This thesis, after a review on the state-of-the-art sequencing and genotyping technologies, focuses on the semiconductor-based systems using pH change for DNA (Deoxyribonucleic acid) detection by ion-sensitive feld effect transistors (ISFETs). Accuracy and throughput, besides cost, are the key concerns in these systems, which are reflected on their signal-to-noise ratio and ability to process enormous measurement data at low levels for base-calling. Simulations are provided on the signal behaviour, supported by the literature review. The ISFETs have been investigated for their dimension and shape (single-plate vs mesh, and square vs octagonal). More complete formula and design methodology (to suppress the process variations and signal drift) have been provided for the ISFET operation by including the coupling effect. The experiment results, on 8 dies each containing 15 devices, showed the decoupling parasitics dependency on the sensing area perimeter. A buffer-shield structure has been proposed to improve the ISFET coupling. In addition, based on the ISFET drift analysis, measuring the biasing reference electrode current is recommended for the drift direction monitoring/prediction. Considering the two main applications of sequencing and genotyping, new readout configurations have been proposed to enhance the on-chip signal processing. Piecewise linear approximating (PLA), and temperature-insensitive continuous-time ΔpH to digital converter (TICTC), tackle the ISFET and temperature dependency. The TICTC has been designed for a resolution of 0.015pH, easily scalable and only dependent on the relative aspect ratio of its current mirrors. Its dynamic range is not limited despite operating in weak inversion. For very large-scale sequencing arrays, common-mode noise elimination using the back-gate has been proposed. It allows on-chip suppression of the background noise in the sequencing microchips, reducing the low-level processing load. Moreover, a pseudo-inverter-based readout has been designed that may allow improving the conversion resolution by current-mode comparison and indirect feedback to the ISFET gate.

More energy is consumed in the UK for heat than either transport or electricity and yet until recently little attention has been given to decarbonising heat to meet the UK's 2050 greenhouse gas targets. The challenges are immense as over 80% of households in the UK use gas for space and water heating. To achieve the UK's greenhouse gas targets will necessitate heat to be almost completely decarbonised and will thus require a transition from gas for heating to a low carbon alternative. However, there is a lack of consensus over which low carbon heat technologies householders should be encouraged to adopt as projections of these vary significantly. This thesis commences by reviewing those projections and identifying the possible reasons for the variations. Low carbon heat technologies suitable for large scale deployment are identified and a heat demand model developed from which demand profiles can be constructed. An integrated heat and electricity investment model is then developed which includes electricity generation assets but also district heating assets such as combined heat and power plant, network storage and large network heat pumps. A core input into this model is the heat demand profiles. The investment model enables the interaction between heat and electricity assets to be evaluated and so using scenarios combined with sensitivities examines the economics and carbon emissions of the low carbon residential heating technologies previously identified. Throughout this analysis the equivalent cost for gas heating is used as a comparator. The results suggest that district heating is an attractive option which is robust under most outcomes. However, its economic viability is crucially dependent on a financing regime that is compatible with other network based assets. Also identified is a role for electric storage heaters for buildings with low heat demand.

This thesis describes the trial design and analysis of the Low Carbon London (LCL) residential dynamic Time-of-Use (dToU) trial. This trial investigated the potential for dToU tariffs to deliver residential demand response to the Supplier, where it may contribute to system balancing through Supply Following (SF) actions, and to the distribution network operator (DNO), where it may be used for network Constraint Management (CM). 5,533 households from the London area participated in the trial and their consumption was measured at 30 minute resolution. 1,119 of these received the dToU tariff, which subjected them to CM and SF price events that were designed according to the specific requirements of these respective use cases. A novel, data driven, engagement ranking index was developed that allowed stratification of subsequent results into sets of the most engaged consumers, who may be indicative of a future populace that is more experienced/engaged in home energy management. Demand response (DR) was calculated relative to baseline model that used the dToU group mean demand as an input, with aggregate response levels calculated over a range of time, socio-economic and household occupancy related variables. Taking a network perspective, the reliability of CM event response was examined and two simple linear models presented as candidate predictors of response level, which was found to be consistent with an 8% reduction in demand. The network capacity contribution of residential DR was theorised to consist of two components: 'mean response' and 'variance response', and the real impact of these was investigated using the LCL gathered data. Potential risks to the network from low price induced demand spikes were explored empirically using the SF event data and the times of highest risk were identified. The extensive metadata set gathered from trial participants was processed into some 200 numerical variables. A correlation analysis was performed which was visualised using weighted correlation network graphs. A number of parameters were found to predict response level, but responsiveness (the level of deliberate engagement) could only be reliably measured by engagement rank.

