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
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.
Copyright (c) 2025 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
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.
IoT Based Smart Parking Systems in Unity With ML-agents Toolkit
Modern cities face problems with limited parking space, which requires effective optimization to reduce congestion. This paper presents an up-to-date approach to solving the problem of parking in restricted space conditions using the Unity Machine Learning Agents Toolkit (ML-Agents). This toolkit is based on artificial neural networks. It opens up wide opportunities for training agents capable of performing tasks in real time to optimize the use of parking areas and reduce the time to search for free spaces in real-time. Data transmission from sensors in Internet of Things (IoT) systems plays a key role in optimizing the use of parking spaces and increasing driver comfort. For this purpose, modern IoT technologies are integrated into the system, which allows the effective solving of parking problems in limited urban space conditions. The concept of an intelligent parking system is presented, which uses sensor technologies and machine learning algorithms to increase the efficiency of the process and driver comfort. Despite the high costs of installation and maintenance of traditional systems, the use of the Unity game engine and ML-Agents allows you to create training environments for preliminary testing, debugging, and optimization of algorithms. In the course of the research, a machine learning model was developed and analyzed using the Proximal Policy Optimization algorithm, which allowed us to reproduce various agent training scenarios. This contributed to the acceleration and stabilization of the training process, as well as the establishment of optimal model parameters based on the analysis of key performance metrics. The results of testing and comparative analysis confirm the prospects of the proposed approach in the field of autonomous car parking.
Methodology for Using Metadata in Machine Learning to Enhance the Security of State Information Systems
The article conducts an in-depth examination of a comprehensive methodology for the integration of metadata into neural networks, aiming to enhance the security frameworks of state information systems. Metadata, encompassing a wide range of contextual information such as timestamps, geolocation data, and user behavioral characteristics, plays a pivotal role in strengthening the capacity to detect and mitigate potential cyber threats. This approach leverages the advanced capabilities of neural networks and state-of-the-art computational technologies, facilitating the effective utilization of metadata across critical domains, including public administration, healthcare, transportation, and cybersecurity. The integration of such metadata is of paramount importance in sectors where the precision and speed of threat detection are essential for averting catastrophic consequences. The proposed methodology underscores the embedding of metadata directly into neural network architectures to enable the detailed analysis of anomalous activities within information systems. This integration significantly enhances the precision, adaptability, and efficiency of cybersecurity measures. The classification and categorization of metadata within neural networks provide a robust foundation for deep analytical capabilities and facilitate rapid adaptation to emerging threats and shifting environmental conditions. Moreover, the research delves into the development and application of innovative algorithms capable of processing and managing extensive volumes of data. These algorithms are designed to ensure scalability, maintain robustness, and enhance the operational resilience of cybersecurity frameworks. Furthermore, the article explores the practical implications and real-world implementation of these algorithms, illustrating their applicability to large-scale government systems and critical infrastructures. By integrating metadata into neural networks, the study demonstrates how these systems can achieve heightened levels of protection against cyber threats. Through detailed case studies and practical applications, the research highlights the transformative potential of metadata-driven neural networks in bolstering the security of critical infrastructures. The findings emphasize the necessity of data-driven decision-making in modern cybersecurity paradigms and outline the prospective expansion of the proposed model to address future challenges. The model’s ability to improve resilience against evolving threats and enhance real-time response capabilities within dynamic environments is particularly noteworthy. The study concludes by showcasing the potential of this methodology to revolutionize cybersecurity practices, offering a scalable and adaptable solution to mitigate risks and ensure the integrity of state information systems.
Methods for the Linearisation of the Transfer Function of Thermoresistive Transducers
The article describes digital and analogue methods for linearising the conversion function of thermoresistive transducers, with a detailed analysis of analogue methods. Analogue methods for linearising bridge circuits and measurement circuits based on passing a reference current through a resistance temperature detector (RTD) are considered. Linearisation of bridge circuits is based on the formation of the compensating supply voltage of the bridge circuit, which depends on the change in the measured temperature. When using the measurement method based on passing a reference current through an RTD, nonlinearity compensation is achieved by changing the conversion coefficient of the measuring signal or passing an additional current through the RTD, which linearly depends on the value of the RTD’s voltage change (measured temperature value). When passing an additional compensation current through the RTD, the nonlinearity error is not grater than 0.1°C in the range of 0…800°C, and the schematic diagram of the measuring transducer contains a minimum number of elements, which allows to increase its reliability. In general, the choice of a linearisation method depends on the requirements for accuracy, operation rate and resource limitations of the measuring system.
Copyright (c) 2024 Security of Infocommunication Systems and Internet of Things https://creativecommons.org/licenses/by/4.0
The article provides further development of software and hardware solutions for building fully functional Industrial Internet of Things (IIoT) systems based on high-speed SPI. The possibility of overcoming the obstacles caused by the limitation of the set of input/output lines to a more complete use of the redundant computing resource of Wi-Fi modules outside the provision of "access point – station" information exchange for solving problems related to other aspects of the functioning of the embedded system has been investigated. It has been established that the current issue is to expand the basic capabilities of embedded system components, taking into account the provision of comprehensive access to the SPI bus for all the functionality of typical IIoT systems. The implementation of the proposed hardware and software solutions was carried out in the form of an expander module for the basic capabilities of the SPI bus, designed to intensify information exchange over the Wi-Fi channel between the SoC controller and peripherals: IO and LCD extensions, SD cards, DRAM and SPI-Flash file storage. The SPIExpander hardware driver and the MultySwitcher software driver for its control were developed. In software driver developing was implement the HAL paradigm and was used RTOS methods to prevent abnormal situations of multiple access on the bus. The advantages of introducing the high-speed SPI bus as opposed to the STM-FSMC interface are demonstrated in the example with the implementation of their HMI components into the structure of the embedded device. SPIExpander was used as a system proxy driver that implements the function of a network switch, with controlled by DrvLoadBlock software driver DrvSPI2LCD hardware adapter driver connecting on the one of its serial ports for converts DMA packets into a stream of 16-bit words. Hardware stability has been increased and the set of input lines for generating LCD control system signals and conducting DMA block operations has been minimized. The software model of the driver for the implementation of polycore and multithreaded information exchange is presented. Recommendations for using the SPI-FSMC expander are provided.
Research Into the Efficiency of Processing a Numerical Random Sequence by Chaotic-type Cellular Automata
The article presents the results of a study of the processing of a sequence of random numbers obtained from a single webcam frame for compliance with one of the requirements of information cryptosecurity: uniformity of distribution. For the experiment an ordinary domestic web camera was used. The functionality of extraction a random number sequence from a frame of a web camera was discussed in the previous investigation. Java language provided software support. Before the main investigation a random number sequence generated by Java class was explored. The practically permissible level of uniformity of distribution for this sequence was obtained. For Java class SecureRandom this level is equal to 1.7%. For the purposes of experiment a high-speed sequence processing functionality based on cellular automata has been developed. As an origin of experiment an occasional snapshot of the web camera was used. As a result, it was found that a processing by chaotic rules (rules 30, 90, 105, 150) of linear cellular automata improves the quality of distribution of elements of a sequence almost to ideal level. It was found that the rule 30 provides the highest efficiency and productivity, but does not differ much from other rules. All the results were supported by statistical calculations and drawing custom graphical histograms especially designed for the experiment with a help of Java statistical and graphical classes. It was established that by selecting the number of iterations, the required level of uniformity of the distribution of sequence elements can be obtained. In theory a high-resolution web camera can provide a productivity of 2 Gbit/s. The results of the investigation can become the basis for the development of an affordable high-speed reliable hardware-software generator of a sequence of random numbers.
Analysis of Machine Learning Methods in Navigation and Trajectory Planning for Autonomous Control of Unmanned Systems
This article investigates the use of machine learning methods in navigation and trajectory planning for the autonomous control of unmanned systems. The main approaches, such as deep learning and reinforcement learning, are considered, offering innovative solutions to challenges arising in dynamic and complex environments. An overview of machine learning methods is conducted, highlighting their advantages over traditional algorithms due to flexibility, adaptability, and the ability to operate under uncertainty. The application of machine learning in trajectory planning is analyzed, including the use of autoencoders, generative models, and graph neural networks for predicting and optimizing routes. Existing problems and challenges are discussed, particularly ensuring safety and reliability, the need for large volumes of high-quality data, issues of model interpretability, and regulatory aspects. Prospects for development are identified, including the development of more efficient algorithms, enhancing model transparency, and establishing standards for the responsible deployment of autonomous systems. In conclusion, it is emphasized that machine learning is a transformative force in the field of autonomous navigation and trajectory planning. Overcoming current challenges and continuing innovation will unlock the full potential of unmanned systems, bringing significant benefits to society and the economy through widespread application across various sectors.
