Abstract

IoT is defined by IEEE as: A network of networks where, typically, a massive number of objects, things, sensors or devices are connected through communications and information infrastructure to provide value-added services via intelligent data processing and management for different applications (e.g. smart cities, smart health, smart grid, smart home, smart transportation, and smart shopping). CISCO stated that IoT is the next evolution of the Internet, connecting the unconnected people, processes, data, and things in any business today. The number of Internet connected devices is expected to reach 25 billion in 2030 and the IoT revenue will reach $1.5 Trillion in 2030 growing from $500 Billion in 2019. IoT technologies evolve to support personal and industrial applications. The aim is to ascertain a pervasive, unified, and seamless experience to the end users. Yet, several challenges need to be addressed before. Specifically, the design of low-cost IoT terminals along with low-active power, and high end-to-end data rate is a major necessity.

Biography

Prof. Raed Mesleh joined German Jordanian university in Amman, Jordan, in February 2016 where he is currently the vice dean of the school of electrical engineering and information technology and an associate professor at the department of Electrical and Communication Engineering. He received his PhD in 2007 from Jacobs University in Bremen, Germany. From 2007 to 2010 he was a postdoctoral fellow at Jacobs University. He was with the Electrical Engineering Department at University of Tabuk in Saudi Arabia from 2010—2015. During that period, he holds the position of department chair and the director of research excellence and intellectual property units at the deanship of scientific research. He was a visiting scholar at Boston University, The University of Edinburgh and Herriot—Watt University. His main research interests are in wireless communication and optical wireless communication with particular focus on MIMO techniques, mmWave communication FSO and VLC. He is an inventor and coinventor of six patents. He invented Spatial Modulation technique during his PhD and he is the inventor of Quadrature Spatial Modulation and Trellis Coded Spatial Modulation. He published more than 200 journal and conference papers with an overall citation of more than 12500. He received distinguished researcher award at University in Tabuk in 2013 and at German Jordanian University in 2016 and 2019. In December 2016, he was awarded the Arab Scientific Creativity award from Arab Thought Foundation.

Abstract

Unlike previous generation networks that were mainly designed to meet the requirements of human-type communications, 5G networks enable the collection of data from the machines with the total number of devices expected to be about 26 billion in 2026 according to Ericsson Mobility Report. The next step in 6G systems is to enable a new spectrum of control applications based on these data, such as extended reality, remote surgery, autonomous vehicle platoons. The design of communication systems for control applications requires meeting the strict delay and reliability requirements of communication systems and addressing the semantics of the control systems. In the first part of this talk, ultra-reliable communication techniques, technologies and architectures are introduced by demonstrating the usage of extreme value theory, federated learning and reinforcement learning. In the second part of the talk, the fundamental paradigm shift from the Shannon paradigm is introduced. While Shannon paradigm aims to guarantee the correct reception of each single transmitted bit, irrespective of the meaning conveyed by transmitted bits, communication for control applications focuses on guaranteeing the success of the task execution, such as plant stability for automated production lines, detection accuracy in cooperative vehicle systems. Novel AI based resource allocation techniques for the joint design of control and communication systems are presented

Biography

Sinem Coleri is Professor in the department of Electrical and Electronics Engineering at Koc University. She is also the founding director of Wireless Networks Laboratory (WNL). Her research interests are in wireless communications and networking with applications in machine-to-machine communication, sensor networks and intelligent transportation systems. Dr. Coleri received the BS degree in electrical and electronics engineering from Bilkent University in 2000, the M.S. and Ph.D. degrees in electrical engineering and computer sciences from University of California Berkeley in 2002 and 2005. She worked as a research scientist in Wireless Sensor Networks Berkeley Lab under sponsorship of Pirelli and Telecom Italia from 2006 to 2009. Since September 2009, she has been a faculty member in the department of Electrical and Electronics Engineering at Koc University.

