Security of Infocommunication Systems and Internet of Things
type: channel
description: 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
publisher: Yuriy Fedkovych Chernivtsi National University
language: en
publication name: Security of Infocommunication Systems and Internet of Things
issn: 2786-8443
items: Mathematical Modeling and Processing of Hydroacoustic Signals in Noise Environments
items: Method SYNERGY of Distributed Optimization and Coordinated Control in Fog/Edge Telecommunication Environments
items: Modeling a Fog Computing Network Architecture for Secure IoT Data Processing
items: Organization of Structural Means for Generating Sequences of Pseudorandom Equiprobable Binary Sets
items: Forecast-Oriented Method of Complex Signal Ensemble Permutations Based on the ARIMA Model
items: Method for Multi-Objective Optimization of Complex Signal Ensembles Based on the Evolutionary Algorithm E-LPT-MOEA/D
items: Information Technology for Assessing and Ensuring Cybersecurity of Large Language Models
items: Research on Switched-mode Power Supplies Influence on a Data Rate Over Home Electrical Wiring Networks
items: Overview of Methods and Technologies for Obtaining Knowledge About Financial Proposals
items: Formation of a Three-dimensional Relief Model Based on a 2D Image
items: Investigation of the Influence of Temperature on the Characteristics of Fibre Bragg Gratings
items: Face Detection and Identification Using Convolutional Neural Network and MobileNetV3 Model
items: FPGA Platforms and Their Use in Edge Computing
items: Precision Photodiode Current Amplifier With Output Signal Scaling Capability
items: Overview of Visual Position Recognition
items: Control of TDS Parameter Using IoT Technologies as a Factor in Improving Equipment Reliability and Cost-Efficiency
items: Review of Nuclear Quadrupole Resonance Signal Processing and Analysis Methods
items: An Adaptive Benchmark Testing Method for Evaluating Arithmetic Precision
items: Preprocessing of Object Images Before Their Detection Using YOLO Neural Network
items: Method of Granular Analysis of Shared Resource Utilization in a Kubernetes Cluster
items: Telecommunication System for Transmission of Adaptively Scaled Digital Images
items: AI-enabled Cloud SDN Controllers: Architecture, Scalability, and Security – A Comparative Study
items: Perspectives on the Development and Use of Nuclear Methods for Detecting Landmines and Minefields: A Review
items: Filtration Methods in Sonar Systems
items: Features of the Application of Compound Codes in Telecommunications
items: Investigation of Statistical Characteristics of Random Number Sequences Generated by a Webcam Using Cellular Automata Functionality and NIST Patterns
items: Interference-based Research of the Goos-Hänchen Effect
items: Thermal Losses of an Anisotropic Electrically Conductive Transformer
items: Information Technology for Data Compression and Transformation by Means of Amazon EMR
items: A Methodological Approach to Auditing Software Engineering Practices in the Energy Sector

Mathematical Modeling and Processing of Hydroacoustic Signals in Noise Environments
type: item
description: 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 operatio…
subject: Articles
  topic: section
subject: spectrum analysis
  topic: keywords
subject: signal processing
  topic: keywords
subject: acoustic signals
  topic: keywords
subject: pulse response
  topic: keywords
subject: scale parameter
  topic: keywords
creator: Yevhen Parkhomenko
creator: Halyna Lastivka
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02019
ending page: 02019
doi: 10.31861/sisiot2025.2.02019

Method SYNERGY of Distributed Optimization and Coordinated Control in Fog/Edge Telecommunication Environments
type: item
description: 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 obtain…
subject: Articles
  topic: section
subject: distributed telecommunication systems
  topic: keywords
subject: Fog/Edge
  topic: keywords
subject: optimization
  topic: keywords
subject: main nodes/coordinators
  topic: keywords
subject: data transmission
  topic: keywords
creator: Pavlo Biеliaіev
creator: Volodymyr Pastushenko
creator: Karyna Trubchaninova
creator: Oleksii Komar
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02018
ending page: 02018
doi: 10.31861/sisiot2025.2.02018

Modeling a Fog Computing Network Architecture for Secure IoT Data Processing
type: item
description: 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.
subject: Articles
  topic: section
subject: fog computing
  topic: keywords
subject: IoT
  topic: keywords
subject: security
  topic: keywords
subject: network
  topic: keywords
subject: architecture
  topic: keywords
creator: Yaroslav Malyuta
creator: Maryna Derkach
creator: Taras Lobur
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02017
ending page: 02017
doi: 10.31861/sisiot2025.2.02017

