Objectives: The weight and volume of battery-powered wireless electroencephalography (EEG) systems are dominated by the batteries. Battery dimensions are in turn determined by the required energy capacity, which is derived from the system power consumption and required monitoring time. Data reduction may be carried out to reduce the amount of data transmitted and thus proportionally reduce the power consumption of the wireless transmitter, which dominates system power consumption. This thesis presents two new data selection algorithms that, in addition to achieving data reduction, also select EEG containing epileptic seizures and spikes that are important in diagnosis. Methods: The algorithms analyse short EEG sections, during monitoring, to determine the presence of candidate seizures or spikes. Phase information from different frequency components of the signal are used to detect spikes. For seizure detection, frequencies below 10 Hz are investigated for a relative increase in frequency and/or amplitude. Significant attention has also been given to metrics in order to accurately evaluate the performance of these algorithms for practical use in the proposed system. Additionally, signal processing techniques to emphasize seizures within the EEG and techniques to correct for broad-level amplitude variation in the EEG have been investigated. Results: The spike detection algorithm detected 80% of spikes whilst achieving 50% data reduction, when tested on 992 spikes from 105 hours of 10-channel scalp EEG data obtained from 25 adults. The seizure detection algorithm identified 94% of seizures selecting 80% of their duration for transmission and achieving 79% data reduction. It was tested on 34 seizures with a total duration of 4158 s in a database of over 168 hours of 16-channel scalp EEG obtained from 21 adults. These algorithms show great potential for longer monitoring times from miniaturised wireless EEG systems that would improve electroclinical diagnosis of patients.

Energy efficiency in all aspects of human life has become a major concern, due to its significant environmental impact as well as its economic importance. Information and Communication Technology (ICT) plays a dual role in this; not only does it constitute a major consumer itself (estimated 2-10% of the global consumption), but is also expected to enable global energy efficiency through new technologies tightly dependent on networks (smart grid, smart homes, cloud computing etc.). To this purpose, this work studies the problem of energy efficiency in wired networks. As this subject has recently become very active in the research community, there is parallel research towards several research directions. In this work, the problem is being examined from its foundations and a solid analytical approach is presented. Specifically, a network model based on G-network queuing theory is built, which can incorporate all the important parameters of power consumption together with traditional performance metrics and routing control capability. This generalized model can be applied for any network case to build optimization algorithms and estimate the performance of different policies and network designs. Composite optimization goals functions are proposed, comprising both power consumption and performance metrics. A gradient descent optimization algorithm that can run in O(N3) time complexity is built thereof. Using power consumption characteristics of current and future equipment, several case studies are presented and the optimization results are evaluated. Moreover, a faster gradient-descent based heuristic and a decentralized algorithm are proposed. Apart from the routing control analysis, the case of a harsher energy saving solution, namely turning o the networking equipment, is also experimentally explored. Applying a tradeoff study on a laboratory testbed, implementation challenges are identified and conclusions significant for future work are drawn. Finally, a novel admission control mechanism is proposed and experimentally evaluated, which can monitor and manage the power consumption and performance of a network.

