Building on my previous optical talk, this presentation delves deeper to look at some of the fundamental concepts underpinning the cost structure for running compl …

Through technology cycles, many networks face constraints around servicing their capacity needs between short haul metro locations in a lean and efficient manner. …

Modern test environments increasingly rely on automation of compute and network resources. The Continuous Integration/Continuous Development paradigm puts even mor …

Open Networking switches and software stacks are disrupting the industry and leading to a rapid increase in innovation, choice and flexibility in network deploymen …

Even in the age of big data, labeled data is a scarce resource in many machine learning use cases. Florian Wilhelm evaluates generative adversarial networks (GANs) when used to extract information from vehicle registrations under a varying amount of labeled data, compares the performance with supervised learning techniques, and demonstrates a significant improvement when using unlabeled data.

The desire to simplify network operations in complex multi-layer transport networks is driving commoditization of equipment along with intelligent software tools to control, configure and observe such …

Predicting driver intention and behavior is of great importance for the planning and decision-making processes of autonomous driving vehicles. Zhou Xing shares a methodology that can be used to build and train a predictive driver system, helping to learn on-road drivers' intentions, behaviors, associated risks, etc.