In the paper, an innovative active control of a horizontal seat suspension system is discussed in context of realistic input vibration that occurs in the cabins of agricultural tractors. The vibration reduction system is represented by the state-space model and its numerical parameters are determined experimentally. For such a reliable mathematical representation of the system dynamics, the finite-time sliding mode controller is successfully designed for different spectral classes of the random excitation signal. A multi-objective control strategy is implemented in order to satisfy opposite requirements, that is, at the limited value of active force, a horizontal vibration of the seated human body is considerably reduced, but the suspension travel does not exceed unacceptable stroke of the system. Laboratory measurements of the seated human body have shown improved comfort of drivers under fore-and-aft vibrations.

Facial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday devices. This study focuses on the development and validation of a facial recognition system utilizing a Haar cascade classifier and the AdaBoost machine learning algorithm. The system leverages characteristic facial features—distinct, measurable attributes used to identify and differentiate faces within images. A biometric facial recognition system was implemented on a Raspberry Pi microcomputer, capable of detecting and identifying faces using a self-contained reference image database. Verification involved selecting the similarity threshold, a critical factor influencing the balance between accuracy, security, and user experience in biometric systems. Testing under various environmental conditions, facial expressions, and user demographics confirmed the system’s accuracy and efficiency, achieving an average recognition time of 10.5 s under different lighting conditions, such as daylight, artificial light, and low-light scenarios. It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. Threshold optimization tests with six male participants revealed a value that effectively balances accuracy and efficiency. While the system performed effectively under controlled conditions, challenges such as biometric similarities and vulnerabilities to spoofing with printed photos underscore the need for additional security measures, such as thermal imaging. Potential applications include access control, surveillance, and statistical data collection, highlighting the system’s versatility and relevance.