Decarbonisation of energy supply in future low-carbon systems is expected to entail two key components: a rapid increase in the capacity of intermittent renewable generation and other low-carbon sources, and the electrification of heat and transport demand, i.e. large-scale deployment of electric vehicles and heat pumps. Both of these components are expected to impose a significant demand for additional flexibility, particularly for ancillary services associated with system balancing, while leading to additional system costs due to the degradation in generation and network infrastructure utilisation, reduced efficiency of real-time operation, and the need for network reinforcement. In this thesis a computationally efficient annual generation scheduling algorithm is developed to assess the economic and environmental performance of low-carbon systems characterised by a high penetration of intermittent renewable capacity. The algorithm performs a simultaneous optimisation of electricity generation and the provision of ancillary services such as operating reserve and frequency response. The results of applying the model to future GB system with high wind penetration indicate that the cost of wind integration in may be very high, particularly in cases when there is less flexibility available from conventional generators. The thesis proceeds to develop methodological approaches to establish system benefits that could be achieved by deploying a range of Flexible Demand (FD) technologies – Heating, Ventilation and Air Conditioning (HVAC) systems, electric vehicles, residential heat pumps or smart domestic appliances – which are all capable of altering their baseline electricity consumption profile in order to improve system operation. Detailed bottom-up models developed in the thesis characterise the behaviour patterns of FD users, establishing a link between the useful service provided by flexible devices and the resulting electricity demand pattern, while also ensuring the level of service expected by the users is not compromised as a result FD participation. A broad range of potential applications of FD has been analysed in the thesis towards a more efficient performance of future electricity systems. Key system-level benefits of deploying FD include the improved long-term system security, more efficient system balancing and ancillary services, and improved utilisation of generation and network capacity. The results suggest that system-level benefits of FD can be substantial, particularly in systems with scarce operational flexibility, such as those based on very high contribution of intermittent renewable electricity, and those accommodating a significant share of electrified transport and heating demand.

Distributed algorithms have gained a lot of attention during recent years. Their application in industry, particularly in wireless sensor networks has motivated researchers to try to design them in order to be less resource-consuming (e.g. memory and power), faster, and more reliable. There have been numerous distributed algorithms for different types of problems in the context of distributed algorithms. We are interested in a fundamental coordination problem namely the majority consensus problem. In the majority consensus problem nodes try to find the opinion of the majority in a network of interest. As our first contribution and motivated by the distributed binary consensus problem in [1] we propose a distributed algorithm for multivalued consensus in complete graphs. As our second contribution we propose an algorithm for the optimization of the binary interval consensus algorithm pioneered by Ben ezit et al in [2]. Finally we use binary interval consensus algorithm to design a framework for error-free consensus in dynamic networks using which nodes can leave or join the network during or after the consensus process.

This thesis investigates electro-mechanical generator systems which harvest energy from an aquatic environment. Such systems are needed to create maintenance-free sensor platforms for use in autonomous wireless sensor networks which have applications in water quality monitoring. Many energy harvesting mechanisms specific to an aquatic environment already exist but the majority of them have been developed for use in renewable energy generation schemes for large-scale electrical power generation. Energy harvesting, however, remains focused on the miniature scale aiming to generate enough power to run a wireless sensor node. This work therefore focuses on the identification, analysis, prototyping and miniaturization issues of existing marine wave-based energy converters. The analysis of different possible energy harvesting mechanisms is performed and their power densities are investigated as a function of their size. In order to be able to maximize the power density of the chosen energy harvester under all operating conditions, expressions have been derived for a generalized load impedance which optimizes the generator damping and resonant frequency, through changes in load resistance and reactance. Within this maximization, an AC/DC H-bridge converter is simulated as an interface between the harvester and its load. This converter is designed to mimic the required generalized load impedance and tune it so that the entire system adapts to the external working frequency. A prototype of the energy harvester was designed and tested. Based on the observation of a natural whistle made of a doubly clamped blade of grass that produces sounds when it is blown on, a MEMS harvester extracting energy from vortex-induced-vibrations was designed. The study of its feasibility as an energy harvester and the determination of its dimensions at a microscopic scale are interesting as it presents a new way of extracting energy using an electromagnetic transduction mechanism and a manufacturing advantage. A prototype of a generator harvesting energy from Vortex-Induced-Vibrations was developed using conventional engineering processes.

