System level modelling and design of hypergraph based wireless system area networks for multi-computer systems

This thesis deals with issues pertaining the wireless multicomputer interconnection networks namely topology and Medium Access Control (MAC). It argues that new channel assignment technique based on regular low-dimensional hypergraph networks, the dual radio wireless hypermesh, represents a promising alternative high-performance wireless interconnection network for the future multicomputers to shared communication medium networks and/or ordinary wireless mesh networks, which have been widely used in current wireless networks. The focus of this work is on improving the network throughput while maintaining a relatively low latency of a wireless network system. By means of a Carrier Sense Multiple Access (CSMA) based design of the MAC protocol and based on the desirable features of hypermesh network topology a relatively high performance network has been introduced. Compared to the CSMA shared communication channel model, which is currently the de facto MAC protocol for most of wireless networks, our design is shown to achieve a significant increase in network throughput with less average network latency for large number of communication nodes. SystemC model of the proposed wireless hypermesh, validated through mathematical models, are then introduced. The analysis has been incorporated in the proper SystemC design methodology which facilitates the integration of communication modelling into the design modelling at the early stages of the system development. Another important application of SystemC modelling techniques is to perform meaningful comparative studies of different protocols, or new implementations to determine which communication scenario performs better and the ability to modify models to test system sensitivity and tune performance. Effects of different design parameters (e.g., packet sizes, number of nodes) has been carried out throughout this work. The results shows that the proposed structure has out perform the existing shared medium network structure and it can support relatively high number of wireless connected computers than conventional networks

Quantitative diffusion MRI with application to multiple sclerosis

Diffusion MRI (dMRI) is a uniquely non-invasive probe of biological tissue properties, increasingly able to provide access to ever more intricate structural and microstructural tissue information. Imaging biomarkers that reveal pathological alterations can help advance our knowledge of complex neurological disorders such as multiple sclerosis (MS), but depend on both high quality image data and robust post-processing pipelines. The overarching aim of this thesis was to develop methods to improve the characterisation of brain tissue structure and microstructure using dMRI. Two distinct avenues were explored. In the first approach, network science and graph theory were used to identify core human brain networks with improved sensitivity to subtle pathological damage. A novel consensus subnetwork was derived using graph partitioning techniques to select nodes based on independent measures of centrality, and was better able to explain cognitive impairment in relapsing-remitting MS patients than either full brain or default mode networks. The influence of edge weighting scheme on graph characteristics was explored in a separate study, which contributes to the connectomics field by demonstrating how study outcomes can be affected by an aspect of network design often overlooked. The second avenue investigated the influence of image artefacts and noise on the accuracy and precision of microstructural tissue parameters. Correction methods for the echo planar imaging (EPI) Nyquist ghost artefact were systematically evaluated for the first time in high b-value dMRI, and the outcomes were used to develop a new 2D phase-corrected reconstruction framework with simultaneous channel-wise noise reduction appropriate for dMRI. The technique was demonstrated to alleviate biases associated with Nyquist ghosting and image noise in dMRI biomarkers, but has broader applications in other imaging protocols that utilise the EPI readout. I truly hope the research in this thesis will influence and inspire future work in the wider MR community

Distributed Adaptation Techniques for Connected Vehicles

In this PhD dissertation, we propose distributed adaptation mechanisms for connected vehicles to deal with the connectivity challenges. To understand the system behavior of the solutions for connected vehicles, we first need to characterize the operational environment. Therefore, we devised a large scale fading model for various link types, including point-to-point vehicular communications and multi-hop connected vehicles. We explored two small scale fading models to define the characteristics of multi-hop connected vehicles. Taking our research into multi-hop connected vehicles one step further, we propose selective information relaying to avoid message congestion due to redundant messages received by the relay vehicle. Results show that the proposed mechanism reduces messaging load by up to 75% without sacrificing environmental awareness. Once we define the channel characteristics, we propose a distributed congestion control algorithm to solve the messaging overhead on the channels as the next research interest of this dissertation. We propose a combined transmit power and message rate adaptation for connected vehicles. The proposed algorithm increases the environmental awareness and achieves the application requirements by considering highly dynamic network characteristics. Both power and rate adaptation mechanisms are performed jointly to avoid one result affecting the other negatively. Results prove that the proposed algorithm can increase awareness by 20% while keeping the channel load and interference at almost the same level as well as improve the average message rate by 18%. As the last step of this dissertation, distributed cooperative dynamic spectrum access technique is proposed to solve the channel overhead and the limited resources issues. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor credibility. Once the vehicle accesses the channel, we propose a decision mechanism for channel switching that is inspired by the optimal flower selection process employed by bumblebees foraging. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero