Bioimpedance spectrum (BIS) measurements are highly appreciated in in vivo studies. This non-destructive method, supported by simple and efficient instrumentation, is widely used in clinical applications. The multi-frequency approach allows for the efficient extraction of the most information from the measured data. However, low-frequency implementations are still unexploited in the development of the technique. A self-developed BIS measurement technology is considered the pioneering approach for low (<5 kHz) and ultra-low (<100 Hz) frequency range studies. In this paper, the robustness of ultra-low frequency measurements in the prototypes is examined using specially constructed physical models and a dedicated neural network-based software. The physical models were designed to model the dispersion mainly in the ultra-low frequency range. The first set of models was used in the training of the software environment, while the second set allowed a complete verification of the technology. Further, the Hilbert transformation was employed to adjust the imaginary components of complex signals and for phase determination. The findings showed that the prototypes are capable of efficient and robust data acquisition, regardless of the applied frequency range, minimizing the impact of measurement errors. Consequently, in in vivo applications, these prototypes minimize the variance of the measurement results, allowing the resulting BIS data to provide a maximum representation of biological phenomena.

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged.