In this talk, I will speak on a novel antenna technology, referred to as fluid antenna, that adopts a software-controlled, position-flexible antenna to operate on the best signal envelope within a given space. This talk presents some early results on fluid antenna systems, which shows its potential for improving wireless communication performance.

This talk will be focused on Integrated Photonics technology, which demands strong and robust confinement of light in order to attain the maximum possible integration of photonics devices on a single chip. Keeping this in mind, three applications based on LEMAC’s recent developments will be presented in this talk.

This thesis consists of three main parts. In the first part, we discuss first-order stationary mean-field games (MFGs) on networks. In the second part, we discuss the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary MFGs. First, we build the Wardrop model on networks. Second, we show how to convert the MFG model into a Wardrop model. Next, we recover the MFG solution from the Wardrop solution. Finally, we study the calibration of MFGs with Wardrop travel cost problems. In the third part, we explain the algorithm for solving the algebraic system associated with the MFG numerically, then, we present some examples and numerical results.

Metaverse is the next-generation Internet after the web and the mobile network revolutions, in which humans (acting as digital avatars) can interact with other people and software applications in a three-dimensional (3D) virtual world. In this presentation, we first briefly introduce major concepts of Metaverse and the virtual service management. Then, we discuss applications of game theory in the virtual service management.

With the transition from traditional to LED light sources, the lighting industry has changed completely. Lighting systems have become intelligent and connected.

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

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. The 5G technology will support the realization of smart cities, smart environments, and big data applications.

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konecny et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.

In this talk, Dr. Sarkar will introduce the history and concepts of power semiconductor devices. A special emphasis will be laid on understanding the power semiconductor device-design keeping the applications in perspective. Apart from covering traditional power electronic devices, Dr. Sarkar will also introduce emerging (ultra)wide power semiconductor devices that are expected to occupy a large market share, and their challenges. The core concepts will be thoroughly discussed with the help of examples and interactive discussions.

In this talk, Dr. Sarkar will introduce the history and concepts of power semiconductor devices. A special emphasis will be laid on understanding the power semiconductor device-design keeping the applications in perspective. Apart from covering traditional power electronic devices, Dr. Sarkar will also introduce emerging (ultra)wide power semiconductor devices that are expected to occupy a large market share, and their challenges. The core concepts will be thoroughly discussed with the help of examples and interactive discussions.

Reconfigurable intelligent surfaces (RISs) are now considered among the key enabling technologies for 6G systems. Empowered by the recent advances in meta-material, RISs are equipped with a large number of low-cost passive elements that allow for the modification of the radio waves in that they reflect, refract, and scatter radio signals in a controllable fashion to counteract the destructive effect of multipath fading. These features can be leveraged to transform the propagation environment into a smart space that can be programmable for the benefit of the communication application. Throughout this proposal, we study RIS-assisted systems from different perspectives, including performance analysis, system optimization, and channel estimation, to analyze and enhance the operation of such systems in different setups. Some possible future research directions to be considered are also highlighted.

In this talk, I will first explain low-complexity, non-iterative, and near-optimal RIS (eflecting intelligent surface) joint active and passive beamformer designs for both narrowband and wideband assuming full channel information. Then, I will explain several practical channel estimation techniques for RIS systems. Lastly, I will introduce WiThRay, a versatile 3D wireless communication simulator based on ray-tracing developed by our lab, that can be used to evaluate different RIS techniques for various environments.

Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and reduce their execution time. Sometimes, even forming the dense matrix can be a bottleneck – in computation or storage.

The Internet of Underwater Things technology can be established using various underwater wireless communications technologies, including acoustic, radio frequency (RF), magnetic induction, and optical. Each of these technologies has its pros and cons; for example, acoustic technology reaches longer distances but is bandwidth limited, while underwater optical wireless communications (UOWCs) can support higher data rates at the cost of short ranges. Besides establishing communication links using these technologies, geographic data tagging of the underwater IoT nodes is also crucial and challenging for several applications. Hence, this talk will provide an overview of the rapidly evolving positioning systems and algorithms for different underwater technologies for location estimation of the underwater IoT nodes and their applications.

In this seminar, we first give background and present the framework of the modern SemCom. Then, we discuss the general semantic extraction methods and semantic metrics used in the existing studies.

This talk will present our recent research works related to the application of data science in the environment, healthcare, and social media.

Unmanned aerial vehicles (UAV) have gained great popularity in recent years due to their decreasing cost and increasing functionality. In wireless communications, they are mainly used as aerial platforms to enable information delivery or power transfer. In this talk, applications of UAV in wireless communications are discussed.

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

We aim to depict a couple of digital technologies-based applications that tackle population health as well efficient mobility for fishermen and breeders. For sustainable livestock farming in Senegal,  transhumance is still performed and plays a very important role in the regulation of macroeconomic balances and social cohesion. According to climate change and fishing opportunities between Senegal, other countries fishes are far away from Senegalese coastal.

In this talk, a distributed beam tracking scheme for millimeter-wave unmanned aerial vehicle (UAV)- assisted communication systems is presented. In the considered systems, aerial node base (aNB) connects the ground vehicles (GV) through directive beams to mitigate the path loss. However, line-of-sight (LoS) blockage may result in the link disconnection, which leads to the corruption of observations. To solve this issue, we extend the one-to-one beam tracking into the multi-node cooperative beam tracking scheme.

In this talk, we present a class of stochastic differential games that can incorporate the distribution of the variables of interest (e.g., the system states and/or decision-makers' actions) into the strategic-interaction problem. We motivate the use of this type of differential games in networked large-scale applications that cover a high variety of engineering systems. In particular, we focus on the crowd evacuation problem. First, we only consider local aggregated congestion terms that penalize the magnitude of the decision-makers’ strategies allowing us to avoid the formation of congestion. Second, we consider both local and global aggregated congestion terms to perform crowd aversion during the evacuation procedure. We present some numerical results and few future directions, e.g., the case where decision-makers do not have prior knowledge about the geometry of the structure to be evacuated neither the existing obstacles.

Critical Infrastructures (CIs) are the cornerstone Economics and Society rely upon. In this talk, we will start with surveying some of the threats different CIs are subject to, highlighting some research problems and related results.

In this talk, we will consider an alternative approach for future networks: serve the users by antennas that are distributed over the coverage area. By shifting from a world where base stations are surrounded by users, to a world where each user is surrounded by antennas, we can deliver almost uniformly good data rates wherever the user is. This concept has recently been called Cell-Free Massive MIMO. A core practical challenge is to deploy such a distributed MIMO array affordably. One potential approach is to use radio stripes, which are cables with integrated antennas. We will take a close look at how these can be implemented and recent results on how communications algorithms can be designed to exploit their special characteristics.

We investigate privacy aspects of tests for symmetry equivalence null hypotheses. Specifically, we consider weighted L2 type tests as well as chi-squared type tests for multivariate symmetry based on the characteristic function, and their privacy properties are specifically quantified within the context of differential privacy. We consider both the case of known centre as well as tests for symmetry about an unknown centre.

In the last decade, the networking research community has significantly fueled the network softwarization and virtualization trend. Network processing tasks, originally performed by dedicated hardware appliances, were converted into software components running on commodity hardware, and deployed in relevant cloud infrastructures (central and/or edge).