Development of n-TiN/p-CdTe/n-CdTe Phototransistors for Use in a Networked Digital Light Sensor
In this work, the mode of production by reactive magnetron sputtering of n-TiN/p-CdTe/n-CdTe heterostructures, which have the properties of a phototransistor with an unconnected (floating) base, is investigated. It is shown that in the structure of n-TiN/p-CdTe/n-CdTe at reverse voltage at the collector junction of p-CdTe/n-CdTe under conditions of irradiation close to АМ1 from the side of the n-TiN emitter, the phenomenon of amplification of the collector photocurrent by the direct current of the directly switched on is observed of the n-TiN/p-CdTe heterojunction. A forward voltage is applied to the n-TiN/p-CdTe heterojunction due to the positive charge of the p-CdTe base during illumination. Photoelectric phenomena during illumination of the n-TiN/p-CdTe/n-CdTe heterostructure were analyzed. The purpose of this work is to evaluate the practical use of the newly created n-TiN/p-CdTe/n-CdTe heterostructure in the phototransistor mode of operation to create an illumination sensor with a digital interface for use in network control and management systems. The possibility of using the manufactured transistor as a digital illuminance sensor for large values of light flux is substantiated. The phototransistor was used as the primary converter for the microcontroller, which includes a functional block of the operational amplifier. The phototransistor and the operational amplifier together implement the photocurrent-voltage converter system. This solution allows you to use the internal operational amplifier of microcontrollers with a unipolar power supply. The linearization of the transfer characteristics of the phototransistor allows you to use microcontrollers with low computing power. It was established that the transfer characteristics are closer to linear ones with an increase in the voltage between the collector and the emitter of the phototransistor. The sensor device using the developed transistor was assembled on a PIC16F1713 series microcontroller, which contains an operational amplifier that allows the implementation of a universal synchronous-asynchronous receiver/transmitter digital interface for transmitting the measured light flux data. Implementing a wireless light sensor on the GB2530 module, which is based on the system on a chip CC2530 allows you to get a mobile device. Data exchange on the airwaves takes place in accordance with the IEEE 802.15.4 standard.
Scanning of Three-Dimensional Objects by Photogrammetry Methods Using LiDAR and Mobile Computing
Three-dimensional models of objects are widely used in various fields, including science, construction, healthcare, and entertainment, making the task of creating such models highly relevant. The primary goal of this paper is to develop a mobile application which uses photogrammetry methods for capturing real-world three-dimensional objects or environments. The main advantage of photogrammetry is low hardware requirements with relatively high accuracy of the models obtained. Additionally, the application utilizes smartphone’s LiDAR sensor to enhance capture quality, especially for low-textured objects. The LiDAR sensor allows for precise distance measurement between the device and the object, which is crucial for accurately capturing the object’s size and position. To build 3D model of the object from a series of images, the application uses Object Capture API, available on iOS, iPadOS, and macOS operating systems. This API fully leverages the build-in GPU and Neural Processing Unit to build and tessellate the point cloud and generate the polygonal mesh model. The application was developed for iPhones that support LiDAR sensor, using Swift programming language, SwiftUI for the user interface, and RealityKit for Object Capture API. The app features three modes: object capturing, model reconstruction and model preview. To simplify the process of capturing the object, the app can automatically take photos of the object, provide the user with guidance and recommendations on optimal lighting conditions, camera positioning, and a preview of the point cloud. Once object capture is complete, the application transitions to the reconstruction mode, which uses captured photos and point cloud data. This process involves image alignment, detailed point cloud generation, polygonal mesh model generation, texture and normal map generation, and model optimization. After reconstruction, the user can obtain model in the USDZ file format and preview it using built-in system tools. Following these steps, two test models of a drum and a garden statue were built with satisfactory quality and accuracy, while maintaining the original size of the scanned objects. The resulting three-dimensional polygonal models can be successfully exported to various 3D editors and programs.