This thesis details the design, fabrication and characterisation of a 13 mm-diameter axial flow microturbine with an integrated electromagnetic generator. Axial turbine blades are not amenable to fabrication by traditional MEMS (micro-electromechanical systems) processes because they cannot be produced by machining prismatic shapes into the rotor disc; the direction of machining has to change as material is removed to produce the required blade curvature. This challenge was met by laser machining the blades with a novel moving-mask process so as to produce a step-wise approximation to the desired profile. The chosen material for making the microturbine rotor was the negative photo-resist SU-8. Once properly cured into its solid state, this polymer becomes very durable and dimensionally stable. The SU-8 was readily preformed using lithography and RIE (reactive ion etching), and was also responsive to excimer laser ablation as necessary for finishing the blade profiles. The microturbine was designed to be assembled into a stacked MEMS device comprising a rotor embedded with ten rare earth magnets sandwiched between upper and lower silicon stators carrying electroplated generator coils. Characterisation of the turbine showed that mechanical losses, mainly in the bearings, were significantly reducing the efficiency. A laser scanning vibrometer (Polytec MSA-400) was used to measure the turbine rundown time which was found to be only ~150 ms due to high bearing friction. The in-plane and out-of-plane vibration (wobble) of the rotor as it spun around on its micro roller bearings were also mapped to determine if bearing alignment was reducing power output. The out-of-plane vibration was found to be the main problem, so a new one-piece rotational support holder was constructed for the device. Some microturbine rotors were found to shatter above 100,000 rpm, and this led to interest in the mechanical properties of the cured SU-8. Firstly, PGAA (prompt gamma activation analysis) was used to measure the constituent element percentages and contaminants in a range of SU-8 samples subjected to different heat curing temperatures and UV cross-linking times. It was of interest to see how the O and H percentages changed as these are normally expected to vary depending on temperature and humidity. SANS (small angle neutron scattering) tests were also performed using a 10 MW reactor which measured sub-surface scattering for the same samples to reveal material defects.

Micromachining and low temperature co-fired ceramic (LTCC) technology have been widely used in modern radio-frequency (RF) and microwave systems. Both technologies provide the possibility to construct a three dimensional (3D) RF/Microwave circuits, which may have advantages in RF performance, size and power consumption when compared to conventional planar circuits. In this thesis, novel tightly-coupled microstrip and coplanar waveguide (CPW) transmission lines have been developed for LTCC bars and high-resistivity silicon (HRS) trenches fabrication processing technologies. The characteristics of the proposed 3D coupled lines have been analyzed by a quasi-static method and confirmed by electromagnetic (EM) simulation. Compared to existing tightly-coupled transmission line, they have a simple and compact layout but can also provide very tight coupling, good balance of even-odd mode phase velocities and high power handling capability. Using the proposed 3D coupled lines, an S-band quadrature hybrid coupler has been designed and fabricated. In the proof-of-concept device, the measured coupling coefficient is 3.61 dB and the insertion loss is 0.7 dB over a 60% fractional bandwidth. Utilizing the hybrid coupler, a single stage reflection-type phase shifter has been demonstrated. The phase shifter exhibited the true phase shifter frequency characteristics in the range of 2.5-3.5 GHz and the measured maximum relative phase shift is 120°. In addition, an ultra-wideband bandpass filter has been synthesized by the modified narrowband bandpass filter design equations and realized by the 3D coupled-lines. The measured fractional bandwidth is 130%, with only 0.7 dB mid-band insertion loss at 6.4 GHz and better than -20 dB return loss across the whole passband. The measured differential-phase differential-phase group delay is less than 200 ps from 2.6 to 10.1 GHz. Being a monolithic filter, it has a compact area of only 3.5 x 5.5 mm2.

