The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

The object of the research is the developed automated computational model (AI-driven system) for real-time monitoring and analysis of road traffic, focusing on the identification and assessment of dangerous situations (traffic violations, congestion, and accident risks). This paper examines how the increased number of people moving to cities and their vehicles increases the likelihood of traffic accidents on public roads. It is also noted that traditional inspections are carried out very slowly and do not fully detect violations of traffic rules. To overcome these limitations, it is proposed a novel automated computational model for vehicle and accident tracking, based on UAVs combined with computer vision and artificial intelligence technologies. The proposed model allows for real-time threat detection and evaluation. The study, modeled in the MATLAB environment using real traffic data from drone-captured video. This model demonstrates significant improvements in operational metrics, an average detection achieved accuracy 89% for vehicles and critical events (e. g., congestion, deviations). The model successfully visualizes risk areas with heat maps and predicts short-term traffic pattern changes, increasing the reliability of traffic management and expanding the possibilities of traffic risk forecasting. The results obtained during the simulation can be used in practice by transport services, road, and maintenance organizations, particularly at difficult intersections and on highly accident-prone highways in urban, heavily built-up areas.

In recent years, the growing demand for efficient voltage boosting solutions has been driven by advancements in renewable energy systems, electric vehicles (EVs), and photovoltaic (PV) arrays. However, conventional magnetic-based inverters remain bulky and inefficient for compact, high-performance applications, limiting their use in emerging technologies. To address this, the objective of this study is to develop a compact, single-source switched-capacitor multilevel inverter (SC-MLI) topology that achieves high voltage gain with minimal component count. The proposed 13-level SC-MLI employs a novel switched-capacitor structure and is evaluated under Natural Level Control (NLC) and Sinusoidal PWM (SPWM) schemes. Theoretical analysis, MATLAB/Simulink simulations, and experimental validation on a 100–1000 W prototype are carried out, along with thermal modeling in PLECS. The results show that the topology achieves a voltage gain of 3, maintains capacitor self-balancing without auxiliary circuits, and reaches a peak efficiency of 97.2% (simulation) and 95.3% (experiment). Moreover, it meets harmonic standards, reduces total harmonic distortion (THD), and outperforms recent single-source designs in terms of accuracy, cost, and control simplicity. This makes the proposed topology highly suitable for grid-connected PV systems, electric vehicle chargers, and compact renewable energy interfaces, with theoretical scalability toward medium- and high-power applications.

This article considers multilevel transistor inverter for converting energy of solar panels into electroenergy. The output of multilevel transistor inverter produces the voltage of almost sinusoidal form. The primary objective of this inverter is to transform solar energy into electroenergy of industrial frequency. The analysis of received output curves of voltage for sinusoidality has been conducted.