This talk provides an overview of the latest laser-based lighting and Gbit/s VLC and underwater communications. Recent progress of visible wavelength high-speed transmitters and receivers will also be discussed.

Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. The emergence of Unmanned Aerial Vehicles (UAVs) gives the possibility to monitor these events over large geographical areas. In this talk, we focus on the problem of trajectory planning for a swarm of unmanned aerial vehicles sensing flooding conditions.

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Due to large volume (billions of devices), there is a requirement that the cost is extremely low, to the extent that they become disposable. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet, screen and 3D printing. This talk introduces additive manufacturing as an emerging technique to realize low cost, flexible and wearable wireless communication and sensing systems.

Abstract

Compliant electronics are an emerging class of electronics which offer physical flexibili

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Ultraviolet (UV) group III-Nitride-based light emitters have been used in various applications such as water purification, medicine, lighting and chemical detection. Despite attractive properties such as bandgap tunability in the whole UV range (UV-C to UV-A), high chemical stability and relative low cost, the low quantum efficiency hamper the full utilization. This thesis aims to show alternative solutions to such problems by employing nanowires (NWs) structures, and target the eventual application of reliable and high power NWs-based light-emitting devices, enabling large-scale production using the established silicon foundry processes. Here, we present the improvement of injection current and optical power of AlGaN NWs LEDs by involving a metal bilayer thin film with a dual purpose: eliminate the potential barrier for carrier transport, and inhibit the formation of silicide.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

International Students in CEMSE who attended Photonics Summer Camp 2019, will present their research findings and experience of KAUST, in 10 minute presentations. Breakfast will be served. All KAUST affiliates are welcome to attend. The Photonics Summer Camp is an international internship program, currently in it’s 4th year. The program is just 4 weeks long and is designed to welcome overseas and local students to KAUST, in order to facilitate research innovation and collaboration amongst the next generation of photonics researchers. The program is sponsored by the CEMSE Division, with support from Graduate Affairs, and the International Programs Office.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

This series of lectures guide students to the preparation and analysis of a well-organized abstract. We will discuss the proper language (tense, voice, and person) for abstract writing, and learn how to meet the purposes of different abstracts. Finally, students will have a chance to compose and evaluate their writing. Topics: Overview of abstract writing; Conference abstract journal abstract; Organization of an abstract; Language conventions of abstract writing; Disciplinary abstract analysis; Frequent mistakes of abstract writing.

Abstract

In the last decade, with the emergence of the internet of things (IoT) as well as machine

The objective of benchmark learning is to use a training sample to learn about fundamental limits on performance of a classifier or other statistical inference procedure. This meta-learning problem is a crucial component of data science and interpretable AI. Examples include sequential design of experiments, reinforcement learning and sensor management in the fields of statistics, machine learning and systems engineering, respectively. The challenge is learn about best achievable accuracy directly from the data sample without having to approximate and mplement an optimal classifier algorithm. In this talk we will introduce a general information theoretic framework that yields benchmark learners having both linear computational complexity and linear sample complexity. We will illustrate how this framework in the context of benchmarking image classification, autonomous navigation, and deep neural network performance.

Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.

Massive multiple-input multiple-output (MIMO) is a key enabling technology to achieve the required spectral and energy efficiency of the next generation of wireless networks. By endowing the base station (BS) with hundreds of antennas and relying on spatial multiplexing, massive MIMO allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate massive MIMO in different contexts.

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Abstract

We are stumbling across a video tsunami flooding our communication channels.

The design of laser-based optical sensors relies heavily on precise spectroscopic knowledge of atomic and molecular absorption transitions. Accurate spectroscopic information is invaluable in several fields such as biology, chemistry, astronomy, and environmental science. Within the electromagnetic spectrum, the mid-infrared (MIR) region can enable sensors with higher sensitivity due to the stronger absorption cross-sections. Moreover, MIR spectral transitions correspond to the fundamental vibrational motions of the molecules and are thus considered fingerprints of the molecular structure. Vibrational bands contain many rotational transitions, resulting in fine-splitting of spectral bands, particularly in gaseous samples. In order to resolve the fine rotational structure of vibrational bands, high-resolution MIR spectrometers are needed.

Non-orthogonal multiple access (NOMA) has recently emerged not only as a new design of multiple access techniques in cellular networks, but also as a general principle of network architecture for applications beyond cellular systems. This talk will present and discuss the fundamentals of NOMA, and examine how it can be combined with other emerging communication technologies. Some new research trends and challenges will also be discussed.

Specifically, I will introduce hyperspectral image restoration and its impacts on content interpretation. Despite advances in sensor technologies, degradation (e.g., noise, blur, low resolution, etc.) cannot be avoided during the hyperspectral images’ acquisitions, which can affect information retrieval and content interpretation. The first part of my talk will present the techniques I developed to improve the image qualities (noise reduction, sharpening, resolution enhancement, etc.), with specific applications to plant disease mapping in precision agriculture and fruit bruise detection in food inspection.

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

I will highlight the main contributions in the field of coded cooperative communications, where at first a study on the performance analysis of network-coded distributed coding schemes with different strategies for handling the relay-error propagation problem will be presented. Furthermore, a study proposing a relay selection scheme based on network-coded soft information relaying will be presented. The introducing of the Rayleigh-Gaussian model, which is applied to the forwarded relay soft symbols, have shown ability to give better performance in dealing with error propagation, and allowed us to give a tractable performance analysis of network-coded schemes under Rayleigh fading channels.

We study a simple learning dynamic model of routing (congestion) games to explore the effects of increasing the total demand on system performance. We focus on the most benign setting, non-atomic routing games with two parallel edges of linear cost, where all agents evolve using Multiplicative Weights Updates with a fixed learning rate.