Electrical impedance tomography (EIT) is a non-invasive imaging technique with promising applications in mobile and embedded healthcare systems. It reconstructs internal conductivity distributions from boundary voltage measurements but suffers from ill-posedness, sensitivity to noise and limited spatial resolution when solved using traditional iterative methods. This work proposes a hybrid reconstruction architecture that combines a Vision Transformer (ViT) with a Physics-Informed Neural Network (PINN). The transformer extracts global contextual features from voltage measurements, while the PINN enforces the quasistatic conduction equation with Neumann boundary conditions through a physics-informed loss function. Although trained and evaluated on synthetic data, the proposed PINN-ViT model demonstrates improved reconstruction accuracy and noise robustness over classical algorithms and purely data-driven networks. These results indicate its potential for future deployment in energy-efficient, real-time EIT systems for mobile healthcare applications.

The study investigates the effectiveness of a differential neural network architecture in ultrasonic tomography (UST) for industrial applications. The proposed model employs a dual-branch structure, where each branch independently processes identical input data before passing the outputs to a differential layer. This approach enhances the model's ability to capture residual components, improving the reconstruction of tomographic images. Experiments were conducted using a tomographic system with 16 transducers, generating training and validation datasets. Comparative analysis between a differential LSTM-based network and a standard LSTM model demonstrated that the differential architecture achieved superior reconstruction quality. The results confirm that this approach enhances accuracy, sharpness, and overall image clarity, making it a promising solution for improving UST image reconstruction.

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.

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique for estimating conductivity distributions, but its inverse problems are computationally demanding and noise-sensitive. This paper presents a deep learning framework integrating classification and regression to estimate conductivity maps efficiently. The model employs MobileNetV2-inspired residual blocks in a U-Net-based encoder-decoder structure. Regression is handled by weighting class probabilities with a predefined conductivity scale. Evaluated on simulated and real EIT data, the model accurately reconstructs conductivity maps, offering an efficient, real-time solution for biomedical and industrial imaging.

The article discusses research on a wearable medical diagnostic system using electrical impedance tomography. This system aims to diagnose long-term respiratory diseases, particularly COPD, ARDS, pneumothorax, pneumonia, bron-chospasm, and pulmonary hypertension. It seeks to reduce the number of tests needed for accurate diagnoses, thus saving time. The article compares two classification models for distinguishing between diseased and healthy individuals. This approach helps streamline the diagnostic process.

During the first stage of the project a computer vision system for human motion tracking was developed. For its implementation, a pair of cameras and a bicycle trainer are required. Human movement is monitored in real time by an effective algorithm that determines the key angles between the joints of a person exercising on a bike trainer. The following phase of the work focused on comparing the discussed system with a reference professional set, based on human motion sensors. The gathered data was then further analysed and compared, indicating that the accuracy of the developed system is fully satisfactory.

This paper presents a system that fuses radio tomography data with RGB camera information for enhanced multi-person detection and tracking in indoor environments. Experiments were conducted in an irregularly shaped room with four subjects. Due to its limited resolution, radio tomography initially represented all individuals as a single entity. By incorporating RGB camera data, we were able to accurately identify and track each person individually. Our findings underscore the significance of integrating data from both modalities for improved detection and tracking performance in indoor surveillance and security systems.

The aim of this work was to develop a proprietary device for real-time cough detection in audio recordings. Using models such as CNN, ResNet-50, and MobileNet, the system classifies cough sounds, enabling the early detection of potential infection cases, such as COVID-19. The device is intended for use in both private and public spaces, including medical facilities, nursing homes, and doctor's offices.

This paper presents novel diagnostic system for medicine, based on electrical impedance tomography (EIT). One of the primary functional features of this system is its ability to detect respiratory diseases with high accuracy, particularly focusing on conditions such as Chronic Obstructive Pulmonary Disease (COPD), Acute Respiratory Distress Syndrome (ARDS), Pneumothorax (PTX), Pulmonary Hypertension (PHTN), Pneumonia (PNA), and bronchospasm. A comparison of several classification models is presented, with the best-performing model achieving an accuracy rate of 98.22% in distinguishing between healthy and diseased patients.

The aim of the research was to develop a new device for monitoring and diagnosing functional disorders of the lower urinary tract, which will enable measurement of muscle tension (EMG) and electrical impedance tomography (EIT) with the possibility of electrostimulation of bladder reconstruction.

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.

In this study are presented the results of our ongoing research on an original concept for visualizing and tracking the state of the urinary bladder. We have developed a measuring device based on electrical impedance tomography (EIT). Using electrical current stimulation and measuring the resulting voltages on a patient's body surface, we can visualize the bladder's position and shape, allowing us to analyze its filling level. The project also involves the development of diagnostic methods for functional disorders of the lower urinary tract. In addition, the device will measure muscle tension by electromyography, with the possibility of incorporating electrostimulation therapy. This approach can be used to monitor and support the treatment of patients with various health conditions related to the urinary bladder.

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.

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.