Integrated Neural Network and Wavelet-Based Model for Web Server Load Forecasting
This paper presents an integrated model for predicting the load on a web server by combining historical server logs, traffic data, and environmental factors to forecast load variations accurately. Key components include time series analysis for trend and seasonality detection, discrete wavelet transforms for noise reduction and feature extraction and neural networks for predictive modeling. Experimental results demonstrate that the integrated model achieves 15–25% higher forecasting accuracy compared to traditional methods, such as ARIMA. The proposed solution is scalable, adaptable, and provides a foundation for proactive load balancing and resource allocation strategies, ensuring robust server performance even during peak demand. The integrated model accounts for both short-term and long-term load variations, which is crucial for predicting peak loads and planning server resources. Future research may focus on optimizing algorithms and expanding the applications of this model to other systems, including cloud computing and distributed systems. The increasing demand for reliable and efficient web services necessitates accurate load prediction models to ensure optimal server performance and user experience. The modularity of the proposed model makes it scalable and adaptable, providing a foundation for active load balancing and resource allocation strategies to maintain server reliability even during peak load periods. A notable feature of the model is its ability to consider a wide range of variables, making it versatile for various types of data through the combination of classical statistical methods and modern machine learning algorithms. In addition to forecasting web server load, the proposed integrated model can be utilized for user behavior analysis, optimizing energy consumption, monitoring and predicting in data centers.
Analysis of Amateur Radio Frequency Code Modulation Protocols for Transmitting Short Messages
The article is dedicated to analyzing common short message transmission protocols in digital code used by radio amateurs in amateur radio communication. The following protocols are considered: RTTY, AMTOR, PACTOR1, ISCAT, JT4, JT9, JT65, FT4, and FT8. All of them use frequency shift keying (FSK), which provides significantly better noise resistance since information is encoded in frequency changes rather than amplitude. Because of this, FM is widely used in broadcasting. The primary purpose of the listed protocols is to prevent signal fading due to radio wave reflection from the ionospheric layer (sporadic propagation), where reflection windows can be short. Therefore, modulation must be fast, and data transmission reliability must be high to ensure successful radio exchange. The article analyzes the main characteristics of these protocols, including the maximum signal-to-noise ratio (SNR) at which digital data reception and decoding are possible, transmission speed, and the bandwidth occupied by the signal. Additionally, related characteristics are examined, such as the bit transmission time and the use of error-correcting codes for noise immunity. The main FSK modulation modes are reviewed, comparing their advantages and disadvantages. It has been established that the JT4, JT9, JT65, QRA4, WSPR, FT4, and FT8 protocols provide a SNR greater than -20 dB compared to RTTY, AMTOR, PACTOR, and ISCAT. In such protocols, the signal occupies a narrower bandwidth. All the discussed protocols ensure a higher message transmission speed compared to other modulation modes. The conclusion is made that frequency modulation, combined with the use of error correction codes, significantly improves the SNR. The application of similar signal detection and decoding methods could enable the development of a decoder with substantially higher sensitivity. The development of a new type of signal for digital information transmission can be based on modifications of the JT4, JT9, JT65, QRA4, WSPR, FT4, and FT8 protocols.
Risk Management of Information Threats in IT with the Help of Intelligent Disinformation Detection Systems
The article describes an intelligent disinformation detection system that counteracts the spread of false information on the Internet. It uses modern text and data processing methods, including machine learning and natural language processing, to accurately identify fake news. The main function of the system is to provide a predictive assessment of the reliability of information, which helps users make informed decisions, minimising the risk of falling under the influence of disinformation. Real-world testing of the system has confirmed its ability to quickly identify fake information, contributing to the information literacy of users and raising their awareness of information threats. Such a system is an important element in strengthening information security, which is especially important for Ukraine in the face of numerous information challenges. The system also plays an important role in managing the risks of information threats in IT. The results of the study made it possible to identify potential threats in the form of disinformation that could be used to manipulate public opinion or undermine trust in institutions. Integration of intelligent systems into risk management processes allows for a timely response to threats, reducing their impact on IT infrastructure and preserving the reputation of organisations. The system can be applied not only in the IT sector, but also in journalism, education, and public administration, where it helps prevent disinformation that has a serious social impact. It can also be used to monitor and analyse information flows, which helps identify and counteract false information. Thus, the developed system is an important step in strengthening information security, providing protection against fake news and serving as an effective tool for managing the risks of information threats in today's digital society.
