This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.

This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.

W artykule przedstawiono badania nad efektywnością badania infiltracji w wałach przeciwpowodziowych za pomocą elektrycznej tomografii impedancyjnej. Zweryfikowano przydatność algorytmu, a także sprawdzono najlepsze wyniki. W celu przetestowania uzyskanych w trakcie badań algorytmów rekonstrukcyjnych wygenerowano obrazy na podstawie pomiarów symulacyjnych. W tym celu zbudowano specjalny model wału przeciwpowodziowego. W celu uzyskania informacji zwrotnej o stopniu przesiąkania w wale przeciwpowodziowym zastosowano predykcję za pomocą metody Random Forest (Metoda drzew losowych do identyfikacji przesiąkania w wałach przeciwpowodziowych).

The article describes the progress of construction and research works on ultrasound tomography. The devices allow for non-invasive measurements of various objects using ultrasonic low and high-frequency transducers. The first constructions were made in a dispersed system with active measurement probes using 40 kHz converters. The next constructions were centralized into one measurement system where the measurement probes were connected separately. As a result, the measuring range of the supported ultrasonic transducers with 300 kHz, 400 kHz and 1 MHz has been extended. Apart from transmission and reflection tomography, the latest designs allow for controlling the ultrasound beam (beamforming) and support transducers up to 5 MHz.