On February 23rd, 2017 at 10.30 am, two seminars will take place at DEIB Conference Room as a new appointment of the CommTech Talks.
"Graph Signal Processing" - 10.30 am
In many applications, from sensor to social networks, transportation systems, gene regulatory networks and so on, the signals of interest are defined over the vertices of a graph. Over the last few years, a series of papers produced a significant advancement in the development of tools for the analysis of signals defined over a graph, or graph signals for short. One of the unique features in graph signal processing (GSP) is that the analysis tools come to depend on the graph topology. A central role is of course played by spectral analysis of graph signals, which passes through the introduction of the Graph Fourier Transform (GFT). In this talk, we will review the fundamentals of GSP and illustrate some of the fundamental properties, such as uncertainty principle and sampling theory. We will show how GSP encompasses standard DSP as a very particular case and at the same time we emphasize the main differences.
Then, we proceed to more advanced topics, such as graph learning and adaptive graph signal processing.
"Mobile Edge Computing: Joint optimization of communication and computation resources" - 11.30 am
Mobile Edge Computing (MEC) has been identified as a key enabler of low latency and energy efficient proximity access to information technology (IT) services from mobile users. The goal of MEC is to bring cloud-computing capabilities, including computing and caching, at the edge of the mobile network, within the Radio Access Network (RAN), in close proximity to mobile subscribers. This is obtained by empowering radio access points (AP) with additional storage and computation capabilities and coordinating the work of nearby cloud-enhanced AP's. In this talk, we focus on this new vision, where radio access, computing and storage are seen as subsystems of a truly pervasive (liquid) computer that follows the mobile user. In this context, resources, be virtual machines, cache or radio links, are deployed when and where they are needed.
Then, we concentrate on the joint optimization of communication and computation resources. Finally, we conclude with the merge of MEC with millimeter-wave communications.
Sergio Barbarossa received his MS and Ph.D. degree from Sapienza University of Rome, where he is now a Full Professor. He has held visiting positions at the Environmental Research Institute of Michigan (’88), Univ. of Virginia (’95, ‘97), and Univ. of Minnesota (’99). He is an IEEE Fellow, EURASIP Fellow, and he served as IEEE Distinguished Lecturer. He received the IEEE Best Paper Awards from the IEEE Signal Processing Society for the years 2000 and 2014. He received the Technical Achievements Award from the European Association for Signal Processing (EURASIP). He is currently a member of the editorial board of the IEEE Transactions on Signal and Information Processing over Networks. He has been the scientific coordinator of several EU projects on wireless sensor networks, small cell networks, and distributed mobile cloud computing.
He is now the technical manager of a H2020 Europe/Japan project on 5G. His current research interests are in the area of graph signal processing, algebraic topology, machine learning, distributed optimization, millimeter wave communications and mobile edge computing.