The paper presents an analysis of Remotely Piloted Aircraft Systems (RPAS) used for monitoring the crops vegetation status. The study focuses on two types of RPAS, namely the flying wing and the multi-copter. The following criteria were taken into account: technical characteristics, power consumption, flight autonomy, flight conditions, costs, data acquisition systems used for monitoring, crops area and so on. Based on this analysis, advantages and disadvantages are emphasized offering a useful tool for choosing the proper solution according to the specific application conditions.

The widespread availability of artificial intelligence (AI) tools has facilitated the development of complex, high-performance applications across a broad range of domains. Among these, processes related to the surveillance and assisted verification of indoor environments have garnered significant interest. This paper presents the implementation, testing, and validation of an autonomous mobile platform designed for the intelligent and flexible inspection of such spaces. The artificial vision system, the main component on which the study focuses, was built using a Raspberry Pi 5 development module supplemented by a Raspberry Pi AI Kit to enable hardware acceleration for image recognition tasks using AI techniques. Some of the most recognized neural network models were evaluated in line with the application’s specific requirements. Utilizing transfer learning techniques, these models were further developed and trained with additional image datasets tailored to the inspection tasks. The performance of these networks was then tested and validated on new images, facilitating the selection of the model with the best results. The platform’s flexibility was ensured by mounting the artificial vision system on a mobile structure capable of autonomously navigating indoor environments and identifying inspection points, markers, and required objects. The platform could generate reports, make decisions based on the detected conditions, and be easily reconfigured for alternative inspection tasks. Finally, the intelligent and flexible inspection system was tested and validated for its deep learning-based vision system performance.

The walking cycle is biomechanically considered a fundamental unit of biomedical analysis, a neuromuscular-controlled repetitive process and an energy-optimized system. As defined in the literature, the walking cycle is represented by the interval between two successive contacts of the same foot with the ground, having two main phases of stance and swing. Current theoretical and experimental research seeks to build complex models of analysis of the parameters of the walking cycle to highlight different behaviors at the level of the human body (biomechanical, energetic, sensory, etc.). In this paper, the authors develop a model of theoretical analysis by simulating the walking cycle in Matlab code to highlight energy levels. The theoretical model is proposed to be applied to a sample of subjects with simulations of locomotor pathologies to confirm hypotheses.