Data-driven models trained in an end-to-end manner can reliably detect events within optical signals. Unfortunately, event detection models poorly generalize when monitoring signals collected from devices with different acquisition procedures. We overcome this limitation by presenting a novel domain adaptation solution for event detection networks that enables inference across multiple types of devices. Rather than training a black-box detection network, we decouple event localization and classification tasks. Localization is performed by the Interval Proposal Algorithm (IPA), which leverages signal processing techniques to localize candidate events and derive context features. These events are then standardized and fed to a feature extractor to obtain morphological features. By combining domain-specific context features with domain-invariant morphological features, the classifier achieves good generalization capabilities through different domains. Our method can successfully detect events in OTDR traces achieving a mAP@0.5 of 75.33% on traces from the source domain and generalizing well (mAP@0.5 of 69.27%) on traces from the target domain, despite being trained solely from the source domain.

Despite the remarkable progress of fiber optics in communication, little attention has been devoted to the automatic detection of anomalies in optical spectra, i.e., poorly transmitted channels. This task is typically addressed by ad-hoc heuristics that fall short in spectra presenting heavy distortions caused by optical amplifiers during transmission. We propose a method based on a joint optimization procedure for estimating the major trends that characterize the spectrum, enabling the detection of anomalies even in the presence of few channels and heavy distortions. Our experiments have shown that the proposed method can successfully localize anomalies achieving more than 98% accuracy, outperforming all competitors.

Optical fiber links are customarily monitored by Optical Time Domain Reflectometer (OTDR), an optoelectronic instrument that measures the scattered or reflected light along the fiber and returns a signal, namely the OTDR trace . OTDR traces are typically analyzed by experts in laboratories or by hand-crafted algorithms running in embedded systems to localize critical events occurring along the fiber. In this work, we address the problem of automatically detecting optical events in OTDR traces through a deep learning model that can be deployed in embedded systems. In particular, we take inspiration from Faster R-CNN and present the first 1D object-detection neural network for OTDR traces. Thanks to an ad-hoc preprocessing pipeline for OTDR traces, we can also identify unknown events , namely events that are not represented in training data but that might indicate rare and unforeseen situations that need to be reported. The resulting network brings several advantages with respect to existing solutions, as these typically classify fixed-size windows of OTDR traces, thus are less accurate in the localization. Moreover, existing solutions do not report events that cannot be safely associated to any label in the training set. Our experiments, performed on real OTDR traces, show very promising performance, and can be directly executed on embedded OTDR devices.

The Optical Time Domain Reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber using the measure of scattered or reflected light from points along the fiber. The resulting signal, namely the OTDR trace, is commonly used to identify and localize possible critical events in the fiber. In this work we address the problem of automatically detecting optical events in OTDR traces, and present the first 1D object-detection neural network for optical trace analysis. Our approach takes inspiration from a successful object detection network in images, the Faster R-CNN, which we adapt to time series domain. The proposed network can both classify and localize many optical events along an input trace. Our results show that the proposed solution is more accurate than existing software currently analyzing OTDR traces, improving the mean average precision score by 27.43%. In contrast with existing solutions that are not able to distinguish many types of events, our algorithm can be trained in an end-to-end manner to detect potentially any type of optic event. Moreover, our network has been deployed on embedded OTDR devices to be executed in real-time.