Serhii Baraban

Chaotic waveforms provide a deterministic and noise-like means of masking information in wireless communication systems. This work presents an FPGA-based implementation and experimental validation of a frequency-modulated amplitude-shift keying (FM-ASK) chaotic communication scheme using a deterministic broadband chaotic waveform generated by a dynamical system of a modernized Anishchenko–Astakhov type. The chaotic generator is derived from a transistor structure with negative differential resistance and implemented on an FPGA using Q16.16 fixed-point arithmetic and non-pipelined Euler numerical integration. The hardware implementation is validated against a high-precision Python model using time-domain waveforms, phase portraits, Poincaré sections, bifurcation diagrams, and Lyapunov characteristics. The resulting chaotic waveform is integrated into a complete radio-frequency transmission and reception chain operating in the 2.3–2.5 GHz frequency range. Experimental measurements confirm the information transmission at data rates of up to 500 kbit/s over distances of up to 1 km at a transmit power of 25 dBm. The system also demonstrates information transmission in the presence of a substantially stronger wideband interference signal. For an interference bandwidth of 157.8 MHz and an experimentally determined receiver bandwidth of up to 100 MHz, the estimated interference-to-signal power ratio is approximately 20.0–24.8 dB for interference powers of 50–150 W, corresponding to an estimated SINR of approximately −20.0 to −24.8 dB when receiver noise is negligible compared with the interference. The obtained results demonstrate the feasibility of implementing deterministic broadband chaotic communication on FPGA hardware and its practical applicability to information transmission even under strong wideband interference conditions.

The rapid development of smart energy infrastructures and renewable energy systems requires advanced sensing solutions that provide high accuracy, expandability, and stability under real operating conditions. However, conventional gas monitoring systems are predominantly based on resistive or voltage-output sensors, which require complex analog front-end circuits and analog-to-digital conversion, leading to increased system complexity, cost, and susceptibility to electromagnetic interference. This paper tackles this limitation by proposing a frequency-domain sensing approach for multichannel monitoring of biogas plant parameters. The objective of this study is to develop and experimentally validate an extendable sensing architecture based on autogenerator microelectronic gas transducers with direct gas concentration–frequency conversion and FPGA-based digital acquisition. The proposed method is grounded in a physical–mathematical model of the space-charge capacitance of gas-sensitive semiconductor structures derived from Poisson’s equation, facilitating analytical formulation of conversion and sensitivity functions. A multichannel FPGA-based measurement system is implemented to process frequency signals without analog conditioning or ADC stages. Experimental validation was performed for CH₄ (0–85%), CO₂ (0–60%), H₂, NH₃, and H₂S (1–20,000 ppm). The results demonstrate measurement uncertainty within 0.25–0.5%, with sensitivity reaching 350–748 Hz/ppm for H₂, 455–750 Hz/ppm for NH₃, and 253–375 Hz/ppm for H₂S, while methane and carbon dioxide sensitivities reach up to 112 kHz/% and 98.7 kHz/%, respectively. Spectral analysis in the LTE-1800 band confirms improved noise immunity (up to 4.5×) and extended transmission capabilities. A 12-channel FPGA-based monitoring system (RDM-BP-1) with a 1 s sampling interval, IP67 protection, and wireless connectivity is developed and validated. The proposed architecture eliminates analog signal conditioning, reduces hardware complexity, and provides an easily expandable and reliable sensing solution for smart buildings, renewable energy systems, and cloud-integrated energy infrastructures.

This study presents a model-driven approach to the design, calibration, and application of frequency-output pressure sensors integrated within an intelligent system for real-time monitoring of rowing performance. The proposed system captures biomechanical parameters of the “boat–rower” complex across 50 parallel channels with a temporal resolution of 8–12 ms. At the core of the sensing architecture are parametric pressure transducers incorporating strain-gauge primary elements and microelectronic auto-generator circuits featuring negative differential resistance (NDR). These oscillating circuits convert mechanical stress into high-frequency output signals in the 1749.9–1751.9 MHz range, with pressure sensitivities from 0.365 kHz/kPa to 1.370 kHz/kPa. The sensor models are derived using physical energy conversion principles, enabling the formulation of analytical expressions for transformation and sensitivity functions. These models simplify sensitivity tuning and allow clear interpretation of how structural and electronic parameters influence output frequency. The system architecture eliminates the need for analog-to-digital converters and signal amplifiers, reducing cost and power consumption, while enabling wireless ultra high frequency (UHF) transmission of sensor data. Integrated algorithms analyze the influence of biomechanical variables on athlete performance, enabling real-time diagnostics. The proposed model-based methodology offers a scalable and accurate solution for intelligent sports instrumentation and beyond.

