This thesis presents an in-detail stability analysis of a Power Hardware in the Loop (PHIL) network formed through an Ideal Transformer Method (ITM) interface. The ever-growing demand for PHIL testing necessitates thorough ...

An artificial olfactory system, capable of detecting and discriminating different volatile organic compounds, has a vast potential to solve several sensing challenges. This work demonstrates an electronic olfaction system ...

This thesis presents the theory and development of a framework for the design of electromagnetic metasurfaces. These metasurfaces can be used to systematically transform an incident electromagnetic field into a different ...

In this thesis, I performed a study on the electromagnetic behaviors of an Electromagnetic Bandgap (EBG) structure with a specific set of parameters, and explored the effects of the geometrical parameters ...

It is difficult to detect atherosclerotic plaque from optical coherence tomography (OCT) images via visual inspection. In this work, we developed three algorithms to allow us to detect atherosclerotic plaque more effectively: ...

Transient simulation of large electric power systems is resource-intensive and it is often prohibitive to model the entire network in the simulator. To save computational resources, only a part of the network is modelled ...

System-wide power outages are fortunately very rare. However, when they do occur, they can catastrophically affect tens or even hundreds of millions of customers. To minimize this impact, a “black start” power restoration, ...

Increased penetration of electric vehicles (EVs) and renewable energy sources (RESs) in power systems can directly affect the system reliability and impose additional complexities to planning and operation due to their ...

Transient stability studies are required to be carried out for an efficient and secure operation of power systems. However, due to the limitations of computer memory and processing speed, handling a complete set of DAEs ...

Least Angle Regression (LARS) solves the basis pursuit optimization problem, as a sparse signal recovery algorithm, for all relevant regularization parameter values simultaneously. However, despite this efficiency of LARS, ...