Dr. Coleri has more than 180 peer-reviewed publications with citations over 8900 (Google scholar profile). She has received numerous awards and recognitions, including TUBITAK (The Scientific and Technological Research Council of Turkey) Incentive Award and IEEE Vehicular Technology Society 2020 Neal Shepherd Memorial Best Propagation Paper Award in 2020, College of Engineering Outstanding Faculty Award at Koc University and IEEE Communications Letters Exemplary Editor Award as Area Editor in 2019, Turkish Academy of Sciences Distinguished Young Scientist (TUBA-GEBIP) in 2015.

Dr. Coleri has been Area Editor of IEEE Communications Letters and IEEE Open Journal of the Communications Society since 2019, Editor of IEEE Transactions on Communications since 2017 and Editor of IEEE Transactions on Vehicular Technology since 2016. She is an IEEE Fellow.

Abstract

Many applications are dealing with ever-increasing data. Not only the sheer amount of data has exponentially increased, the data modality and structure have seen become significantly richer. Examples include internet of things, sensor networks, but also biological networks, and the brain network, in particular.

Network science has emerged as the multidisciplinary field that processes network data using methods from graph theory, statistical mechanics, statistical inference, advanced visualization, and domain knowledge from applied fields. More specifically, graph signal processing (GSP) became a new research theme at the intersection between signal processing and graph theory, with a particular focus on processing graph signals that associate values to the nodes of the graph. Conventional operations on signals such as sampling and filtering can then be redesigned accounting for the graph backbone.

In this talk, GSP will be presented as a novel framework to represent network data, with a focus on the application to neuroimaging, in particular, state-of-the-art magnetic resonance imaging (MRI) that provides unprecedented opportunities to study brain structure (anatomy) and function (physiology). We will take the perspective of considering the structural connectome as derived from diffusion-weighted MRI to represent the major neural pathways in white matter. Next, the functional MRI data is considered as a time-dependent graph signal that is expressed on this anatomical background. The graph Laplacian can be used to measure smoothness of the graph signals and its eigendecomposition to define the graph-equivalent of the Fourier transform. The power spectral density of brain activity in the graph Fourier domain exhibits a power-law trend, similar to the spectral signature of natural images. Therefore, we define complementary low- and high-pass graph filters that separate graph signals in their smooth and non-smooth part, respectively. The nodal energy ratio between these signals is then interpreted as a measure of “coupling” between structure and function, termed the structural decoupling index (SDI). To provide statistical inference, we extend the well-known Fourier phase randomization method to generate surrogate data to the graph setting. The SDI reveals a behaviorally-relevant spatial gradient, where sensory regions tend to be more coupled with structure, and high-level cognitive ones less so. In addition, SDI maps are informative both for task decoding and individual fingerprinting pointing again toward the different involvement of unimodal and transmodal regions, respectively. Finally, recent work will highlight how the spatial resolution of GSP brain graphs can be increased to the voxel level, representing a few hundredth thousands of nodes, where explicit eigendecompositions becomes unfeasible.

Biography

Dimitri Van De Ville (F) received the M.S. degree in Computer Sciences and the Ph.D. degree in Computer Science Engineering from Ghent University, Belgium, in 1998, and 2002, respectively. He was a post-doctoral fellow (2002-2005) at the lab of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, before becoming group leader for the Signal Processing Unit at the University Hospital of Geneva, Switzerland, as part of the Centre d’Imagerie Biomédicale (CIBM). In 2009, he received a Swiss National Science Foundation professorship and since 2015 became Professor of Bioengineering at the École polytechnique fédérale de Lausanne (EPFL) (Institute of Bioengineering), jointly affiliated with the University of Geneva (Department of Radiology and Medical Informatics), Switzerland.

Dr. Van De Ville serves as Senior Editor, IEEE Transactions on Signal Processing (2019-present); Editor, SIAM Journal on Imaging Science (2018-present); Associate Editor, IEEE Transactions on Image Processing (2006 to 2009); Associate Editor, IEEE Signal Processing Letters (2004 to 2006); Chair, Bio Imaging and Signal Processing (BISP) Technical Committee (2012-2013); Founding Chair, EURASIP Biomedical Image & Signal Analytics SAT (2016-2018); Co-Chair, Biennial Wavelets & Sparsity series conferences, together with Y. Lu and M. Papadakis. He is the recipient of the Pfiz