The paper discusses the problem of secure data aggregation in wireless sensor networks (WSNs) - a procedure that is of critical importance for reducing energy consumption, minimizing transmission overhead, and thus prolonging network lifetime. Due to the limited computational and energy resources of WSN nodes, traditional aggregation methods often fail to perform effectively in dynamic heterogeneous environments. With such a context taken into consideration, this study emphasizes the potential of artificial intelligence techniques, such as neural networks, genetic algorithms, and fuzzy logic, to enable adaptive aggregation approaches tailored to environmental and network-specific parameters. Furthermore, the integration of fuzzy logic, genetic algorithms, and artificial neural networks into a hybrid system leverages the strengths of each approach, resulting in enhanced adaptability and accuracy of the aggregation process. As part of the investigation, a fuzzy inference system (FIS) model was developed that incorporates attributes such as energy, current load, distance to the base station, and trust level. The model was implemented in Matlab using the Fuzzy Logic Designer toolbox. To further improve system performance, a genetic algorithm was applied to optimize membership functions. In the final phase, the model was transformed into an adaptive neurofuzzy inference system (ANFIS) which was trained using simulated data within Matlab. The simulation results demonstrate that the proposed hybrid approach ensures flexible, robust and energy-efficient control of the data aggregation process under dynamically changing conditions in which WSNs operate.

This paper addresses critical challenges in renewable energy research, particularly under the difficult operational conditions caused by the military conflict in Ukraine. Despite significant infrastructure loss due to the armed conflict (13% of solar and 70% of wind power), Ukraine maintains a commitment to reach 27% renewable energy in final consumption by 2030. However, the wartime conditions present unique challenges to scientific research, with laboratories vulnerable to missile strikes and frequently requiring evacuation. This paper introduces innovative portable laboratory stands designed for comprehensive analysis and monitoring of photovoltaic (PV) module parameters. These portable platforms, integrating advanced microcontrollers, sensors, and data-processing units, enable effective real-time monitoring and parameter estimation of PV modules, thereby enhancing their operational efficiency and reliability. Two distinct portable laboratory setups were developed and are detailed: the first focuses on real-time voltage and current measurements, while the second, termed the photovoltaic module parameter scanner (SPFEM), emphasizes data collection, remote data transmission, and database integration for subsequent analysis. This research provides essential tools for ensuring continuity in scientific activities and practical training for students and researchers amidst the ongoing security threats. The presented systems significantly contribute to optimizing the performance of PV systems in Ukraine and underscore the necessity for continuous adaptation and technological advancement in renewable energy infrastructure.

The paper addresses critical challenges in renewable energy research, particularly under the difficult operational conditions caused by military conflicts in Ukraine. Despite significant infrastructure loss due to armed conflict (13% of solar and 70% of wind power), Ukraine maintains commitments to reach 27% renewable energy in final consumption by 2030. However, wartime conditions present unique challenges to scientific research, with laboratories vulnerable to missile strikes and requiring frequent evacuations. This paper introduces innovative portable laboratory stands designed for comprehensive analysis and monitoring of photovoltaic (PV) module parameters. These portable platforms, integrating advanced microcontrollers, sensors, and data processing units, enable effective real-time monitoring and parameter estimation of PV modules, thereby enhancing their operational efficiency and reliability. Two distinct portable laboratory setups are developed and detailed: the first focuses on real-time voltage and current measurements, while the second, termed the Photovoltaic Module Parameter Scanner (SPFEM), emphasizes data collection, remote data transmission, and database integration for subsequent analysis. This research provides essential tools for ensuring continuity in scientific activities and practical training for students and researchers amidst ongoing security threats. The presented systems significantly contribute to optimizing the performance of PV systems in Ukraine and underscore the necessity for continuous adaptation and technological advancement in renewable energy infrastructure.