Organization of Structural Means for Generating Sequences of Pseudorandom Equiprobable Binary Sets
type: item
description: 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.
subject: Articles
  topic: section
subject: GL-models
  topic: keywords
subject: fault-tolerant multiprocessor systems
  topic: keywords
subject: generators
  topic: keywords
creator: Ihor Yermolenko
creator: Anton Zhurba
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02016
ending page: 02016
doi: 10.31861/sisiot2025.2.02016

Forecast-Oriented Method of Complex Signal Ensemble Permutations Based on the ARIMA Model
type: item
description: 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 ac…
subject: Articles
  topic: section
subject: telecommunication systems
  topic: keywords
subject: optimization
  topic: keywords
subject: complex signal ensembles
  topic: keywords
subject: SNR
  topic: keywords
subject: correlation
  topic: keywords
creator: Oleksii Shevchenko
creator: Volodymyr Lysechko
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02015
ending page: 02015
doi: 10.31861/sisiot2025.2.02015

Method for Multi-Objective Optimization of Complex Signal Ensembles Based on the Evolutionary Algorithm E-LPT-MOEA/D
type: item
description: 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 in…
subject: Articles
  topic: section
subject: telecommunication systems
  topic: keywords
subject: optimization
  topic: keywords
subject: evolutionary approach
  topic: keywords
subject: complex signal ensembles
  topic: keywords
subject: SNR
  topic: keywords
creator: Ihor Tulenko
creator: Sergii Indyk
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02014
ending page: 02014
doi: 10.31861/sisiot2025.2.02014

Information Technology for Assessing and Ensuring Cybersecurity of Large Language Models
type: item
description: 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 cybersecurit…
subject: Articles
  topic: section
subject: information technology
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: IMECA
  topic: keywords
subject: countermeasures
  topic: keywords
creator: Oleksii Neretin
creator: Vyacheslav Kharchenko
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02020
ending page: 02020
doi: 10.31861/sisiot2025.2.02020

Research on Switched-mode Power Supplies Influence on a Data Rate Over Home Electrical Wiring Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: power line communication
  topic: keywords
subject: broadband access
  topic: keywords
subject: switched-mode power supply
  topic: keywords
subject: data rate
  topic: keywords
creator: Oleksandr Yanevych
creator: Kseniia Tryfonova
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02013
ending page: 02013
doi: 10.31861/sisiot2025.2.02013

Overview of Methods and Technologies for Obtaining Knowledge About Financial Proposals
type: item
description: 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 …
subject: Articles
  topic: section
subject: financial proposals
  topic: keywords
subject: knowledge obtaining
  topic: keywords
subject: natural language processing
  topic: keywords
subject: machine learning
  topic: keywords
subject: semantic analysis
  topic: keywords
creator: Maksym Azarov
creator: Vasyl Lyashkevych
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02012
ending page: 02012
doi: 10.31861/sisiot2025.2.02012

Formation of a Three-dimensional Relief Model Based on a 2D Image
type: item
description: 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.
subject: Articles
  topic: section
subject: bas-relief
  topic: keywords
subject: 3D modeling
  topic: keywords
subject: depth estimation
  topic: keywords
subject: neural networks
  topic: keywords
subject: STL
  topic: keywords
creator: Vitalii Ariichuk
creator: Yuliya Tanasyuk
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02011
ending page: 02011
doi: 10.31861/sisiot2025.2.02011

Investigation of the Influence of Temperature on the Characteristics of Fibre Bragg Gratings
type: item
description: 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 sensitivi…
subject: Articles
  topic: section
subject: fibre Bragg grating (FBG)
  topic: keywords
subject: uniform FBG
  topic: keywords
subject: tilted FBG
  topic: keywords
subject: spectral characteristics
  topic: keywords
subject: temperature sensitivity
  topic: keywords
creator: Les Hotra
creator: Jacek Klimek
creator: Ihor Helzhynskyy
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02010
ending page: 02010
doi: 10.31861/sisiot2025.2.02010

Face Detection and Identification Using Convolutional Neural Network and MobileNetV3 Model
type: item
description: 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.
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: computer vision
  topic: keywords
subject: object detection
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: transfer learning
  topic: keywords
creator: Mykola Ilashchuk
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02009
ending page: 02009
doi: 10.31861/sisiot2025.2.02009

