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.

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.

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.