Image-Based Rendering (IBR) is an effective technique for rendering novel views of a scene from multi-view images. The plenoptic function enables IBR to be formulated in terms of sampling and reconstruction. In this thesis, we combine the theoretical results from uniform plenoptic sampling with non-uniform camera placement. The central concept is that geometry of the scene can be modelled with a sequence of slanted planes. The positions of the cameras are then derived from the plenoptic spectral analysis of a slanted plane. To this end, we present novel results for the plenoptic spectral analysis of a slanted plane and an algorithm for adaptive plenoptic sampling. The novelty of our spectral analysis lies in the inclusion of two realistic conditions when calculating the plenoptic spectrum: finite scene width and cameras with finite field of view. Using these conditions, we derive an exact closed-form expression for the plenoptic spectrum of a slanted plane with bandlimited texture. From this spectrum, we determine an expression for the maximum spacing between adjacent cameras. Using synthetic and real scenes, we show that this expression is a more accurate gauge of the Nyquist sampling density than the current state-of-the-art. Based on these results, we design an adaptive plenoptic sampling algorithm for a scene with a smoothly varying surface and bandlimited texture. The algorithm operates by determining the best sequence of slanted planes to model the scene given its geometry and a limited number of cameras. Once this sequence of planes is obtained, the algorithm then positions the cameras using our sampling analysis of a slanted plane. Using synthetic and real scenes, we show that this algorithm outperforms uniform sampling. Finally, we also present a novel reconstruction filter for plenoptic sampling that outperforms the state-of-the-art for both synthetic and real scenes. The filter uses interpolators of maximum-order-minimal-support (MOMS).

In this thesis, we consider the problem of providing emergency support when existing communication infrastructure is unavailable. We propose using opportunistic communications (oppcomms) among mobile devices carried by civilians for the dissemination of emergency information. With oppcomms, devices exchange mes- sages at a close range of a few to tens of meters with limited or no infrastructure and messages are carried over multiple hops in a "store-carry-forward" manner by exploiting human mobility. We specifically look at the evacuation component of emergency response and propose an emergency support system (ESS) based on oppcomms to provide evacuation guidance to civilians in small-scale and large-scale urban emergencies in the absence of other means of communication. We evaluate the evacuation performance of ESS and investigate the communication characteristics of oppcomms for emergency support by simulation experiments. Our evaluations show that ESS improves evacuation by up to 31% and 14% compared to shortest path evacuation in large and small scale emergencies, respectively, and by up to 9% compared to a static-node based building evacuation system. We also investigate the resilience and security of oppcomms for emergency support under node failures and network attacks. We consider insider attacks where some nodes participating in oppcomms are compromised and misbehave. We investigate three different types of misbehaviour, including dropping packets, signal jamming and a hybrid attack on routing and evacuation that uses data falsification. Our evaluations show that node failures up to 20% are well-tolerated, and that data falsification has the most significant effect on evacuation by decreasing performance by up to 54%. In order to improve resilience of the system to such attacks, we propose a collaborative defense mechanism that combines identity-based cryptography and content-based message verification, and show that our defense mechanism improves performance by up to 50% in the presence of attacks.

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. However, the resource allocation problem in current networks has characteristics that turn the respective optimization problem into a non-convex one. First, current networks very often consist of a number of wireless links, whose capacity is not constant but follows Shannon capacity formula, which is a non-convex function. Second, the majority of the traffic in current networks is generated by multimedia applications, which are non-concave functions of rate. Third, current resource allocation methods follow the (bandwidth) proportional fairness policy, which when applied to networks shared by both concave and non-concave utilities leads to unfair resource allocations. These characteristics make current convex optimization frameworks inefficient in several aspects. This work aims to develop a non-convex optimization framework that will be able to allocate resources efficiently for non-convex resource allocation formulations. Towards this goal, a necessary and sufficient condition for the convergence of any primal-dual optimization algorithm to the optimal solution is proven. The wide applicability of this condition makes this a fundamental contribution for Optimization Theory in general. A number of optimization formulations are proposed, cases where this condition is not met are analysed and efficient alternative heuristics are provided to handle these cases. Furthermore, a novel multi-sigmoidal utility shape is proposed to model user satisfaction for multi-tiered multimedia applications more accurately. The advantages of such non-convex utilities and their effect in the optimization process are thoroughly examined. Alternative allocation policies are also investigated with respect to their ability to allocate resources fairly and deal with the non-convexity of the resource allocation problem. Specifically, the advantages of using Utility Proportional Fairness as an allocation policy are examined with respect to the development of distributed algorithms, their convergence to the optimal solution and their ability to adapt to the Quality of Service requirements of each application.