<p> This thesis presents a novel hybrid methodology using Recurrent Neural Network and Dynamic Time Warping to solve the mode estimation problem of a radar warning receiver (RWR). The RWR is an electronic support (ES) system with the primary objective to estimate the threat posed by an unfriendly (hostile) radar in an electronic warfare (EW) environment. One such radar is the multi-function radar (MFR), which employs complex signal architecture to perform multiple tasks. As the threat posed by the radar directly depends on its current mode of operation, it is vital to estimate and track the mode of the radar. The proposed method uses a recurrent neural network (echo state network and recurrent multi-layer perceptron) trained in a supervised manner, with the dynamic time warping algorithm as the post processor to estimate the mode of operation. A grid filter in Bayesian framework is then applied to the dynamic time warp estimate to provide an accurate posterior estimate of the operational mode of the MFR. This novel approach is tested on an EW scenario via simulation by employing a hypothetical MFR. Based on the simulation results, we conclude that the hybrid echo state network is more suitable than its recurrent multi-layer perceptron counterpart for the mode estimation problem of a RWR.</p> / Thesis / Master of Applied Science (MASc)

The task of modeling and controlling the evolution of dynamical sys- tems is one of the main objectives in mechatronics engineering. When approaching the problem of controlling physical or digital systems, the dynamical models have been historically divided into continuous-time, described by differential equations, and discrete-time, described by difference equations. In the last decade, a new class of models, known as hybrid dynamical systems, has gained popularity in the control community because of its high versatility. This framework combines continuous-time and discrete- time evolution, thus allowing for both the description of a broader class of systems and the achievement of better-performing controllers, compared to the traditional continuous-time alternatives. After the first rigorous introduction of the framework, several Lyapunov-based results were published in the literature, and numerous application areas were shown to benefit from the introduction of a hybrid dynamics, like systems involving impacts or physical systems connected to digital controllers (cyber-physical systems). In this thesis, we use the hybrid framework to study different mechatronics-inspired control problems. The applications we consider are diverse, so we split the presentation into three parts. In the first part we further analyze a particular hybrid control strategy, known as reset control, providing some new theoretical guarantees, together with an application to adaptive control. In the second part we consider two applications of the hybrid framework to the network dynamics field, specifically we analyze the problems of distributed state estimation and of uniform synchronization of nonlinear oscillators. In the third part, we use a hybrid approach to study two applications where this framework has been rarely employed, or not at all, namely smart agriculture and trajectory tracking for a bipedal walking robot. We study these application-inspired problems from a theoretical point of view, giving robust Lyapunov-based stability guarantees. We complement the theoretical analysis with numerical results, obtained from simulations or from experiments.

Drawing on multiple theoretical lenses, this research studies continuous transformation, or ‘morphing’, of a business process resource network (BPRN). The aim is to further our understanding of continuous organisational change at the lowest levels of analysis within an organisation: that is, at the resource level, and that resource’s relationships to other resources as they exist within a BPRN. Data was gathered from a single, in depth case study. Analysis was achieved by means of mapping BPRN evolution using ‘temporal bracketing’, ‘visual’ and ‘narrative’ approaches (Langley, 1999). The analysis revealed two mechanisms that appear to govern microstate morphing: bond strength and stakeholder expectation. In addition, four factors emerged as important: environmental turbulence, timing and timeliness of changes, concurrency of changes, and enduring business logic. An emergent model of microstate morphing which acknowledges the importance of socio-materiality in actor network morphogenesis (ANM) is presented. This study shows how effective relationships and configuration of resources within the BPRN can be achieved to facilitate timely, purposeful morphing. Five propositions are offered from the emergent ANM model. Specifically, these relate to the conditional operating parameters and the identified generative mechanisms for continuous organisational transformation within the BPRN. Implications for practice are significant. A heuristic discussion guide containing a series of questions framed around the ANM model to highlight the challenges of microstate morphing for practitioners is proposed. Two routes for future research are suggested: replication studies, and quantifying BPRN change in relation to an organisation’s environment using a ii survey instrument and inferential statistical analysis based on the ANM model features and propositions.