Inelastic Electron Tunnelling Spectroscopy (IETS) [1-5] provides a means to characterise the phonon spectrum of a molecule by measuring the phonon-assisted tunnelling current through a potential barrier impregnated with target molecules. Traditionally, this technique has used Metal - Insulator - Metal (MIM) junctions, and the molecules of interest are adsorbed on to the insulator during junction fabrication. At low applied voltage V, tunnelling through the barrier is elastic. However, inelastic tunnelling caused by electron interaction with vibrational states in the adsorbed molecules can create additional conduction channels, occurring when V reaches a value of hω/e, where ω is a molecular vibrational mode. These lead to peaks in the d^2 I / dV^2 vs. V characteristics for each additional channel, giving a spectrum of the molecular vibration modes. As energy separations in the vibrational spectrum are relatively small compared to the electronic spectrum, the full vibrational spectrum is measured only at T < 30K. However, it may be possible to measure part of the spectrum even at room temperature, raising the possibility of a molecular detector. This project is concerned with fabricating nanoscale tunnel junctions based on Si nanowires (NWs) made by electron-beam lithography (EBL), for the purpose of IETS measurements, at 300K. A Si/SiO2 tunnel barrier/Al structure is used, where the Al NW crosses an oxidised Si NW. This allows the fabrication of tunnel junctions down to 50nm x 120nm in area and tunnelling occurs across a 10nm thick SiO2 layer. The reduction in device dimensions to the nanoscale may increase the sensitivity of the device to molecules adsorbed on the tunnel junction. Furthermore, the use of Silicon on insulator (SOI) material allows modulation of the tunnel junction using the back gate formed by the SOI substrate, control the Fermi energy and electron concentration in the NW, and hence the IETS characteristics of the device. In principle, an IETS sensor may be possible using such a configuration. In principle, a switchable IETS measurements are performed at 300K for ammonium hydroxide (NH4OH), acetic acid (CH3COOH), and propionic acid (C3H6O2) molecules. The I-V , dI/dV - V , and d2^I/dV^2-V characteristics of the tunnel junction are measured before and after the adsorption of molecules on the junction using vapour treatment or immersion. Peaks can be observed in the d^2I/dV^2-V characteristics in all the cases following molecules adsorption. These peaks may be attributed to vibrational modes of N-H and C-H bonds. Simulation of IETS characteristics modelled based on a combination of elastic, inelastic tunnelling and Schottky barriers at the Si / SiO2 / Al interface in the device. A comparison has been made between the simulation results and experimental measurements which showed very good agreement. This device modelling can be used to predict experimental characteristics and allow thermal broadening of the IETS peaks to be investigated.

Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster-Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.

