed to enhance numerical accuracy. For each operation, the resulting values produced by the two arithmetic systems are compared. Measures of accuracy are derived by calculating the differences between the outputs using both absolute and relative error estimations. These differences are then statistically analyzed to detect patterns in the occurrence and magnitude of errors. Based on this analysis, if the error associated with a particular input is determined to be higher than expected, additional test values are generated in the vicinity of that input. This is accomplished through carefully controlled variations, allowing the method to explore neighboring regions where similar errors might occur. In this way, the test suite evolves over time, becoming increasingly focused on those numerical situations that are most likely to expose weaknesses in arithmetic implementations. By uncovering patterns in how errors emerge and accumulate, the method provides a structured and repeatable process for evaluating the reliability of floating-point arithmetic under varying conditions. Its targeted nature makes it especially useful for scientific and engineering applications, where computational precision is critical. In summary, this approach improves upon traditional benchmarking techniques by introducing an adaptive, data-driven strategy that emphasizes the most challenging areas of numerical computation. As such, it offers a powerful tool for the verification and validation of arithmetic systems, supporting both development and quality assurance in software that relies heavily on floating-point calculations.
Preprocessing of Object Images Before Their Detection Using YOLO Neural Network
Software was developed in Python for the digital processing of object images before their detection using the You Only Look Once (YOLO) artificial neural network. Detection was carried out on object images, specifically vehicles and other road users. The preprocessing of images was performed through histogram equalization and local contrast enhancement. The original images were read from video cameras or graphic files. Image detection was performed using the medium-sized YOLO convolutional neural network model, YOLOv8m. As a result of detection, the studied objects, particularly cars, were highlighted with rectangular bounding boxes. To evaluate the accuracy of object detection, several metrics (parameters) were used: Precision, Recall, Intersection over Union (IoU), F1, and Average Precision (AP). The YOLO network was pre-trained on the Common Objects in Context dataset. The YOLO neural network returns the object recognition confidence, the coordinates of its center (x, y) on the image, and its width and height (w, h). The detected objects are marked with rectangular bounding boxes. The positioning of objects in the images was manually performed using the Roboflow tool, which allows for accurate spatial positioning of each object. The study of the impact of image preprocessing on the accuracy of object detection by the YOLO network was conducted using a typical traffic scene image. After histogram equalization, the histogram becomes more symmetrical and uniform, improving the visual quality of both dark and bright areas. This led to improved object detection accuracy across almost all metrics. The increase in detection accuracy by the IoU parameter is 0.169. The summary improvement in detection accuracy by the IoU, F1, AP parameters is 0.502. It was shown that after image preprocessing, detection accuracy increased, and all vehicles were correctly detected. The developed software can be used in various computer vision systems as well as in Internet of Things systems for remote monitoring and control.
Method of Granular Analysis of Shared Resource Utilization in a Kubernetes Cluster
In modern computing environments, the Kubernetes platform has become the standard for automated deployment, scaling, and management of containerized applications. With the growing popularity of Kubernetes worldwide, there is an increasing need for accurate and efficient analysis of resource consumption. This is critically important to ensure stable performance, optimal infrastructure utilization, and economically justified capacity planning. However, resource management in Kubernetes is complicated by the dynamic nature of workloads and the interdependence of applications operating within a shared environment. The discovery establishes initial conditions for the analysis, considering the main types of resources: processor, random-access memory, disk space, and network traffic. For each resource, weighting coefficients are defined that reflect its relative importance in the context of different task executions. Additionally, each resource is divided into four states: allocated, reserved, utilized, and free. Such a division allows for a more detailed picture of the actual state of infrastructure usage and supports decision-making based on both technical and economic efficiency. The primary model focuses on evaluating the resource consumption of a single application at a specific point in time. Within this model, the relationship between reserved and actually utilized resources is examined, enabling the identification of excessive reservation or, conversely, insufficient provisioning. The model forms the foundation for a basic understanding of the application's behavior in the cluster and allows for initial diagnostics of inefficiencies. Further model refinement incorporates changes in resource usage over time. The behavior of a single application is analyzed throughout a specified period, which opens the possibility to identify long-term trends, gradual resource leakage, peak loads, or uneven consumption. This approach significantly improves the accuracy of assessing application performance and supports informed decisions regarding scaling or setting constraints. The final level of the model provides for analyzing resource usage by multiple applications simultaneously over a given period. This allows consideration of the mutual influence between applications, competition for shared resources, and overall environment load. As a result, a comprehensive picture of resource balance is formed, which serves as the basis for intelligent cluster policy planning, workload placement optimization, and service stability assurance. The proposed approach to granular analysis of resource utilization in Kubernetes clusters is a promising research direction. It opens wide opportunities for further development of forecasting models, automated resource management, and the construction of adaptive monitoring systems capable of independently responding to changing cluster loads. The conclusions of the study emphasize the importance of integrating such methods to enhance the efficiency of modern cloud infrastructure operations.
