Ali C Begen is currently a Computer Science Professor at Ozyegin University and a technical consultant in Comcast's Advanced Technology and Standards Group.
This lecture will describe the "Robotics for Equality and Democracy" STEM and educational robotics program deployed in Tunisian public schools as a partnership between IEEE Tunisia Chapter SIGHT and the academic inspection for primary education of Dar Chaabane II, Nabeul, Tunisia Republic.
This presentation will review cutting-edge concepts and technologies for the development of ultrafast fibre lasers, including passive mode-locking mechanisms, novel generation regimes, and expansion of operational wavelength ranges.
One of the key drivers for next-generation mobile communications is the support of the Internet of Things (IoT), with billions of objects connected to the Internet and very low latency. This Distinguished Lecture investigates the integration of satellite technology and IoT, considering different architectures and sensor technologies as NB-IoT, LoRa/LoRaWAN, and SigFox.
A new generation of services and applications is emerging that require lower latency, better reliability, large connections, and increased energy efficiency. This Distinguished Lecture sheds light on different aspects of Ultra-Reliable Low-Latency Communication and Time-Sensitive Networking through the use of V2X as an example.
This webinar describes how to use analytics as a way to spot expansion opportunities in a manner that makes the process easy to understand.
This Distinguished Lecture will introduce extreme value theory-based channel modelling and communication techniques detecting rare events in ultra-reliable communication together with federated learning techniques for channel estimation.
In this workshop, expert panel members will be discussing on cutting edge research and development for enabling future transport and mobility systems.
This presentation unpacks the contradictions in urban robotics, suggests how to avoid doing harm to humans, and encourages critical discussion about which version of the intelligent urban future we are building.
COVID-19 is a pandemic disease that caused more than 6.59 million deaths as of 30th October 2022. Traditional manual labelling of CT-based scans is tedious. To solve the problem, intelligent automation methods, such as transfer learning, graph neural networks, attention neural networks and weakly supervised networks can be employed.