Periodicity of Timeseries Generated by Logistic Map: Part II
The paper investigates the impact of limited computational precision on chaotic systems used in cryptography, focusing on how floating-point arithmetic influences the periodicity and degradation of chaotic timeseries. The study analyses the behaviour of logistic map, revealing that different initial conditions and parameters lead to varying cycle lengths, which are critical for maintaining chaos in encryption algorithms. The order of arithmetic operations and the choice of coupling methods between maps are shown to significantly affect the system's dynamics. The efficiency of using floating-point and fixed-point arithmetic is compared, demonstrating that precision limitations can lead to the degradation of chaos, thereby compromising the effectiveness of some chaos-based ciphers. The research provides insights into the behaviour of chaotic systems under computational constraints, aiming to improve the reliability and security of chaotic encryption techniques.
Prospects for the Application of Wavelet Analysis in the Problems of Classification of Hydroacoustic Signals
Wavelet transformation is widely used for signal analysis, which, unlike the classical Fourier transform, make it possible to simultaneously detect low-frequency and high-frequency characteristics of signals on different time scales (i.e., it is feasible to examine how the frequency spectrum of signals varies over time). The theory of wavelet transformation relies on the concept of multi-scale analysis, which involves examining the signal at various frequencies and resolutions. At the same time, it increases the flexibility of signal processing methods and expands the scope of their application. Wavelet transformation is also widely used in information and telecommunication systems, physics and astrophysics, mathematics, seismology, image compression, speech recognition, medicine etc. Moreover, based on wavelet analysis of non-stationary and nonlinear echoes of underwater objects, it is possible not only to classify and identify objects, but also to solve these problems in highly noisy and complex conditions for detecting hydroacoustic signals. Further research using wavelet analysis opens up new perspectives for the development and improvement of systems used to monitor and analyze complex signals in various environments. Wavelet transformation open up new horizons for scientific research, become an integral part of modern signal analysis technologies, contribute to a more effective solution of complex problems in different fields of technology and science, which ensures high accuracy and reliability of the results obtained. In addition, due to the ability to localize in the time-frequency domain and resistance to interference, wavelet transforms are used in highly efficient modern systems for detecting and identifying underwater objects. Further development of scientific research in this area will certainly further expand the possibilities of wavelet analysis, making it an even more powerful tool in the field of signal and information processing.
information and telecommunication systems
Embedded Operating Systems in IoT Edge Computing
Embedded systems and Edge Computing in Internet of Things (IoT) represent a special approach to creating systems for collecting, processing and analyzing data in an unstable environment. This article examines the benefits of Linux in this context, highlighting its flexibility, robust software ecosystems, and scalability, which are critical for a variety of IoT applications. The question of the operation of devices in an environment with a weak and/or unstable network is also raised, the general development of IoT/Internet-of-Everything (IoE) as a technology in the conditions of various distribution of high-speed networks is considered. Edge Computing technology, its use and areas of application in the need for rapid adaptation to the environment are also taken into account. Because the ability to calculate and analyze data on a local network can be critical for simplifying infrastructure in remote areas or in environments where access to an external network is difficult or impossible. We focus on the Linux kernel because its versatility in IoT is highlighted by its ability to handle a variety of workloads and seamlessly integrate with services, increasing adaptability to changing environmental conditions and ensuring reliable data processing at the edge. This adaptability is critical to mitigating the challenges caused by unreliable network infrastructure, thereby facilitating real-time decision-making and increasing operational efficiency. In addition, the open nature of Linux fosters innovation, allowing developers to create solutions tailored to the specific needs of edge computing, from industrial automation to smart city initiatives. By allowing devices to operate autonomously and efficiently manage resources at the network edge, Linux significantly optimizes latency, resource utilization, and overall system performance. Use of edge computing with correctly set-up embedded operating system (OS) allows to avoid issues common in IoT field and related to environment change. Article provides insight into pros and cons edge computing, its implementation in IoT and IoE by embedded Linux based OS. We will go through most common use-cases and market shares of common OS options. While IoT takes part in most industries by storm, there are still problems common for new industry. The primary advantage of using embedded *nix OS is the agility and ease of incorporation of those devices into edge computing systems, allowing to deal with network issues. Due to IoT/IoE being a new industry where many technologies are combined there are a lot of different approaches and frameworks that are used in it, but some of them are more popular and common than other ones. While going through the IoT/IoE data in the article, we will focus on embedded edge computing as one of the most efficient ways in building IoT solutions. Especially in perspective of OS market changes now and in near future. The result of that study will provide insight into possible trends and positives of the use of embedded OS with edge computing.