FPGA Platforms and Their Use in Edge Computing
type: item
description: 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 pl…
subject: Articles
  topic: section
subject: edge computing
  topic: keywords
subject: FPGA
  topic: keywords
subject: SoC FPGA
  topic: keywords
subject: system flexibility
  topic: keywords
subject: IoT
  topic: keywords
creator: Yurii Herman
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02008
ending page: 02008
doi: 10.31861/sisiot2025.2.02008

Precision Photodiode Current Amplifier With Output Signal Scaling Capability
type: item
description: 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 …
subject: Articles
  topic: section
subject: photodiode
  topic: keywords
subject: operational amplifier
  topic: keywords
subject: current-to-voltage converter
  topic: keywords
creator: Yurii Dobrovolsky
creator: Yurii Sorokatyi
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02007
ending page: 02007
doi: 10.31861/sisiot2025.2.02007

Overview of Visual Position Recognition
type: item
description: 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.
subject: Articles
  topic: section
subject: computer vision
  topic: keywords
subject: visual position recognition
  topic: keywords
subject: object recognition
  topic: keywords
subject: machine learning
  topic: keywords
subject: neural networks
  topic: keywords
creator: Andriy Kramar
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02006
ending page: 02006
doi: 10.31861/sisiot2025.2.02006

Control of TDS Parameter Using IoT Technologies as a Factor in Improving Equipment Reliability and Cost-Efficiency
type: item
description: 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.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: filtration
  topic: keywords
subject: ThingsBoard
  topic: keywords
subject: cloud processing
  topic: keywords
subject: condition diagnostics
  topic: keywords
creator: Roman Rusnak
creator: Marharyta Rozhdestvenska
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02005
ending page: 02005
doi: 10.31861/sisiot2025.2.02005

Review of Nuclear Quadrupole Resonance Signal Processing and Analysis Methods
type: item
description: 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 …
subject: Articles
  topic: section
subject: nuclear quadrupole resonance
  topic: keywords
subject: data analysis
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: artificial neural networks
  topic: keywords
subject: intelligent system
  topic: keywords
creator: Serhii Hospodariuk
creator: Andrii Samila
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02004
ending page: 02004
doi: 10.31861/sisiot2025.2.02004

An Adaptive Benchmark Testing Method for Evaluating Arithmetic Precision
type: item
description: 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…
subject: Articles
  topic: section
subject: arithmetic verification
  topic: keywords
subject: floating-point arithmetic
  topic: keywords
subject: precision evaluation
  topic: keywords
subject: numerical accuracy
  topic: keywords
subject: adaptive testing
  topic: keywords
creator: Denys Deineko
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02003
ending page: 02003
doi: 10.31861/sisiot2025.2.02003

Preprocessing of Object Images Before Their Detection Using YOLO Neural Network
type: item
description: 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 system…
subject: Articles
  topic: section
subject: digital image processing
  topic: keywords
subject: artificial neural networks
  topic: keywords
subject: YOLO
  topic: keywords
subject: object detection
  topic: keywords
subject: software
  topic: keywords
creator: Serhiy Balovsyak
creator: Serhii Stets
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02002
ending page: 02002
doi: 10.31861/sisiot2025.2.02002

Method of Granular Analysis of Shared Resource Utilization in a Kubernetes Cluster
type: item
description: 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 …
subject: Articles
  topic: section
subject: Kubernetes
  topic: keywords
subject: resources optimization
  topic: keywords
subject: containerization
  topic: keywords
subject: analysis of resources usage
  topic: keywords
subject: clusterization
  topic: keywords
creator: Mykola Buriak
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 02001
ending page: 02001
doi: 10.31861/sisiot2025.2.02001

Telecommunication System for Transmission of Adaptively Scaled Digital Images
type: item
description: 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 us…
subject: Articles
  topic: section
subject: telecommunication systems
  topic: keywords
subject: digital image scaling
  topic: keywords
subject: interpolation algorithms
  topic: keywords
subject: video camera
  topic: keywords
subject: Internet of Things
  topic: keywords
creator: Yurii Hnatiuk
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01012
ending page: 01012
doi: 10.31861/sisiot2025.1.01012

