Predicting failures in networking equipment using machine learning

This project is in collaboration with Ciena, an international company specialized in the supply of telecommunications networking equipment and software services. Electrical components produced by Ciena are important in many networking equipment such as routers or switches. Every year, a large number of components is produced. However, the production process may have some flaws, causing the production of faulty components.

Towards Next Generation Translation Management with an adaptation to specialized and low-resource domains

This project fits into the strategic goal to accelerate transition to digital technology in working with universities to research and analyse industry trends. The second phase will help the Ciena Documentation team test ideas and to implement them in the Component Content Management System (CCMS) and Translation Management System (TMS) in order to improve their

ability to meet end-users’ expectations.

Millimeter-Wave Photonic Component Packaging and Interconnect

With the increasing demand for data rates in modern high-speed links come new requirements for the simulation environments that are used for their design. With optical modulator now achieving beyond-100-GHz large-signal modulation bandwidth in hybrid silicon photonics, the main challenges that such systems are currently facing is the lack of efficient interconnects to interface with the outside world. These interconnects are designed and optimized using full-wave simulations.

Identifying and Modeling Families of Serial Tests

Ciena is a leading corporation supplying IT products. Before delivering any product, the company makes sure all products go through a set of tests which are recorded into log files. Operating in a crowded and highly competitive market, Ciena is continuously running after innovation for remaining highly competitive. Therefore, the company wants to increase their data analytical solutions capabilities by exploring the huge amount of log data that are continuously gathered.

PERFORMANCE DEGRADATION AND FAILURE DETECTION METHODS FOR OPTICAL NETWORKS BASED ON MACHINE LEARNING

Large network operators have to deal with dynamic network service changes, including scheduled or unscheduled outages, while keeping efficient service levels with different requirements. Currently available restoration techniques remain limited to after the fact detection of hard or catastrophic failures when the service has already been impacted. Machine learning technologies have been explored as a potential solution to enable proactive fault management and performance prediction.

Ciena OPn Innovation WP 1.1.6 - High Speed Low Power Transceiver

The intent of this project is to address the high-speed electronic portion of a silicon photonic transceiver solution that will explore new and innovative metro reach terabit optical modems. In total there are five projects that combine to create the solution.

WP1.1.4 - Digital Compatible Modeling of Analog/RF/Optical Circuits

The intent of this project is to address the analog and silicon photonic modelling portion of a silicon photonic transceiver solution that will explore new and innovative metro reach terabit optical modems. In total there are five projects that combine to create the solution.