Access Control System Based on Ring Resonator’s Sensitive Properties
The paper is dedicated to the development of a new type of electromagnetic (EM) devices to achieve unique output signal patterns for their potential applications in secure systems. The proposed device involves modification of a microstrip transmission line modification by ring resonators. The ring resonator is an EM component that is characterized by high sensitivity, impedance of which can be easily adjusted by its shape changing. It was performed with the ring resonator’s microstrip lines lengthening from 1 to 13.5 mm that allows the resonance frequency changing from 1 to 1.6 GHz, demonstrating the tunability of the device. The modification of a microstrip transmission line with one or a few of such ring resonators by their strong near-field coupling leads to a deep minimum/minima appearance in the transmission line transfer function (S21-parameters spectrum). This minimum can disappear under direct touching of the ring resonator by a human finger – changing of the total capacitance of the ring resonator. It means that the consequence touching/untouching of the ring resonator leads to a modulation of the input transmission line signal and producing unique output signal patterns. As the number of ring resonators increases, the complexity of these patterns also increases. The variety of the patterns can be unique and secure; thus, the output signals can serve as a key for creation of password for systems of access control. To ensure that the security level provided by the device meets the necessary standards, the keyspace – the total number of possible unique patterns – was estimated for various combinations of the developed ring resonators. The analysis revealed that with 14 available ring resonators, the keyspace can exceed 1015, indicating a vast number of possible combinations and, therefore, a very high level of security.
Spectrum Sensing Using Wavelet Transforms and Filtering Under Signal Frequency Distortion and Fading Conditions
This article explores improving the accuracy and reliability of spectrum sensing methods within cognitive radio networks. The primary focus is on how signal fading and frequency distortion influence the results of spectral analysis. These issues can severely impact the precision of signal detection, making adaptive methods and filters indispensable for accurately detecting changes in the spectral landscape. The purpose of the paper is to evaluate the effectiveness of various adaptive methods and filters – such as wavelet transforms, along with Butterworth, Chebyshev, and Kaiser filters—in improving the detection of changes within the spectral environment across different signal-to-noise ratio (SNR) levels. The research spans a broad frequency range, concentrating on pivotal technologies like 5G NR, Wi-Fi 6, DVB-T2, and GPS, each having unique requirements for signal precision and dependability. The spectrum sensing approach described in the article achieves high signal detection accuracy under favorable conditions, particularly when the SNR is strong. Experiments revealed that with SNR values above 1 dB, the signal detection accuracy (True Positive Rate, or TPR) for all technologies examined remains at or above 0.90. For instance, the TPR for 5G NR is 0.92 at an SNR of 1 dB, while for Wi-Fi 6, it stands at 0.90. However, the effectiveness of the method declines as the SNR decreases. For example, with 5G NR, the TPR drops to 0.70 at an SNR of -21 dB, indicating a heightened probability of false signal detection. Similar patterns are observed with Wi-Fi 6, where the TPR falls to 0.65, with DVB-T2 to 0.68, and GPS to 0.66. Additionally, the average noise level rises as SNR diminishes, making accurate signal detection increasingly challenging and emphasizing the need for further refinement of these methods. The findings underscore the need for ongoing advancements in spectrum monitoring, especially under low SNR conditions. Future research should prioritize developing new or refining existing adaptive algorithms capable of operating effectively in complex spectral environments. Exploring the impact of other filtering and transformation methods could also yield valuable insights. Moreover, the incorporation of machine learning techniques offers a promising path for boosting the adaptability and accuracy of spectrum monitoring in real-world telecommunication systems.