AI-enabled Cloud SDN Controllers: Architecture, Scalability, and Security – A Comparative Study
type: item
description: 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.
subject: Articles
  topic: section
subject: software-defined networking
  topic: keywords
subject: cloud infrastructure
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: quality of service
  topic: keywords
subject: resource constraints
  topic: keywords
creator: Anatolii Banar
creator: Heorhii Vorobets
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01011
ending page: 01011
doi: 10.31861/sisiot2025.1.01011

Perspectives on the Development and Use of Nuclear Methods for Detecting Landmines and Minefields: A Review
type: item
description: 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.
subject: Articles
  topic: section
subject: nuclear quadrupole resonance
  topic: keywords
subject: neutron-based detector
  topic: keywords
subject: landmines
  topic: keywords
subject: mine-fields detection
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Artem Kasianchuk
creator: Halyna Lastivka
creator: Andrii Samila
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01010
ending page: 01010
doi: 10.31861/sisiot2025.1.01010

Filtration Methods in Sonar Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: hydroacoustic
  topic: keywords
subject: wavelet transformation
  topic: keywords
subject: Fourier transform
  topic: keywords
subject: Wiener filter
  topic: keywords
creator: Yevhen Parkhomenko
creator: Halyna Lastivka
creator: Oleksandr Lastivka
creator: Andrii Samila
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01009
ending page: 01009
doi: 10.31861/sisiot2025.1.01009

Features of the Application of Compound Codes in Telecommunications
type: item
description: 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.
subject: Articles
  topic: section
subject: compound codes
  topic: keywords
subject: interference
  topic: keywords
subject: corrective property
  topic: keywords
subject: optimality
  topic: keywords
subject: redundancy
  topic: keywords
creator: Oleksandr Yevdochenko
creator: Oleksandr Shefer
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01008
ending page: 01008
doi: 10.31861/sisiot2025.1.01008

Investigation of Statistical Characteristics of Random Number Sequences Generated by a Webcam Using Cellular Automata Functionality and NIST Patterns
type: item
description: 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.
subject: Articles
  topic: section
subject: software engineering
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: random number sequences
  topic: keywords
subject: webcam
  topic: keywords
subject: cellular automata
  topic: keywords
creator: Heorhii Prokhorov
creator: Mariia Hanzhelo
creator: Rostyslav Diachuk
creator: Serhii Yanushevskyi
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01007
ending page: 01007
doi: 10.31861/sisiot2025.1.01007

Interference-based Research of the Goos-Hänchen Effect
type: item
description: 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.
subject: Articles
  topic: section
subject: Goos-Hänchen effect
  topic: keywords
subject: total internal reflection
  topic: keywords
subject: orthogonal polarizations
  topic: keywords
subject: fiber optic communication lines
  topic: keywords
subject: polarizer
  topic: keywords
creator: Petro Maksymiak
creator: Serhii Shchukin
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01006
ending page: 01006
doi: 10.31861/sisiot2025.1.01006

Thermal Losses of an Anisotropic Electrically Conductive Transformer
type: item
description: 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 …
subject: Articles
  topic: section
subject: anisotropy
  topic: keywords
subject: electrical conductivity
  topic: keywords
subject: transformer
  topic: keywords
subject: thermal losses
  topic: keywords
subject: infocommunication system components
  topic: keywords
creator: Anatoly Ashcheulov
creator: Mykola Derevianchuk
creator: Dmytro Lavreniuk
creator: Marharyta Rozhdestvenska
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01005
ending page: 01005
doi: 10.31861/sisiot2025.1.01005

Information Technology for Data Compression and Transformation by Means of Amazon EMR
type: item
description: 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.
subject: Articles
  topic: section
subject: information technology
  topic: keywords
subject: Big Data
  topic: keywords
subject: AWS
  topic: keywords
subject: distributed processing
  topic: keywords
subject: data compression
  topic: keywords
creator: Yevhen Kyrychenko
creator: Igor Malyk
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01004
ending page: 01004
doi: 10.31861/sisiot2025.1.01004

A Methodological Approach to Auditing Software Engineering Practices in the Energy Sector
type: item
description: 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.
subject: Articles
  topic: section
subject: software audit
  topic: keywords
subject: energy sector
  topic: keywords
subject: software engineering
  topic: keywords
subject: functional safety
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Ihor Liutak
creator: Zinoviy Liutak
rights: Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 01003
ending page: 01003
doi: 10.31861/sisiot2025.1.01003