This thesis explores the potential that Intended Learning Outcomes (ILOs) networks have to support learning and teaching, particularly for supporting self-directed learners. As a contribution to knowledge, this work presents evidence that suggests algorithms traversing ILO networks can produce learning routes that are similar to routes produced by teachers. For this thesis, an ILO network comprised of cognitive learning outcomes in the area of music theory was created, and algorithms to traverse the network were designed. Trials were undertaken to determine the interpretability of the ILOs and the ILO network to non-subject matter experts. Further trials explored to what degree the routes produced by the traversal algorithms differed from routes produced by contemporary teaching professionals. Findings indicate that ILOs and ILO networks were understood well by the learners involved in the first trial. Results from the second trial suggest that the algorithms produced similar routes to those produced by teachers, but conclude that the metrics and the route lengths may need to be refined in order to better reflect the scale of educational undertakings pursued today.

This thesis focuses on the energy efficiency (EE) of relay-aided cellular networks, which is motivated by the ever-increasing need to support higher and higher data rates, while reducing the energy costs. Relaying is a beneficial tool for either increasing the reliability or the coverage area of a wireless network as a result of the reduced communication distances, albeit this might increase the energy consumption in practice. Our approach in this thesis was to study and model progressively more complex and more realistic cellular networks. By utilizing novel transmission protocol designs, we were able to formulate their associated EE resource allocation optimization problems. Thus, efficient tools can be employed for solving these problems to maximize the EE.

Wireless sensor networks are receiving a considerable degree of research interest due to their deployment in an increasing number and variety of applications. However, the efficient management of the limited energy resources of such networks in a way that maximises the information value of the data collected is a significant research challenge. To date, most of these systems have adopted a centralised control mechanism, but from a system's perspective this raises concerns associated with scalability, robustness, and the ability to cope with dynamism. Given this, decentralised approaches are appealing. But, the design of efficient decentralised regimes is challenging as it introduces an additional control issue related to the dynamic interactions between the network's interconnected nodes in the absence of a central coordinator. Within this context, this thesis first concentrates on decentralised approaches to adaptive sampling as a means of focusing a node's energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a node's observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute). The second major line of work then focuses on the adaptive sampling, transmitting, forwarding, and routing actions of each node in order to maximise the information value of the data collected in resource-constrained networks. This adds additional complexity because these actions are inter-related, since each node's energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, in this setting we develop two optimal decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed (either because the underlying communication network is a tree, or because an arbitrary choice of route has been made) and then calculates the optimal integration of actions that each node should perform. The second deals with flexible routing, and makes optimal decisions regarding both the sampling, transmitting, and forwarding actions that each node should perform, and also the route by which this data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing delivers approximately twice the quantity of information to the base station compared to the algorithm with fixed routing. However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing should be favoured.

In this thesis, a new approach based on an electrostatically suspended gyroscope (ESG) was explored in order to improve the performance of micromachined gyroscopes. Typically, a conventional micromachined gyroscope consists of a vibrating mass suspended on elastic beams that are anchored to a substrate. It measures the rotation rate of a body of interest by detecting rotation-induced Coriolis acceleration of a vibrating structure. Such a gyro is sensitive to fabrication imperfections and prone to cross-coupling signals between drive and sense modes, which degrade its performance. The icromachined ESG, on the other hand, employs a proof mass with no elastic beams connecting it to a substrate. The proof mass is levitated and spun electrostatically. In the presence of rotation, the spinning mass will rotate in the direction perpendicular to the spin and input axes. The displacement of the mass is capacitively sensed by a closed-loop electrostatic suspension system based on a sigma delta modulator (ΣΔM). The system, in turn, produces feedback forces to counteract the movement of the mass, moving it back to its nominal position. These feedback forces are equal to the precession torque and provide a measure of the rotation rate. Electrostatic levitation isolates the proof mass from unwanted inputs (for instance, mechanical friction, wear and stress), and thus the long-term stability of the gyroscope is expected to be improved. Furthermore, the micromachined ESG has a potential to achieve higher device sensitivity than that of a conventional vibrating-type micromachined gyroscope. This thesis deals with three aspects of the development of the micromachined ESG: device design and analysis, design and simulation of an electrostatic suspension system and device fabrication. Analytical calculations and ANSYS simulations were carried out to predict the behaviour of the micromachined ESG. The micromachined ESG with an electrostatic suspension control system based on a sigma-delta modulator (ΣΔM) was modelled in Matlab/Simulink and OrCAD/PSPICE to evaluate the operation and performance of the closed-loop gyroscope. A front-end capacitive readout circuit was also developed. Initial tests were carried out and the measurement results showed a reasonable good agreement to both theoretical calculation and OrCAD/PSPICE simulation. The fabrication of the prototype micromachined ESG was developed using a triple-stack glass-silicon-glass anodic bonding in combination with a high-aspect-ratio DRIE process. Fabrication results and processing issues were discussed. However, it was found that the rotor of the fabricated gyroscopes was stuck to the substrate. Therefore, a fabricated prototype, which had not yet covered by a top substrate, was used to investigate an alternative approach to provide electrostatic levitation using sidewall electrodes. The analysis of this approach was investigated using 2D electrostatic finite element simulations in ANSYS. Initial tests were also carried out.

