8 PhD projects receive funding from RU´s Resarch Fund
RU Research Fund has awarded 8 PhD Student Grants og the total amount 42.720.000 ISK. Each grant is 420.000 ISK per month for max one year + 300.000 ISK travel grant.
Furthermore, RU has launched a special two-semester course for PhD students. The course addresses the following topics: How to write a good grant proposal to a competitive research fund, how to write and publish a scientific paper and write scientific English, ethics in science, statistical methods in science – best practice (field specific), and Instruction how to teach. Instructors are Dr. Kristján Kristjánsson, Director of RU Research Services and Dr. Rannveig S. Sigurvinsdóttir, Assistant Professor at School of Business, Psychology Department.
Ármann Gylfason: Lagrangian dispersion in mixed convective turbulence
Grant amount: 5.340.000 ISK
School: School of Science and Engineering
Doctoral Student: NN
Supervisor: Ármann Gylfason
Project title: Dreifni agna í blönduðu varmadrifnu iðustreymi
Short description of the project:
The goal of the project is to explore the transport properties of turbulent thermal convection by a detailed investigation of the Lagrangian properties of Rayleigh-Bénard convection and mixed convective flow systems where the thermal field is perturbed by mechanical forcing of turbulence. We will perform Lagrangian measurements of passive tracers and their temperature and inertial particles, both near solid boundaries and in the bulk flow. Our focus is on the coherent thermal plume structures of the buoyant field, and implications on passive and inertial particle dynamics, dispersion, and distribution of particles, heat and substances. Experiments will be performed in Laboratories at Reykjavik University and at CNRS ENS de Lyon. We apply Lagrangian Particle Tracking combined with Eulerian measurements of the velocity and temperature fields. In addition, we will develop a new Mie Scattering Imaging method, to simultaneously detect Lagrangian particle temperature and velocity, by measuring changes in the size of expanding polymer microcapsules.
The focus of the proposal lies in systematic adjustment of flow parameters, ranging from buoyancy driven flows to perturbations of isotropic flows. The results will be useful to the study of turbulence involving convection, occurring widely in natural and engineering flows. The results will provide a new perspective on convective turbulence and give us insight into deterministic and probabilistic structures that signifies turbulence.
Duncan Paul Attard: Ensuring Correctness in Distributed Systems
Grant amount: 5.340.000 ISK
School: School of Computer Science
Doctoral Student: Duncan Pal Attard
Supervisor: Adrian Francalanza (University of Malta)
Short description of the project:
Runtime Verification is becoming a widespread software verification technique used in cases where the model of the system under scrutiny is unavailable or infeasible to obtain. The technique perceives the system as a black box and can be used for post-deployment verification or in scenarios were system components are loaded dynamically. However, runtime verification has severe limits in terms of what can be monitored at runtime since its analysis is restricted to the current execution trace. We propose to investigate novel methods for extending these limits. In particular, we consider methods that rely on software replication to increase the runtime information available for analysis: multiple traces obtained via the observed execution of each system replica are exploited to obtain a more comprehensive view of system behaviour. Software replication arises naturally in distributed settings where systems typically consist of multiple components, making this an ideal area where our proposed research can be extensively applied.
Gylfi Þór Guðmundsson: The use cases of Anomaly Detection in Aerial Images
Grant amount: 5.340.000 ISK
School: School of Computer Science
Doctoral Student: NN
Supervisor: Gylfi Þór Guðmundsson
Short description of the project:
The use of autonomous unmanned aerial vehicles (UAV) is already growing into a blooming business
and is expected to grow to a revenue of 10 billion Euros in Europe by 2035. The UAV's fulfil a wide range of services but the primary sensing devices of the UAV is the video camera and imaging. Image processing is therefore an important research area in maximising the utility of the UAV revolution. The current state-of-the-art in almost all image processing is Deep-Learning (DL) but the DL algorithms are computationally demanding and thus require power-hungry hardware. For UAV's, where battery power is a scares resource, this is a serious issue and why the image processing is typically done post flight (off-line). The ability to do on-line image processing can however be of great benefit, both in regards to developing new types of applications and services but also in improving the current applications of the drones. We propose here a project with two primary goals: 1) The development of Anomaly Detection in aerial imagery and demonstrate how useful that ability is to solve a wide range of tasks; and 2) The adaptation and extensive evaluation of applying our software to state-of-the-art mobile graphical processing units (GPU) that can do the processing on-line.
Henning Arnór Úlfarsson: Combinatorial Exploration with Applications to Permutation Patterns and other Structures
Grant amount: 5.340.000 ISK
School: School of Computer Science
Doctoral Student: NN
Supervisor: Henning Arnór Úlfarsson
Short description of the project:
We propose to continue the development of a framework that can leverage domain-specific knowledge to discover and automatically prove theorems in several areas of mathematics. Combinatorial exploration is an experimental approach that rigorously derives structural results about mathematical objects. When a human has discovered the structure of an object, there are several tools which allow various properties of the object to be computed. However, the steps from the problem statement to the structure is often ad-hoc. This is the gap we propose to fill.
Using techniques from enumerative and analytic combinatorics, computer algebra, and algebraic geometry, we have implemented a prototype of our framework. By adding domain-specific knowledge from the field of permutation patterns to the prototype, we created an algorithm that has discovered new theorems and rediscovered results spanning dozens of papers in the literature.