The accuracy of ultrasonic rangefinders is crucial for mobile robotic navigation systems, yet environmental factors such as temperature, humidity, atmospheric pressure, and wind conditions can influence ultrasonic speed in the air. The primary objective is to investigate how environmental factors influence the output signal of an ultrasonic emitter and to develop a method for improving the accuracy of distance measurements in both outdoor and indoor settings. The research employs a combination of theoretical modeling, statistical analysis, and experimental validation. The research employs an ultrasonic rangefinder integrated with environmental sensors (BME280, Bosch Sensortec GmbH, Kusterdingen, Germany) and wind sensors (WMT700, WINDCAP^®, Vaisala Oyj, Vantaa, Finland) to account for environmental influences. Experimental studies were conducted using a prototype ultrasonic rangefinder, and statistical analysis (Student’s t-test) was performed on collected data. The results of estimation by Student’s t-test for 256 measurements demonstrate the maximum effect of air temperature and the minimum effect of relative air humidity on a piezoelectric emitter output signal both outdoors and indoors. In addition, wind parameters affect the rangefinder’s operation. The maximum range of obstacle detection depends on the reflection coefficient of the material that covers the obstacle. The results align with theoretical expectations for highly reflective surfaces. A cascade-forward artificial neural network model was developed to refine distance estimations. This study demonstrates the importance of considering environmental factors in ultrasonic rangefinder systems for mobile robots. By integrating environmental sensors and using statistical analysis, the accuracy of distance measurements can be significantly improved. The results contribute to the development of more reliable navigation systems for mobile robots operating in diverse environments.

The paper presents a comprehensive analysis of the innovation potential of Vinnytsia National Technical University (VNTU), focusing on the ten-year experience of the University`s startup school “Vinnytsia Challenge”— a regional center of the nationwide innovation ecosystem “Sikorsky Challenge Ukraine.” The study investigates the integration of practical entrepreneurial experience into higher education and research activities. A structured model for effective interaction between academia, science, and business within the University`s innovation ecosystem is proposed, emphasizing the strategic role of startups in enhancing institutional innovation capacity. The development of the startup school is traced across four key stages: initial formation (2015—2017), rapid expansion (2018—2020), international recognition (2021—2023), and sustainable integration (2024–present). The school has incubated dozens of high-impact projects in critical domains such as defense, healthcare, ecology, and information technology. Its educational framework includes over 130 hours of training in innovation management, startup marketing, artificial intelligence applications, financial planning, and pitching, supported by both local and international experts and mentors. Prominent outcomes include award-winning startups such as SolarInt (renewable energy storage), BrightBrille (AI-powered accessibility tools), and Escadron (remote-operated battlefield transport). The study outlines the financial architecture supporting the school, including state grants, municipal funding, and strategic investments from private Israeli firms. Key institutional challenges—such as limited early-stage funding, weak commercialization pipelines, and insufficient faculty engagement — are critically analyzed, and evidence-based solutions are offered. These include the establishment of university-level venture funds, incentivization frameworks for faculty, and enhanced access to global startup networks. The findings underscore the replicability of the “Vinnytsia Challenge” model across other Ukrainian universities, advocating the formation of regional startup hubs and methodical dissemination of best practices. The experience accumulated by VNTU demonstrates a scalable approach to cultivating innovation-driven human capital and strengthening the national ecosystem during post-war reconstruction and digital transformation. The study's insights offer a strategic foundation for aligning academic institutions with market-oriented innovation processes, thereby enabling universities to act as engines of economic resilience and technological advancement. Комплексно проаналізовано інноваційний потенціал Вінницького національного технічного університету (ВНТУ) з урахуванням десятирічного досвіду діяльності Школи стартапів «Вінницький виклик», яка функціонує в рамках Всеукраїнської інноваційної екосистеми «Sikorsky Challenge Україна». Дослідження зосереджено на визначенні ефективних механізмів інтеграції практичного досвіду стартап-руху в освітній процес та наукову діяльність ВНТУ. Запропоновано модель взаємодії освіти, науки та бізнесу, яка забезпечує формування університетського середовища, сприятливого для інноваційного підприємництва та комерціалізації наукових розробок. Важливою складовою є опис діяльності школи стартапів протягом 2015–2025 років, де простежено її еволюцію від локальної ініціативи до національного та міжнародного визнання. У межах школи реалізовано десятки технологічних проєктів, зокрема у галузях оборони, енергетики, медицини та екології. Значну увагу приділено підготовці студентів та викладачів у сфері стартап-підприємництва через навчальні модулі, менторство, міжнародне стажування, пітчинг-платформи та роботу з інвесторами. У статті також зауважено на успішних стартапах, що отримали міжнародні відзнаки, такі як SolarInt (енергетика), BrightBrille (AI), Escadron (оборонна технологія). Описано механізми організаційної та фінансової підтримки школи, зокрема гранти Міністерства освіти і науки України, співпрацю з міською радою та підтримку з боку ізраїльських інвесторів. Проаналізовано головні виклики, зокрема недофінансування ранніх етапів стартапів, низьку мотивацію викладачів до участі в підприємницьких активностях, та запропоновано шляхи подолання цих бар’єрів. Зроблено висновок про можливість масштабування моделі «Вінницького виклику» на інші заклади вищої освіти України, з урахуванням місцевих особливостей та...