Alessandro Fantoni

Modern airports are complex, dynamic infrastructures integrating terminals, gates, commercial areas, and operational assets, including passengers, Autonomous Guided Vehicles (AGVs), baggage carts, and ground support equipment. Ensuring safe, efficient, and resilient operations requires real-time coordination among these heterogeneous agents. This work presents a dual-layer AI-driven airport management system leveraging Visible Light Communication (VLC) and Multi-Agent Reinforcement Learning (MARL) to enhance operational efficiency, safety, and situational awareness in critical airport environments. The architecture separates airport operations into two functional layers: an outdoor layer for AGV traffic, aircraft taxiing, and gate assignments, and an indoor layer for passenger circulation, baggage handling, and commercial services. Vertical segmentation minimizes conflict zones between humans and autonomous systems, improving safety and reducing congestion. Autonomous Deep Reinforcement Learning (DRL) agents are deployed across both layers, making decentralized, real-time decisions based on local and neighboring traffic data. VLC-enabled luminaires transmit high-resolution spatial and navigational information using On-Off Keying (OOK) modulation and tetrachromatic LEDs, received by hybrid amorphous SiC optical sensors in a mesh topology. This enables precise localization and coordination of AGVs and pedestrians, independent of GPS. Adaptive queue/request/response mechanisms prioritize high-demand zones, resolve conflicts, and optimize flow management. Wayfinding algorithms dynamically route passengers to gates, check-in counters, baggage claim, and lounges, enhancing operational efficiency and passenger experience. Simulation and experimental results demonstrate reliable VLC communication, accurate localization, improved AGV throughput, reduced passenger wait times, and more efficient aircraft turnaround under cooperative MARL strategies. By embedding AI-driven agents within a VLC-enabled optical network, the framework provides a scalable, resilient, and intelligent solution for airport operations. This study highlights the potential of integrating multi-agent reinforcement learning with photonic technologies to support critical infrastructure management, improve safety, and enable adaptive, data-driven decision-making in increasingly automated airport environments.

In this work, we present the design and numerical validation of a reconfigurable electro-optic photonic integrated circuit architecture for dynamic 1 × 4 optical routing, based on electro-optic phase shifters and a chain of cascaded multimode interference structures, and implemented in an amorphous silicon platform. By exploiting recent developments in symmetry-engineered silicon photonics to enable phase control in a platform compatible with complementary metal-oxide-semiconductor fabrication requirements, this approach addresses the increasing demand for ultra-fast, compact and energy efficient reconfigurable photonic integrated circuits. The photonic circuit design bases its functionality on self-imaging theory and has been optimized through simulations implementing the beam propagation method, while optical performance and electro-optical behavior have been validated through finite difference time-domain simulations and multiphysics modeling. The numerical results obtained confirm the operational performance of the individual building blocks and demonstrate the proposed architecture as being able to perform as a reconfigurable electro-optic platform and provide the dynamic 1 × 4 optical routing. Hence, this architecture provides a scalable platform for reconfigurable silicon photonics, complementary metal-oxide-semiconductor compatible, and the fabrication-ready layout establishes a practical path to experimental validation and future reconfigurable photonic integrated circuits.

Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the forearm are processed via amplification, filtering, and digital analysis to enable accurate detection of muscle activity. The system achieves independent and simultaneous actuation of five fingers using a tendon-driven, servo-actuated mechanism in a lightweight ABS structure. Experimental evaluation demonstrates finger actuation delays ranging from 314 ms to 650 ms, maximum holding strengths between 1.75 N and 4.07 N, and minimum gripping distances between 22 mm and 49 mm across all five fingers, with peak motor currents remaining below 0.7 A. Results validate consistent muscle activity detection, successful execution of individual and combined finger movements, and the robustness of the proposed design.

Programmable photonic integrated circuits implement optical switching and processing by interconnecting reconfigurable 2 × 2 cells in mesh topologies. Directional couplers are widely used in these cells, often combined with phase-shifting mechanisms to enable tunability. However, conventional directional couplers in dense meshes typically require submicron gaps and tight etching tolerances, which increase sensitivity to fabrication variations and can introduce excess loss and variability. In addition, interconnected waveguides (e.g., S-bends and crossings) increase layout complexity, footprint, and bending-related penalties, while thermo-optic control may introduce power consumption and thermal crosstalk. Here, we propose a shallow-rib strip directional coupler in hydrogenated amorphous silicon (a-Si:H) for 1 µm × 1 µm multimode waveguides. The proposed geometry enables efficient coupling for waveguide separations ≥ 1 µm by shifting the coupling control from the lateral gap to the slab height, allowing smoother transitions and a relaxed fabrication flow. The analysis combines coupled-mode theory and beam propagation method simulations. As an application example, the layout of a 4 × 4 thermo-optically reconfigurable switching matrix is designed and simulated using 2 × 2 shallow-rib strip coupler cells.