Telecommunication System for Transmission of Adaptively Scaled Digital Images
The hardware and software of a telecommunication system designed for the transmission of adaptively scaled digital images has been developed. The system consists of transmission and reception subsystems. The hardware of the transmission subsystem includes a USB video camera, a microcomputer Raspberry Pi3 and a Raspberry Pi3 radio module. The hardware of the reception subsystem includes a radio module nRF24L01 # 2, a microcontroller Funduino Uno and a computer. The radio module Funduino Uno has a relatively low transmission speed, but provides low power consumption. The system software has been developed in Python and C++. In the transmission subsystem, the image from the video camera is read using the program cam2nrf, its scale is reduced by interpolation and the reduced image is transmitted via radio module # 1. In the reception subsystem, the reduced image is read via radio module # 2, its scale is increased by interpolation. Image scaling is performed using bilinear or bicubic interpolation. The program nrf_rx, which runs on the Funduino Uno microcontroller, is used to receive wireless data using the radio module nRF24L01. The program recv_img, which runs on the computer, reads the image from the microcontroller Funduino Uno, scales, visualizes and saves it. Scaling adaptability is ensured by choosing the image interpolation algorithm depending on its average spatial period TCR, which is calculated based on the Fourier power spectrum of the original image. For values TCR ​​< 4.5 pixels, bilinear interpolation is performed, and otherwise bicubic interpolation is performed. Testing of the telecommunication system for transmitting real images shows its operability. By reducing the image size by a factor of 2, the transmission time is reduced by a factor of 4 with a slight decrease in the visual quality of the resulting image. Such image scaling is especially effective when transmitting images over low-bandwidth channels. The developed hardware and software can be used in `Internet of Things systems for image transmission.
AI-enabled Cloud SDN Controllers: Architecture, Scalability, and Security – A Comparative Study
This article investigates the applicability and advantages of deploying a software-defined networking (SDN) controller within a cloud infrastructure. The study begins by analyzing the architectural differences between on-premises SDN controllers and cloud-based implementations, including monolithic, microservice, and hybrid edge-cloud approaches. The motivation is driven by the need to address modern network challenges such as dynamic scalability, efficient control over geographically distributed systems, and the integration of advanced automation tools. Cloud-based SDN controllers offer more convenient maintenance and seamless integration with external services, including telemetry, monitoring, and DevOps pipelines, than their locally deployed counterparts. However, they also introduce latency concerns and potential risks related to data confidentiality, which must be mitigated through encryption and secure communication protocols. The article emphasizes the integration of artificial Intelligence (AI) into the control plane. AI-powered modules enable the prediction of traffic patterns, detection of network anomalies, dynamic adjustment of routing policies, and overall improvement in quality of service. Examples from recent research, including implementations such as TeraFlowSDN, demonstrate the viability of embedding machine learning components within cloud-hosted controllers to enhance their decision-making capabilities. Comparative evaluation demonstrates that cloud deployment is preferable in contexts such as IoT-oriented systems or rapidly evolving network infrastructures. Properly designed, cloud-based SDN controllers can deliver performance levels comparable to traditional systems while offering greater flexibility for future development and integration with intelligent network services.
Perspectives on the Development and Use of Nuclear Methods for Detecting Landmines and Minefields: A Review
Landmines remain a deadly legacy of past and present conflicts, with these hidden explosive devices causing thousands of casualties each year. In addition to the existing mainstream methods of detecting landmines and minefields, several new technologies are being investigated, such as nuclear mine detection methods, namely nuclear quadrupole resonance (NQR) and neutron detection. Both NQR and neutron techniques are quite promising and offer powerful advantages in detecting landmines and minefields, although they have certain draw-backs in practical use. Artificial intelligence can significantly mitigate these shortcomings through advanced signal processing, adaptive algorithms, etc. The purpose of the article is to analyze, research and systematize available information on the positive effectiveness and feasibility of using nuclear methods (NQR and neutron-based) to detect mines and minefields, as well as to improve the accuracy and effectiveness of using these methods using artificial intelligence.
