Artificial Intelligence (AI) refers to the development of computer systems or algorithms that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, understanding natural language, speech recognition, visual perception, and decision-making. AI aims to create machines capable of mimicking cognitive functions associated with human intelligence, allowing them to adapt, learn from experience, and execute tasks autonomously.


Citizen Science for the Sea with Information Technologies: An Open Platform for Gathering Marine Data and Marine Litter Detection from Leisure Boat Instruments

Data crowdsourcing is an increasingly pervasive and lifestyle-changing technology due to the flywheel effect that results from the interaction between the Internet of Things and Cloud Computing. This paper presents the Citizen Science for the Sea with Information Technologies (C4Sea-IT) framework. It is an open platform for gathering marine data from leisure boat instruments. C4Sea-IT aims to provide a coastal marine data gathering, moving, processing, exchange, and sharing platform using the existing navigation instruments and sensors for today’s leisure and professional vessels. In this work, a use case for the detection and tracking of marine litter is shown. The final goal is weather/ocean forecasts argumentation with Artificial Intelligence prediction models trained with crowdsourced data.

AI-Driven Applications over Telecom Networks by Distributed Fiber Optic Sensing Technologies

By employing distributed fiber optic sensing (DFOS) technologies, field deployed fiber cables can be utilized as not only communication media for data transmissions but also sensing media for continuously monitoring of the physical phenomenon along the entire route. The fiber can be used to monitor ambient environment along the route covering a wide geographic area. With help of artificial intelligence and machine learning (AI/ML) technologies on information processing, many applications can be developed over telecom networks. We review the recent field results and demonstrate how DFOS can work with existing communication channels and provide holistic view of road traffic monitoring included vehicle counts and average vehicle speeds. A long-term wide-area road traffic monitoring system is an efficient way of gathering seasonal vehicle activities which can be applied in future smart city applications. Additionally, DFOS also offers cable cut prevention functions such as cable self-protection and cable cut threat assessment. Detection and localization of abnormal events and evaluating the threat to the cable are realized to protect telecom facilities.

Employing Telecom Fiber Cables as Sensing Media for Road Traffic Applications

Distributed fiber optic sensing systems (DFOS) allow deployed fiber cables to be sensing media, not only dedicated function of data transmission. The fiber cable can monitor the ambient environment over wide area for many applications. We review recent field trial results, and show how artificial intelligence (AI) can help on the application of road traffic monitoring. The results show that fiber sensing can monitor the periodic traffic changes in hourly, daily, weekly and seasonal.

Neuron-Network-based Nonlinearity Compensation Algorithm

A simplified, system-agnostic NLC algorithm based on a neuron network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6dB Q improvement after 2800km SMF transmission using 32Gbaud DP-16QAM.

Evolution from 8QAM live traffic to PCS 64-QAM with Neural-Network Based Nonlinearity Compensation on 11000 km Open Subsea Cable

We report on the evolution of the longest segment of FASTER cable at 11,017 km, with 8QAM transponders at 4b/s/Hz spectral efficiency (SE) in service. With offline testing, 6 b/s/Hz is further demonstrated using probabilistically shaped 64QAM, and a novel, low complexity nonlinearity compensation technique based on generating a black-box model of the transmission by training an artificial neural network, resulting in the largest SE-distance product 66,102 b/s/Hz-km over live-traffic carrying cable.