Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.

The employability of photonics technology in the modern era’s highly demanding and sophisticated domain of aerospace and submarines has been an appealing challenge for the scientific communities. In this paper, we review our main results achieved so far on the use of optical fiber sensors for safety and security in innovative aerospace and submarine applications. In particular, recent results of in-field applications of optical fiber sensors in aircraft monitoring, from a weight and balance analysis to vehicle Structural Health Monitoring (SHM) and Landing Gear (LG) monitoring, are presented and discussed. Moreover, underwater fiber-optic hydrophones are presented from the design to marine application.

Diabetes is a chronic condition, and traditional monitoring methods are invasive, significantly reducing the quality of life of the patients. This study proposes the design of an innovative system based on a microcontroller that performs real-time ECG acquisition and evaluates the presence of diabetes using an Edge-AI solution. A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device. By applying quantization as an optimization technique, the model effectively balances memory usage and accuracy, achieving an accuracy of 89.52% with an average precision and recall of 0.91 and 0.90, respectively. These results were obtained with a minimal memory footprint of 347 kB flash and 23 kB RAM, showcasing the system’s suitability for wearable embedded devices. Furthermore, a custom PCB was developed to validate the system in a real-world scenario. The hardware integrates high-performance electronics with low power consumption, demonstrating the feasibility of deploying Edge-AI for non-invasive, real-time diabetes detection in resource-constrained environments. This design represents a significant step forward in improving the accessibility and practicality of diabetes monitoring.

In this manuscript, an optically passive fiber Bragg grating (FBG) interrogation system able to perform high-frequency measurement is proposed. The idea is mainly based on the use of an arrayed waveguide grating (AWG) device which is used to discriminate the fiber optic sensor (FOS) wavelength encoded response under test in function of its output channels. As made clear by the theoretical model studied in the proposed manuscript, the Bragg wavelength shift can be detected as in linear dependence with the proposed interrogation function which changes with the voltage produced by two (or more) adjacent AWG output channels. To prove the feasibility of the system, some experimental analyses are conducted with a custom electrical module characterized by high-speed and low-noise operational amplifiers. As static measurements, three FBGs with different full width at half maximum (FWHM) have been monitored under wide-range wavelength variation; while, as dynamic measurement, one FBG, glued onto a metal plate, in order to sense the vibration at low and high frequency, was detected. The output signals have been processed by a digital acquisition (DAQ) board and a graphical user interface (GUI). The presented work highlights the characteristics of the proposed idea as competitor among the entire class of interrogation systems currently used. This is because here, the main device, that is the AWG, is passive and reliable, without the need to use modulation signals, or moving parts, that affect the speed of the system. In addition, the innovative multi-channel detection algorithm allows the use of any type of FOS without the need to have a perfectly match of spectra. Moreover, it is also characterized by a high dynamic range without loss of sensitivity.

In order to complete this set of three companion papers, in this last, we focus our attention on environmental monitoring by taking advantage of photonic technologies. After reporting on some configurations useful for high precision agriculture, we explore the problems connected with soil water content measurement and landslide early warning. Then, we concentrate on a new generation of seismic sensors useful in both terrestrial and under water contests. Finally, we discuss a number of optical fiber sensors for use in radiation environments.

In this document is described the design, the characterization, the development, and pre-industrialization of a novel interrogation system for Fiber Bragg Grating (FBG) sensors capable to take advantage of the Internet of Things (IoT) technology. Due to its complexity and cost, classical interrogation systems limit the design and use of a sensor network based on fiber optic technology that are often replaced with less performant electronic sensor systems. The proposed system has the potential to overcome the limits of today’s commercial interrogators, converting the classic FBG interrogator into an IoT node, where each circuit is represented by a sensory node that continuously exchanges and publishes data on the cloud. Moreover, the proposed concept may be applied in harsh environments, still taking advantage of the fiber optic technology. The interrogator is composed of a passive optical device section; 4-layer electronic boards section (one board for each connected FBG in the external array), which conditions the optical signal to a compliant voltage at the downstream analog-to-digital converter (ADC). In the subsequent digital processing section, created inside an ARM based board, the conversions are then carried out through dedicated algorithms to match the voltage variation to the Bragg wavelength shift and the subsequent conversion into the relative physical quantity (temperature, deformation, or humidity) via a subsequent conversion algorithm. Finally, physical values, in real time, are published on a remotely accessible server. The proposed system was tested and validated comparing data produced by commercial interrogations. The tests were performed using an array composed of 6 (six) independent FBG sensors. It was tested through stability and on-field measurements, detecting one temperature compensated strain point, and four temperature monitoring points.