Sub-GHz Broadband Multi-channel Waveguiding Based on Subwavelength Wire Media
The paper involves the development of a new type of image transfer. With this aim the multi-channel waveguide was considered in a novel representation – it was based on a wire media structure where each space between each four the closest wires is a single subchannel. The utilized wire media (WM) structure has 10-by-10 wires dimensions and 150 mm of the length, thus the first Fabry-Perot resonance appears around 0.9-1 GHz. The goal is to show the broadband multi-channel imaging at frequencies lower than the first Fabry-Perot resonance that means the considered structure tends to electrically short size that has not been studied previously. Taking into account the multi-channel principle of the wire media, the imaging can be performed as a binary one. For this reason, each single channel is fed by individual electromagnetic (EM) source that is a dipole antenna in the non-resonant mode. The reason is to use a weak source and match it with a single channel because in the other case the dipole is perfectly matched at the defined resonance frequency. It was found in the paper that the broadband transfer is possible in the frequency range-under-study and impossible at the region of the first Fabry-Perot resonance due to the perfect complex interaction between all wires of structure. As evidence, a simultaneous transfer of EM power from several independent EM sources shaped as R-letter was performed in the frequency range from 0.3 to 0.75 GHz by simulations and experimental investigations. The clear recovery of the transferred letter is possible in the case when it performs not at the frequency of Fabry-Perot and for the enough value of signal-to-noise ratio (SNR). The binary recovery of the transferred image became possible in the paper with an additional post-image processing with a threshold method involvement.
Technologies Overview for Typo Segregation
The article focuses particularly on the difference between typos (accidental mechanical errors) and spelling or conceptual errors that arise from insufficient knowledge of language rules. Modern typo detection methods are analyzed, highlighting the advantages and disadvantages of each. The Levenshtein method is one of the most common algorithms for detecting and correcting errors in text. It effectively identifies and corrects errors in short words where the number of operations to convert the erroneous word to the correct one is small. However, this method does not consider the context in which the word is used, which can lead to incorrect corrections. The keyboard layout-based typo detection method analyzes probable errors that can occur due to the proximity of keys on the keyboard. It is simple to implement and integrate into existing spell-checking systems but does not consider the context of word usage. The contextual analysis method for typo detection relies on using contextual information to identify and correct errors in text, requiring significant computational resources and a large, diverse corpus of texts for effective model training. Deep models, such as BERT or GPT, consider the context of entire sentences or even larger text blocks, allowing for high accuracy in typo detection but require significant computational resources for training and inference, as well as large volumes of high-quality data for training. Machine learning methods, such as n-grams and Bayesian classifiers, show significant potential due to their simplicity and efficiency but may not account for complex dependencies between words and context, reducing their accuracy. The study highlights the importance of accurate error detection in student assessment systems, where typos can affect final grades and the relevance of answers.
Analysis of Methods for Classification and Aggregation of Textual Data From Images
This study investigates modern methods of text recognition from images, specifically comparing optical character recognition and intelligent character recognition. The technologies of machine learning, including convolutional and recurrent neural networks, are compared based on criteria such as accuracy and efficiency in processing handwritten and printed texts. The advantages and limitations of existing solutions for forming digital documents from images containing various handwriting styles and low-quality text images are analyzed. Key challenges associated with processing multilingual texts are identified, and future prospects for the development of text recognition technologies are discussed.
Analysis of Results of Scaling Digital Images by Interpolation Algorithms
Scaling of digital bitmap images is often used in modern computer and telecommunications systems. Among image scaling algorithms, the most common are interpolation algorithms, namely nearest neighbor, Lanczos, bilinear and bicubic interpolations. However, in the process of scaling images by interpolation algorithms, characteristic distortions appear on them. Therefore, in this work, software implementation and research of image interpolation algorithms was performed in order to identify their advantages and disadvantages, areas of use and ways of improvement. In order to study the interpolation algorithms, a scaled fRGBs image was calculated based on the initial image fRGB, and then a scaled image fRGBs2 with the dimensions of the original image was calculated based on fRGBs. The scaling accuracy is evaluated using the Root Mean Square Error between the pixel values of the original and scaled images. The software for scaling images is developed in the Python language. Calculation of scaled images is performed by the cv2.resize() function of the OpenCV library. Using the developed program, scaling of a series of 100 images was carried out, the accuracy and speed of digital image scaling by interpolation algorithms were investigated. For each interpolation method, the average scaling error AR and the limits of its confidence interval ARmin and ARmax with a specified reliability γ are calculated. The average scaling time AT of a series of 100 images by different algorithms was determined. The research results showed that in most cases the smallest scaling error is provided by the bicubic interpolation algorithm, which is slightly inferior in speed to the nearest neighbor and bilinear interpolation algorithms. Recommendations for the application of interpolation algorithms have been developed. It is shown that the use of convolutional neural networks is promising for the highest quality image scaling.