The paper considers the main methods of hydroacoustic signal filtering used to extract useful information from natural and anthropogenic noise. The reliability and accuracy of sonar systems depend on the ability to suppress interference while preserving the useful components of the received acoustic signals. Particular attention is paid to wavelet smoothing, the Wiener filter, adaptive filtering algorithms based on the Least Mean Squares (LMS) method, and a variety of frequency-selective filters, including bandpass, low-pass, high-pass, and notch filters. The effectiveness of each method is discussed in the context of typical underwater acoustic environments, where noise sources vary in origin and spectral characteristics. As part of the study, a real hydroacoustic signal recorded using a broadband hydrophone in natural aquatic conditions was used to evaluate and compare the filtering techniques. The signal contained both low-frequency and high-frequency interference components, as well as impulsive noise typical of biological and anthropogenic sources. MATLAB R2024a software was used to simulate and visualize the filtering process, including wavelet decomposition and thresholding. Based on the results obtained, a combined approach to filtering is proposed, which integrates several complementary methods to enhance signal clarity. This hybrid strategy enables more accurate detection and identification of underwater objects by adapting to specific noise scenarios. The simulation results confirm that a multi-stage filtering scheme significantly improves the signal-to-noise ratio and preserves informative features of the hydroacoustic signal. The proposed approach is applicable to sonar systems used for marine research, underwater navigation, and environmental monitoring.
Features of the Application of Compound Codes in Telecommunications
In telecommunications systems and networks designed for information transmission, independent and packet errors may occur simultaneously. To correct this type of error, it is advisable to use a code that can correct both packet and independent errors. Compound codes are one type of cyclic code. They are a type of systematic code. They are uniform, divisible, and block-based. They have an exceptional property: if a code combination belongs to a code, then a new combination obtained by cyclic permutation of bits also belongs to that code. These codes have a simple hardware implementation of encoding/decoding schemes and high efficiency in detecting and correcting errors. This, in turn, has ensured their widespread use. Irreducible polynomials are used to construct cyclic codes. To obtain a compound code, it is necessary to multiply the generating polynomial of the Fire code by the generating polynomial of the Bose-Chaudhuri-Hocquenghem (BCH) code. In reference literature, compound codes are usually denoted as FxBCH. A comparison of codes that correct only error packets and compound codes showed that compound codes have a greater number of check digits. Most of the obtained optimal compound codes can be used in practice in coding systems. The article studies compound codes that combine several coding methods to improve the reliability of data transmission in telecommunications systems. Their structure, advantages, and disadvantages compared to traditional codes, such as Hamming and Reed-Solomon codes, are considered. The effectiveness of compound codes in noisy channels is analyzed, including numerical calculations of error probabilities and performance comparisons. The results are presented in tables and graphs showing the dependence of effectiveness on channel parameters. The work aims to determine the optimal conditions for the use of compound codes in modern telecommunications systems, particularly in the context of 5G and promising 6G technologies.
Investigation of Statistical Characteristics of Random Number Sequences Generated by a Webcam Using Cellular Automata Functionality and NIST Patterns
At the current stage of development, software-based random number generation is increasingly vulnerable to attacks due to the growing computational power of modern systems. Hardware-based generation relies on reliable stochastic physical phenomena, yet often demonstrates low throughput or an insufficient level of statistical quality. In this study, linear cellular automata are utilized as a post-processing mechanism aimed at improving key statistical properties of the random number sequences. The study presents the results of improving the statistical properties of number sequences obtained from a standard webcam with regard to one of the key cryptographic requirements – uniform distribution of values. Prior analyses revealed that inherent stochastic behavior within webcam sensors produces irregular, non-uniform distributions in the output sequences. This limitation can be mitigated by post-processing using linear cellular automata, specifically employing rules 30, 90, 105, and 150. To evaluate the effectiveness of this method, a comparative analysis was conducted against software-generated sequences produced via the Java SecureRandom class. It is shown that by applying a sufficient number of iterations, it is possible to achieve the required level of uniformity in the distribution of sequence elements. However, the generated sequences did not fully pass the NIST test suite. Statistical uniformity was assessed efficiently using lightweight Java-based libraries, enabling rapid integration even on low-performance hardware such as smartphones. The research findings can be applied in the design of a hardware-based random number generator.
Interference-based Research of the Goos-Hänchen Effect
The paper provides an overview of the Goos-Hänchen effect, which demonstrate that upon total internal reflection of a polarized beam, longitudinal shifts of the beam occur, differing for various polarizations. These shifts must be taken into account when a beam propagates through an optical fiber, as in a fiber-optic communication system, total internal reflection of the beam occurs at the core-cladding interface of the fiber. The paper presents a novel method for measuring longitudinal Goos-Hänchen shifts, which is based on three-beam interference. Using an experimental setup that implements this method, it was possible to measure the phase shifts of orthogonal beams separately. It was established that during the total internal reflection of coaxial, orthogonally linearly polarized beams, there is no change in the phase difference between these beams, even in the presence of the Goos-Hänchen effect. This result suggests the feasibility of the simultaneous use of orthogonally linearly polarized beams in fiber-optic communication lines and provides a positive prognosis for such applications.