We propose to integrate techniques from machine learning to enhance the prototype, as well as turning the output into a formal proof. We will allow the researcher to interact with the framework while running through a graphical user interface. Finally, we propose to add more strategies for reasoning about several combinatorial objects.
The outcome of this proposal training of young researchers, and publications in journals and presentations at international conferences. The implementations of these theoretical algorithms will be made available open source.
Kamilla Rún Jóhannsdóttir: Assessment of cognitive workload by understanding the heart's neurophysiology
Grant amount: 5.340.000 ISK
School: School of Business
Doctoral Student: NN
Supervisor: Kamilla Rún Jóhannsdóttir
Short description of the project:
Managing cognitive workload effectively is critical for many real world operational environments such as the cockpit or the automobile. Workload monitoring is most commonly done by measuring cardiovascular reactivity. However, our understanding of the psycho-physiological factors controlling the heart‘s reactivity to workload is limited, hindering us in moving forward to a reliable and meaningful measure of cognitive workload. The objective of the proposed research is to understand how cognitive workload as handled by the individual is reflected in the heart's reactivity by looking further at the temporal nature of the signal, individual differences, and the brain. The temporal nature of the cardiovascular signal will be captured using time- and frequency domain methods thereby reducing the use of averaging over time segments and matched against the relevant individual trait dimensions. Additional empirical work will be carried out to further understand the complex interaction between brain and peripheral physiology in regulating the heart‘s state. The ultimate goal of the proposed project is to model the heart‘s reaction to cognitive workload, taking into consideration the brains input and personality trait characteristics.
Mohammad Adnan Hamdaqa: A Framework for Building Secure and Reliable Proof-Carrying Blockchain Applications
Grant amount: 5.340.000 ISK
School: School of Computer Science
Doctoral Student: NN
Supervisor: Mohammad Adnan Hamdaqa
Project title: A Framework for Building Secure and Reliable Proof-Carrying Blockchain Applications
Short description of the project:
The goal of this project is to improve trust in blockchain technologies by filling the gap between smart contract developers and consumers. The project aims to facilitate the development of smart contracts by providing a high-level language on top of current smart contract frameworks. The proposed language is more abstract than existing smart contract specification languages (e.g., Solidity, Viper, or Serpent). It will help software developers architect and build blockchain applications, and generate target executable contracts with the accompanying safety and reliability proofs that will satisfy consumers' safety policies.
The project will take a model-driven engineering approach to develop a modelling language for smart contracts that is platform agnostic. Unlike any other model-driven code generator, our proposed code generation approach will employ a proof carrying code (PCC) mechanism that will enable us to automatically generate proofs of safety and attach certificates to the smart contract code, in order to enable any of the blockchain platforms to verify the smart contract code prior to its deployment.
Slawomir Marcin Koziel: Design-Oriented Computationally-Efficient Surrogate Modelling of High-Frequency Structures
Grant amount: 5.340.000 ISK
School: School of Science and Engineering
Doctoral Student: NN
Supervisor: Slawomir Marcin Koziel
Short description of the project:
Accurate evaluation of the system performance is a fundamental prerequisite to ensure reliability of design processes in any engineering discipline. In the design of high-frequency structures (RF/microwave circuits, antennas and antenna arrays, photonics, electromagnetic compatibility, etc.) it is normally performed using full-wave electromagnetic (EM) analysis, which is inherently expensive in computational terms when applied to realistic structures. Consequently, its straightforward application in design and modelling processes is challenging, especially when repeated simulations at different points of the design/modelling space are necessary, e.g., for parametric optimization, statistical analysis, robust or tolerance-aware design. For these tasks, fast yet reliable replacement models are indispensable. The majority of existing methods for constructing of replacement models, data-driven and surrogate-assisted, are of limited applicability in terms of dimensionality and ranges of parameter spaces. This research project aims at development of techniques for low-cost construction of fast surrogate models which can be utilized for real-world design and performance analysis tasks of RF, microwave, antenna, and photonic engineering, and closely related fields, e.g., high-frequency electronics, EM packaging, RFID, digital signalling, wireless powering.
Slawomir Marcin Koziel: Accelerated Surrogate-Assisted Design of High-Performance Microstrip Corporate Feeds Integrated with Array Apertures
Grant amount: 5.340.000 ISK
School: School of Science and Engineering
Doctoral Student: NN
Supervisor: Slawomir Marcin Koziel
Short description of the project:
Integration of devices, components, and modules is essential for miniaturization of modern electronics. At the digital end, integration is realized with semiconductor technology. For integrated antenna-feed modules at the high-frequency end of radars, communication, navigation, RFID, and medical systems, CAD tools and techniques (in addition to devices and materials) are essential for development. Microstrip antenna arrays are an important class of low-profile/-weight/-cost and embeddable antennas of spatial filtering/directive capabilities. Microstrip corporate feeds allow for better control of microwave signals and extendable for phased and reconfigurable solutions. Systematic, man-hour efficient design approaches have not been developed yet for integrated high performance array modules—contemporary antenna engineering means used in practice are not readily suitable for integrated microstrip arrays. This project aims reliable, comprehensive, computationally-efficient, and automated techniques (from prototyping stage up to validation) for design of low-sidelobe microstrip antenna arrays with integrated apertures and feeds.