The development of photonic-integrated circuits (PICs) for data communication, sensing, and quantum computing is hindered by the high complexity and cost of traditional fabrication methods, which rely on expensive equipment, limiting accessibility for research and prototyping. This study introduces a Direct Laser Writing (DLW) system designed as a low-cost alternative, utilizing an XY platform for precise substrate movement and an optical system comprising a collimator and lens to focus the laser beam. Operating on a single layer, the system employs SU-8 photoresist to fabricate polymer-based structures on substrates such as ITO-covered glass. Preparation involves thorough cleaning, spin coating with photoresist, and pre- and post-baking to ensure material stability. This approach reduces dependence on costly infrastructure, making it suitable for academic settings and enabling rapid prototyping. A user interface and custom slicer process standard .dxf files into executable commands, enhancing operational flexibility. Experimental results demonstrate a resolution of 10 µm, with successful patterning of structures, including diffraction grids, waveguides, and multimode interference devices. This system aims to transform PIC prototype fabrication into a cost-effective, accessible process.

The m-lines spectroscopy is a precise, non-destructive and contactless method, and one of its main applications is the determination of the geometric-optical parameters of a thin film deposited over a substrate, namely the refractive index and the thickness of the film under analysis. The method was first described in 1969 with the seminal work of Tien, more than half a century ago, and, since then, it has been reported in the literature that at least two modal indices of the same polarization are required to unequivocally determine a given film’s refractive index and thickness. This constraint imposes a limit on the waveguide’s thickness, for it leaves out the possibility of determining the geometric-optical parameters of all films where only single-mode propagation is feasible. In this work, we propose and validate a strategy that extends the applicability of the method to single-mode operation, enlarging its operational thickness detection range. Moreover, the results obtained demonstrate that restricting the parameter extraction to fundamental modes leads to a measurable increase in precision. This improvement is attributed to the lower susceptibility to experimental uncertainties, lower sensitivity to surface roughness and nearby structures, and higher confinement that characterize fundamental modes as opposed to higher-order ones.

The development of photonic integrated circuits (PICs) for data communication, sensing, and quantum computing is hindered by the high complexity and cost of traditional fabrication methods, which rely on expensive equipment, limiting accessibility for research and prototyping. This study introduces a Direct Laser Writing (DLW) system designed as a low-cost alternative, utilizing an XY platform for precise substrate movement and an optical system comprising a collimator and lens to focus the laser beam. Operating on a single layer, the system employs SU-8 photoresist to fabricate polymer based structures on substrates such as ITO covered glass. Preparation involves thorough cleaning, spin coating with photoresist, and pre and post-baking to ensure material stability. This approach reduces dependence on costly infrastructure, making it suitable for academic settings and enabling rapid prototyping. A user interface and custom slicer process standard .dxf files into executable commands, enhancing operational flexibility. Experimental results demonstrate a resolution of 10 µm, with successful patterning of structures, including diffraction grids, waveguides, and multimode interference devices. This system aims to transform PIC prototype fabrication into a cost-effective, accessible process.

This study represents a preliminary proof of concept intended to demonstrate the feasibility of using a single-point LiDAR sensor for wearable gait analysis. The study presents a low-cost wearable sensor system that integrates a single-point LiDAR module and IoT connectivity to assess Gait Cycle Duration (GCD) and gait symmetry in real time. The device is positioned on the medial side of the calf to detect the contralateral limb crossing—used as a proxy for mid-stance—enabling the computation of GCD for both limbs and the derivation of the Symmetry Ratio and Symmetry Index. This was conducted under simulated walking at three cadences (slow, normal and fast). GCD estimated by the sensor was compared against the visual annotation with Kinovea^®, showing reasonable agreement, with most cycle-wise relative differences below approximately 13% and both methods capturing similar symmetry trends. The wearable system operated reliably across different speeds, with an estimated materials cost of under 100 € and wireless data streaming to a cloud dashboard for real-time visualization. Although the validation is preliminary and limited to a single healthy participant and a video-based reference, the results support the feasibility of a photonic, IoT-based approach for portable and objective gait assessment, motivating future studies with larger and clinical cohorts and gold-standard references to quantify accuracy, repeatability and clinical utility.