EssilorLuxottica (France)

Laser feedback interferometry (LFI) is a compact and highly sensitive technique for non-contact sensing, typically limited to configurations involving a single external target. Extending LFI to simultaneous multi-target measurement remains a significant challenge due to the increased complexity introduced by multiple optical feedback paths. In this work, we present a unified theoretical and computational framework for modeling and solving the excess phase equation in LFI systems with one or more external reflectors. The proposed approach accommodates both weak and moderate/strong optical feedback regimes, enabling robust analysis for diverse operating conditions. To validate the model, we perform experimental measurements of absolute distances to two consecutive targets using a single laser source. The results show a strong agreement between simulation and measured LFI signals across a broad range of optical feedback strengths.

Event-based cameras are becoming a popular solution for efficient, low-power eye tracking. Due to the sparse and asynchronous nature of event data, they require less processing power and offer latencies in the microsecond range. However, many existing solutions are limited to validation on powerful GPUs, with no deployment on real embedded devices. In this paper, we present EETnet, a convolutional neural network designed for eye tracking using purely event-based data, capable of running on microcontrollers with limited resources. Additionally, we outline a methodology to train, evaluate, and quantize the network using a public dataset. Finally, we propose two versions of the architecture: a classification model that detects the pupil on a grid superimposed on the original image, and a regression model that operates at the pixel level.

Edge devices have garnered significant attention for their ability to process data locally, providing low-latency, context-aware services without the need for extensive reliance on cloud computing. This capability is particularly crucial in context recognition, which enables dynamic adaptation to a user’s real-time environment. Applications range from health monitoring and augmented reality to smart assistance and social interaction analysis. Among edge devices, smart eyewear has emerged as a promising platform for context recognition due to its ability to unobtrusively capture rich, multi-modal sensor data. However, the deployment of context-aware systems on such devices presents unique challenges, including real-time processing, energy efficiency, sensor fusion, and noise management. This manuscript provides a comprehensive survey of context recognition in edge devices, with a specific emphasis on smart eyewear. It reviews the state-of-the-art sensors and applications for context inference. Furthermore, the paper discusses key challenges in achieving reliable, low-latency context recognition while addressing energy and computational constraints. By synthesizing advancements and identifying gaps, this work aims to guide the development of more robust and efficient solutions for context recognition in edge computing.

Historic water systems have become iconic features of cities like Venice and Amsterdam. The Navigli of Milan were constructed to channel groundwater for various purposes and a consequence was the desiccation of the surrounding marshy land. As the city faced new water challenges amid imminent water needs, its water identity was affected by the covering up of the historic water system. Climate change poses new challenges to preservation and planning in this historical water city. This article highlights the importance of history and water heritage for future interventions, by evoking the Navigli vistas that were once the cityscape of Milan. It discusses the current challenges of the hydrographic network, including more frequent and severe floods, and proposes the daylighting of the canals to inspire and adapt modern and future water systems to climate impact. The goal is to reclaim Milan’s identity as a “city of water” through a deliberate design methodology informed by the city’s history.

A study was conducted with 115 subjects who regularly drove at night to validate a refraction protocol for detecting refractive visual changes from daytime to nighttime conditions. Objective and subjective refractions were performed in both photopic and mesopic conditions, with a dark adaptation period before the mesopic subjective refraction. The results showed that in mesopic conditions, visual acuity decreased by 0.2 logMAR units on average (p < 0.01), and there was a myopic refractive shift of − 0.36 ± 0.20 D (p < 0.01). Most subjects (92.2%) exhibited a myopic refractive shift of at least 0.12 D. Compensation of refractive shift improved mesopic visual acuity by 0.06 logMAR on average (p < 0.01) and higher refractive shifts showed higher improvement. Night Rx was preferred by 82.1% of subjects with myopic refractive shift. Gender and age did not significantly affect the refractive shift, although myopes showed a higher shift compared to emmetropes (p < 0.01). The refractive shift remained stable over time when the time slot of the day did not change (p < 0.01). Night Rx protocol proved to be a robust and accurate method for identifying drivers with refractive changes when transitioning from photopic to mesopic conditions. The high prevalence and inter-individual variability of Rx shift highlight the need of customized refraction.