Machine learning (ML) has disrupted a wide range of science and engineering disciplines in
recent years. ML applications in optical communications and networking are also gaining …

The ability to interpret information through automatic sensors is one of the most important
pillars of modern technology. In particular, the potential of biosensors has been used to …

The estimation of the quality of transmission (QoT) in optical systems with machine learning
(ML) has recently been the focus of a large body of research. We discuss the sources of …

Objective. The automatic discrimination between the coughing sounds produced by patients
with tuberculosis (TB) and those produced by patients with other lung ailments. Approach …

To support the development of intelligent optical networks, accurate modeling of the physical
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …

The optical signal-to-noise ratio (OSNR) and fiber nonlinearity are critical factors in
evaluating the performance of high-speed optical fiber communication systems. Recently …

Optical network automation requires accurate physical layer models, not only for
provisioning but also for real-time analysis. In particular, in-phase (I) and quadrature (Q) …

Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to
support elastic optical networks. In the past, they were normally developed and investigated …

Increased global traffic puts tough requirements not just on fiber communications links but
on the entire network. This manifests itself in multiple ways, including how to optimize …

In the optical networks, the dynamicity, the complexity and the heterogeneity have
dramatically increased owing to the deployment of advanced coherent techniques, and the …