This paper presents the development of an advanced non-invasive inspection system utilizing beamforming technology, specifically designed for detecting and characterizing defects across various materials. The device employs sophisticated algorithms to enhance both the precision and resolution of non-invasive examinations. Additionally, the system incorporates 3D reconstruction capabilities, providing improved visualization of detected anomalies. A comparative analysis with current market solutions further validates the superior diagnostic performance and potential applications of this innovative technology in sectors such as aerospace engineering, construction, and manufacturing, where accurate defect detection is paramount.

This paper introduces the development of a beamforming defectoscope tailored for advanced non-invasive techniques. The device employs beamforming technology to enhance the detection and characterization of defects in a range of materials. Through the use of sophisticated signal processing algorithms, the defectoscope increases both the resolution and accuracy of non-invasive inspections. The study underscores the superior performance of the beamforming method when compared to traditional approaches, demonstrating notable improvements in diagnostic capabilities. This cutting-edge tool holds promising applications across multiple industries, such as aerospace, civil engineering, and manufacturing, where accurate defect detection is essential.

Progress on the development of a wearable ultrasonic bladder monitoring device is reported. The device is intended to help in the diagnosis of urinary incontinence of both young and elderly patients. We present all the components of the device - the textile band, electronics, as well as the early results of reconstruction algorithms.

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.