This first-ever study demonstrates the applicability of a fiber Bragg grating (FBG) system for MR cardiac triggering of cardiovascular magnetic resonance at 3 Tesla. The unique patented system senses body movements caused by cardiac activity using a non-invasive ballistocardiography (BCG) sensor. The pilot research compares a novel FBG-based system with clinically used triggering systems based on electrocardiography (ECG) and pulse oximetry (POX). The pilot pre-clinical study was conducted on 8 subjects at a Siemens Prisma 3T MR Scanner. The study compares images from two basic cardiac sequences, TRUE FISP (Free Induction Decay Steady-State Precession) and PSIR (Phase Sensitive Inversion Recovery), using objective methods and subjective evaluation by clinical experts. The study presents original results that confirm the applicability of optical sensors in the field of cardiac triggering having a number of advantages in comparison to conventional solutions, such as no eddy current interference, ease of placement of the sensor on the patient's body, and senor reusability. The proposed FBG-based system achieves comparable results with the most frequently used and most accurate ECG-based and POX-based clinical systems. In terms of subjective evaluation by experts, the FBG system outperformed the POX-based system used in clinical practice.

The paper examines the development of a portable sensor strip with fiber optic Bragg grating for monitoring urban traffic density up to 80 kph. It contains a 2.5-m-long and a 2-cm-high sensor created from a combination of silicone addition rubber (bicomponent addition silicone rubber) and Bragg grating placed inside a carbon tube. The design of the portable sensor permits traffic density and cars crossings to be monitored and detected in a single lane. The functionality of the sensor was verified in real traffic; the results of this study are based on the detection of 1518 vehicles of different types and sizes. According to the measurements, the sensor is characterized by a high detection rate of 98.946%.

This paper presents a novelty approach to usage of the fiber-optic phase-based sensor in railway transportation. We designed and tested the real deployment of this sensor working on the principle of light interferences within optical fibers. The proposed construction of the sensor allowed to increase the sensitivity and thanks to this can be detected and calculated individual axles and wheels of tram vehicles. We performed long-time period measurements (April to September 2019) in diverse climatic conditions, including measurements of 642 tram passages (several different construction types) in real urban traffic. The detection accuracy level was slightly above 99.4 %.

This article describes the fiber-optic Bragg grating sensor which is encapsulated by using 3D print and polylactic acid material. This FBG sensor is designed for heart rate monitoring of the human body. In this article, we describe the complete process consisting of creating, encapsulating, and experimental verification of the sensor. This sensor we compared with the conventional ECG monitoring system. Measurement was performed with a group of 5 volunteers in the laboratory conditions. The measured data were compared by the Bland-Altman method.

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people’s everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.