Segmenting objects with complex shapes, like segmenting vessels in iridology and detecting roads in remote sensing, is of practical significance in image analysis. Region growing and snakes are the main methods used in the field, but the former cannot yield an exact result whilst the latter has difficulties with topological changes. This thesis presents a new method, based on the paradigm of water flow. The mechanism embodies the fluidity of water and thus can deal with complicated objects. A snake-like force functional is utilized, including edge-based and region-based forces, guiding extraction to select the target feature. Water characteristics such as surface tension and adhesion are also implemented so that the smoothness of extracted contour and ability of flow to narrow branches are obtained. Furthermore, force field theories are incorporated for an alternative definition of the water flow forces so that the water flow framework becomes more generalised and flexible and the underlying theoretical basis is more consistent. Because of the 3-D nature of the physical water flow process, the extension of the model to higher dimensions is straightforward and has been implemented in this research. The higher dimensionality, however, increases the computational cost. To improve the efficiency, a water cooling system is proposed. This system identifies water elements that are inactive in previous flow iterations and removes them from the subsequent computations to reduce the total computational burden. The water cooling system provides an adjustable efficiency-controlling-facility and is also useful in 2-D applications. The new technique has been assessed on both synthetic and natural images. The topological adaptability and the geometrical flexibility of the model are assessed. In addition, the approach is shown to be able to segment shapes with weak contrast to their background. The good ability to handle noise, consistent with properties included in its formulation, has been justified both qualitatively and quantitatively. The effectiveness of the water cooling system is also proved graphically and quantitatively. Further, the method is applied to medical imaging problems such as MRI image segmentation, vessel detection and medical volume segmentation. The results justify the great potential of the new technique in the real-world applications.

Fixed-rate and rateless channel code constructions are designed for satisfying conflicting design tradeoffs, leading to codes that benefit from practical implementations, whilst offering a good bit error ratio (BER) and block error ratio (BLER) performance. More explicitly, two novel low-density parity-check code (LDPC) constructions are proposed; the first construction constitutes a family of quasi-cyclic protograph LDPC codes, which has a Vandermonde-like parity-check matrix (PCM). The second construction constitutes a specific class of protograph LDPC codes, which are termed as multilevel structured (MLS) LDPC codes. These codes possess a PCM construction that allows the coexistence of both pseudo-randomness as well as a structure requiring a reduced memory. More importantly, it is also demonstrated that these benefits accrue without any compromise in the attainable BER/BLER performance. We also present the novel concept of separating multiple users by means of user-specific channel codes, which is referred to as channel code division multiple access (CCDMA), and provide an example based on MLS LDPC codes. In particular, we circumvent the difficulty of having potentially high memory requirements, while ensuring that each user’s bits in the CCDMA system are equally protected. With regards to rateless channel coding, we propose a novel family of codes, which we refer to as reconfigurable rateless codes, that are capable of not only varying their code-rate but also to adaptively modify their encoding/decoding strategy according to the near-instantaneous channel conditions. We demonstrate that the proposed reconfigurable rateless codes are capable of shaping their own degree distribution according to the nearinstantaneous requirements imposed by the channel, but without any explicit channel knowledge at the transmitter. Additionally, a generalised transmit preprocessing aided closed-loop downlink multiple-input multiple-output (MIMO) system is presented, in which both the channel coding components as well as the linear transmit precoder exploit the knowledge of the channel state information (CSI). More explicitly, we embed a rateless code in a MIMO transmit preprocessing scheme, in order to attain near-capacity performance across a wide range of channel signal-to-ratios (SNRs), rather than only at a specific SNR. The performance of our scheme is further enhanced with the aid of a technique, referred to as pilot symbol assisted rateless (PSAR) coding, whereby a predetermined fraction of pilot bits is appropriately interspersed with the original information bits at the channel coding stage, instead of multiplexing pilots at the modulation stage, as in classic pilot symbol assisted modulation (PSAM). We subsequently demonstrate that the PSAR code-aided transmit preprocessing scheme succeeds in gleaning more information from the inserted pilots than the classic PSAM technique, because the pilot bits are not only useful for sounding the channel at the receiver but also beneficial for significantly reducing the computational complexity of the rateless channel decoder.

