Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.

The research team has developed a complex system that is capable to record and analyse various psychophysiological data. This article represents the program created in NI DIAdem which can automatically identify illnesses and generate reports based on the input and saved parameters. The created program can import data from different types of file formats. It is converted and saved in a new standardized format for further processing. It can play-back the processed data synchronized with adjustable speed, and visualize them on customized display areas. It can analyse the data based on the input parameters and the pre-defined mathematical equations. The program displays the input and calculated parameters, the results and the detected illnesses on automatically generated reports.

This paper presents the examination of the suspension of a quarter car model from the point of view of observability and controllability. The modeling of an active suspension with full state feedback control and the design of a state observer is planned to be used for real-time processing of the university's self-driven vehicles under development. The validation of the concept is demonstrated also. These types of mechatronics design synergy, merged with advanced engineering pedagogy are essential for professionals with appropriate qualifications from universities to enter the industry.

This paper provides an overview of the performance measurement study conducted on the Budapest International TAR-1 Monopulse Secondary Surveillance Radar (MSSR) antenna. Secondary Surveillance Radar systems performance plays a crucial role in modern air traffic control, ensuring safe and efficient airspace management [1]. The focus of this research is to evaluate and analyze the antenna performance long time operational degradation, reliability, and test of our fault isolation methodology of the TAR-1 MSSR antenna deployed at the Budapest International Airport. The antenna rows fault isolation data collection is based on In Situ circular Near field set up connected to Microwave Holography calculations

Nowadays the consumer society applies a huge amount of energy in many fields including transportation sector. Internal combustion vehicles contribute substantially to the air pollution. An alternative solution for reducing energy consumption is replacing the internal combustion vehicles by electrical or hybrid vehicles. Today one of the biggest disadvantages of the electrical vehicles is the finite capacity of batteries. The research topic presented in this paper is the "Energy Harvesting", and development of energy recovery system for electrical vehicles which largely contributes in increasing the driving range. At the current phase of the research efficiency analysis of the heat energy recovery devices are investigated in real driving circumstances. Computer based mobile and wireless measurement system for the analysis was developed, tested and installed in a real vehicle. Driving tests were performed and analysed in different circumstances.

Napjainkban az egyik legnagyobb ütemben fejlődő biztonságtechnikai terület a biometrikus azonosítók világa. A kutatócsoport a biometrikus azonosításon belül a járásfelismerés szakterületével foglalkozik. A kutatócsoport megvalósított egy olyan komplex járásfelismerő rendszert NI LabVIEW fejlesztőkörnyezetben, amely képes univerzális kamera segítségével detektálni több egyidejű referenciapontot, és képes a gyűjtött mintákra előre definiált görbét illeszteni. A program továbbá képes a referenciagörbére és az aktuális görbére illesztett függvényeket meghatározott módon összehasonlítani és kiértékelni, hogy azonos-e két járáskép vagy sem. A programban mentési és visszatöltési funkció is helyett kapott, amely hozzájárul a referencia járáskép előállításához. A járásfelismerést megelőző lábelemző program a pontosság növelését szolgálja. A saját fejlesztésű járásfelismerő rendszert több személyen is teszteltük, mely tesztek közül idegen személy esetén egyszer sem mutatott egyezést a program.