Thermal Losses of an Anisotropic Electrically Conductive Transformer
This paper presents the design and comprehensive analysis of an anisotropic electrically conductive transformer developed as an innovative component for modern infocommunication systems. The transformer is based on the use of materials with anisotropic electrical conductivity, opening new possibilities for improving device efficiency and integration. A detailed study of thermal losses is carried out under three main operating modes: no-load operation, short-circuit conditions, and optimal load performance, using numerical calculation methods without simplifications or assumptions. Special attention is paid to the influence of electrical conductivity anisotropy and the orientation of crystallographic axes on the overall efficiency and thermal behavior of the transformer. Unlike conventional transformers with magnetic cores, the proposed design eliminates hysteresis losses, significantly enhancing energy efficiency and minimizing unwanted heat generation. Equivalent electrical circuit models are developed for each operating mode, and analytical expressions for resistance and current are derived, allowing for precise evaluation of performance characteristics. Thermal losses due to Joule heating are quantitatively analyzed for different types of materials. Cadmium antimonide (CdSb), zinc antimonide (ZnSb), single-crystal bismuth (Bi), and single-crystal tellurium (Te) are proposed as promising materials for anisotropic electrically conductive transformers. Numerical modeling results demonstrate a significant potential for minimizing energy losses and optimizing transformer design. The presented results confirm the suitability of the proposed transformer for use in microelectronic and telecommunication applications, where compactness, high efficiency, and thermal stability are critical. The relevance of this study is driven by the growing global demand for compact and energy-efficient components in infocommunication systems. The obtained results provide a foundation for further development of anisotropic electrically conductive transformers as effective matching elements and functional modules within modern telecommunication equipment.
Information Technology for Data Compression and Transformation by Means of Amazon EMR
As data processing volumes grow in various fields, the demand for applications capable of efficiently managing, processing, and transforming large amounts of information is also increasing. Modern approaches to storing and processing large amounts of data are primarily based on universal text formats, such as CSV and JSON. Their prevalence can be explained by their compatibility with a wide range of software tools and ease of integration. These formats are inefficient when dealing with massive volumes of data, particularly when scaling systems or executing analytical queries. The lack of built-in compression, row structure, and metadata leads to significant time and computing resources, which creates a conflict between the requirements for speed and cost-effectiveness of processing and the technical capabilities of traditional text formats. Columnar storage formats, such as Parquet and ORC, offer an alternative. They employ a compact structure tailored for quick analytical queries in distributed computing settings. Effective coding, indexing, and built-in compression techniques considerably lower data sizes and speed up processing. This research aims to develop and experimentally verify the technology of automated data conversion from inefficient text formats to Parquet and ORC formats using Apache Airflow and Amazon EMR. The proposed architecture involves creating a cloud pipeline that performs data conversion and subsequent storage in formats focused on analytical workloads. The system uses Apache Airflow for process orchestration, Amazon EMR and Apache Spark for distributed processing, AWS S3 as scalable storage, AWS Glue for metadata management, and Amazon Athena for SQL access to transformed data. This approach solves performance problems by offering a flexible, reliable, cost-effective solution that adapts to different work scenarios and workloads.
A Methodological Approach to Auditing Software Engineering Practices in the Energy Sector
The growing complexity of software systems in the energy sector, particularly those involved in the management of distributed and renewable energy resources, requires the introduction of structured and domain-specific auditing methodologies. Ensuring the reliability, safety, and security of software products in this context is critical due to the increasing dependence of industrial and energy infrastructures on automated and software-driven solutions. This paper proposes a comprehensive methodological approach to auditing software engineering practices tailored to the needs of the energy sector. The developed methodology is based on an integrated audit model that defines Process, Product, and Safety and Security layers to enable a holistic and systematic evaluation. Furthermore, it incorporates a structured audit process aligned with quality management principles, covering all essential stages from planning to follow-up. A key feature of the approach is the mathematical formalization of audit activities, which includes models for estimating effort, measuring audit coverage, analysing nonconformities, and evaluating process maturity. These models enhance the objectivity and analytical rigor of audits, enabling organizations to quantify and compare results across projects and audit cycles. The proposed methodology was developed based on a thorough analysis of international standards, including ISO/IEC 12207, ISO/IEC 25010, IEC 61508, IEC 62443, and ISO 9001, and aims to bridge the gap between general software engineering requirements and domain-specific needs related to functional safety, cybersecurity, and operational reliability. The results of this research contribute to the advancement of audit methods in the field of software engineering and provide a scientifically substantiated and practically oriented tool for improving the quality, security, and compliance of software systems used in the energy sector.