In recent years metal/semiconductor Schottky barriers have found numerous applications in nanoelectronics. The work presented in this thesis focuses on the improvement of a few of the relevant devices using electrodeposition of metal on Ge for Schottky barrier fabrication. This low energy metallisation technique offers numerous advantages over the physical vapour deposition techniques. Electrical characteristics of the grown diodes show a high quality rectifying behaviour with extremely low leakage currents even on highly doped Ge. A non-Arrhenius behaviour of the temperature dependence is observed for the grown Ni/Ge diodes on lowly doped Ge that is explained by a spatial variation of the barrier heights. The inhomogeneity of the barrier hights is explained in line with an intrinsic surface states model for Ge. The understanding of the intrinsic surface states will help to create ohmic contacts for doped n-MOSFETs. NiGe were formed single phase by annealing. Results reveal that by using these high-quality germanide Schottky barriers as the source/drain, the subthreshold leakage currents of a Schottky barrier MOSFET could be minimised, in particular, due to the very low drain/body junction leakage current exhibited by the electrodeposited diodes. The Ni/Ge diodes on highly doped Ge show negative differential conductance at low temperature. This effect is attributed to the intervalley electron transfer in Ge conduction band to a low mobility valley. The results show experimentally that Schottky junctions could be used for hot electron injection in transferred-electron devices. A vertical Co/Ni/Si structure has been fabricated for spin injection and detection in Si. It is shown that the system functions electrically well although no magnetoresistance indicative of spin injection was observed.

Research in the last few years has indicated that, despite modern algorithms being secure against all published mathematical attacks and being far too complex to break by brute force, secret key data can be gathered by monitoring the power consumption. This is known as a power analysis attack, the most successful has been differential power analysis (DPA). Several countermeasures have been proposed for preventing power analysis attacks with varying degrees of efficacy. One thing all the countermeasures have in common is their large cost in terms of performance and or cost. In this thesis several modifications to the AES algorithm are proposed that seek to inherently secure it against DPA and their effectiveness and cost are investigated. Due to the statistical nature of DPA there is no set amount of power consumption data that will always give the correct result for a given device, rather, a value for the SNR and the number of power measurements involved in the attack will equate to a probability of success. In this thesis a statistical model of the DPA attack is derived and it is used to find a method for calculating the probability that a particular attack will be successful. A more benign use for DPA is also discussed. If the signature of a specific pattern of register transitions can be detected in the power consumption of a device then designers can add hardware whose sole purpose is to be detectable in a power trace and act as a watermark to prove the presence of intellectual property.

Dielectrophoresis (DEP) is a technique that can be used to separate particle at microscale. It is of particular interest because it is a non-invasive, non-destructive and non-contact technique, which ensures that sample composition remains the same with only the particles being separated. On the microscale, DEP has been used to separate viable and non-viable cells, and cells with different dielectric properties, with the aid of a range of miniaturised, microfabricated devices. However, DEP at the nano-scale is a novel area and is still under research. Miniaturisation of devices in general has been an ongoing trend to improve the performance of analytical tools. In particular, processes for micro-device fabrication using dry film resist have been studied in order to reduce size, cost, sophisticated hardware usage and power consumption. This thesis presents an investigation into the novel design of dielectrophoretic particle separator, using rapid dry film resist methods to construct an integrated device. The development of analysis software for detecting particle movement in videos of experiments is presented, along with its use as a data analysis tool for determining particle position in the array. Characterisation measurements have been performed for a range of experimental parameters demonstrating the variability and behaviour of the device. Separation experiments were performed using test micron and submicron particles over a wide range of applied field frequencies, confirming the theoretical predictions and demonstrate the standard of separation efficiency. Preliminary investigations of other application of the device to larger particle and integrating micropump technology are also presented.

Efficient resource management is one of key problems associated with large-scale distributed computational systems. Taking into account their increasing complexity, inherent distribution and dynamism, such systems are required to adjust and adapt resources market that is offered by them at run-time and with minimal cost. However, as observed by major IT vendors such as IBM, SUN or HP, the very nature of such systems prevents any reliable and efficient control over their functioning through human administration. For this reason, autonomic system architectures capable of regulating their own functioning are suggested as the alternative solution to looming software complexity crisis. Here, large-scale infrastructures are assumed to comprise myriads of autonomic elements, each acting, learning or evolving separately in response to interactions in their local environments. The self-regulation of the whole system, in turn, becomes a product of local adaptations and interactions between system elements. Although many researchers suggest the application of multi-agent systems that are suitable for realising this vision, not much is known about regulatory mechanisms that are capable to achieve efficient organisation within a system comprising a population of locally and autonomously interacting agents. To address this problem, the aim of the work presented in this thesis was to understand how global system control can emerge out of such local interactions of individual system elements and to develop decentralised decision control mechanisms that are capable to employ this bottom-up self-organisation in order to preserve efficient resource management in dynamic and unpredictable system functioning conditions. To do so, we have identified the study of complex natural systems and their self-organising properties as an area of research that may deliver novel control solutions within the context of autonomic computing. In such a setting, a central challenge for the construction of distributed computational systems was to develop an engineering methodology that can exploit self-organising principles observed in natural systems. This, in particular, required to identify conditions and local mechanisms that give rise to useful self-organisation of interacting elements into structures that support required system functionality. To achieve this, we proposed an autonomic system model exploiting self-organising algorithms and its thermodynamic interpretation, providing a general understanding of self-organising processes that need to be taken into account within artificial systems exploiting self-organisation.