On May 11–14, 2026, the research team associated with R&D Netrix S.A. and the WSEI University participated in the international SenSys 2026 conference, which took place in Saint-Malo, France.

SenSys 2026, the ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems, is one of the most important scientific events dedicated to embedded systems, intelligent sensor systems, the Internet of Things, cyber-physical measurement systems, edge computing, and the application of artificial intelligence in measurement and diagnostics. The 2026 edition was particularly significant because it represented a new, integrated format that brought together the research communities of the SenSys, IPSN, and IoTDI conferences.

Seven scientific papers were presented during the conference. Topics covered modern tomography systems, non-invasive medical diagnostics, in-building localization, ultrasonic flaw detection, digital twins of biotechnological processes, and image reconstruction methods using deep learning.

The first paper presented a wearable system for non-invasive monitoring of the lower urinary tract in children, combining ultrasonic reflectance tomography and electrical impedance tomography. This solution enables imaging of the bladder area and assessment of its filling level.

he second paper focused on a mobile, multi-channel infrared optical tomography system for monitoring reactive processes. The system utilizes fiber optic modules, FPGA control, and deep learning models to support near-real-time process analysis.

The third paper presented a precise indoor localization system based on Bluetooth Low Energy, angle-of-arrival estimation, and the MUSIC algorithm. The solution can support the tracking of assets, devices, and objects in indoor environments.

The fourth paper presented a dual-mode ultrasonic flaw detection system using multi-angle beamforming. The system enables both rapid material scanning and more accurate defect reconstruction with improved imaging quality.

The fifth paper focused on the VestiCare system for continuous cardiopulmonary diagnostics, integrating EIT lung, ECG, and user activity data. The use of diffusion models improves the quality of the reconstruction and increases resistance to motion artifacts.

The sixth paper presented the application of EIT, ResNet-50 networks, and Monod kinetics in bioprocess control. The solution aligns with the digital twin concept and enables real-time monitoring of fermentation processes.

The seventh paper presented an EIT reconstruction method based on physically consistent iterative learning and a graph U-Net. The proposed approach improves anomaly localization, boundary sharpness, and reconstruction accuracy compared to classical methods.

The SenSys 2026 conference was an important forum for exchanging knowledge in the fields of embedded AI, sensor systems, IoT, CPS, and intelligent measurement data processing. The team’s participation in the event enabled the presentation of the latest research results internationally and confirmed the importance of ongoing work in the field of modern measurement, diagnostic, and industrial technologies.

The presented work points to the further development of solutions integrating measurement equipment, physical modeling, image reconstruction, machine learning, and embedded systems. This direction is crucial for the development of non-invasive diagnostics, process control, health monitoring, industrial automation, and next-generation intelligent sensor systems.