The thermal (emitted) infrared frequency bands, from 20 to 40 THz and 60 to 100 THz, are best known for applications in thermography. This underused and unregulated part of the spectral range offers opportunities for the development of secure communications. The 'THz Torch' concept, operating between the THz and mid-infrared ranges, was recently introduced. This technology fundamentally exploits engineered blackbody radiation, by partitioning thermally-generated spectral power into pre-defined frequency channels; the energy in each channel is then independently pulsed modulated to create a robust form of short-range secure communications in the far/mid-infrared. In the thesis, the development of 'THz Torch' wireless communications systems will first be introduced. State-of-the-art THz technologies, infrared sources and detectors, as well as near-infrared and visible light communications technologies, will be reviewed in Chapter 2. Basic single-channel architecture of the 'THz Torch' technology will be presented in Chapter 3. Fundamental limits for the first single-channel proof-of-concept demonstrator will be discussed, and possible engineering solutions will be proposed and verified experimentally. With such improvements, to date, octave bandwidth (25 to 50 THz) single-channel wireless links have been demonstrated with >2 kbit/s data rate and >10 cm transmission distance. To further increase the overall end-to-end data rate and/or the level of security, multiplexing schemes for 'THz Torch' technologies are proposed in Chapter 4. Both frequency division multiplexing (FDM) and frequency-hopping spread-spectrum (FHSS) working demonstrators, operating between 10 and 100 THz spectral range, will be implemented. With such 4-channel multiplexing schemes, measured bit error rates (BERs) of < 10⁻⁶ have been achieved over a transmission distance of 2.5 cm. Moreover, the integrity of such 4-channel multiplexing system is evaluated by introducing four jamming, interception and channel crosstalk experiments. Chapter 5 gives a detailed power link budget analysis for the 4-channel multiplexing system. The design, simulation and measurement of scalable THz metal mesh filters, which have potential applications for multi-channel 'THz Torch' technology, will be presented in Chapter 6. The conclusions and further work are summarised in the last chapter. It is expected that this thermodynamics-based approach represents a new paradigm in the sense that 19th century physics can be exploited with 20th century multiplexing concepts for low cost 21st century ubiquitous security and defence applications in the thermal infrared range.

The work described in this thesis consists essentially of two parts. The first is the design and development of/ ä rocket-borne V. L. P. receiver for the observation of the magnetic field component of electromagnetic waves in the frequency range 100 to 20,000 Hz. The author's particular contribution was/ the optimisation of the design of a smallμ-metal rod aerial. The approach was necessarily partly empirical, partly theoretical. The former involved the systematic adjustment of the number of turns, coil geometry, dimensions and type of wire, etc. for the particular rod length small enough to be flown in the space available in the rocket nose-cone. This was complemented by a theoretical approach involving the rod and equivalent circuits which satisfactorily reproduced the measured frequency response and signal/noise properties of the aerial and preamplifier. The second part of the thesis is concerned with the analysis and interpretation of discrete V. L. F. emissions using the phenomenological theory of Helliwell. Both medium and high latitude emissions were analysed in detail. Significant advances were made in the computer iteration techniques applied to the analysis of these emissions and limitations in the analytical methods used were investigated in great detail. Particularly interesting were several "slow risers" recorded at Andoya (L = 6.6) in northern Norway. The analysis determined the time varying energy spectrum of the phase-bunched electrons giving rise to the emission in the vicinity of the earth's equator (- 6.6 earth radii) for a rangeof possible equatorial ambient electron densities. It was possible to determine an upper limit to the electron density at the equator which, since it was in excellent agreement with that (2 to 5 electrons/ cc) determined by more direct measurements, provided new experimental support for the theory. The limitations in the theory were examined and a small error in the original formula of Helliwell corrected.

This thesis optimizes the vacuum, vapour-phase self-assembly of n-octyl phosphonic acid monolayer. This monolayer is chemisorbed to the aluminium oxide (AIOx) and together they form an ultra-thin gate dielectric in organic thin-film transistors based on pentacene. The electrical measurements of the transistors and the corresponding metal-insulator-metal structures were combined with the characterization of n-octylphosphonic acid monolayers using the atomic force microscopy, water contact angle measurement and Fourier transform infrared spectroscopy. The results show that the properties of the organic monolayer depend on its evaporation rate, growth temperature and the post-growth annealing and affect the performance of the as-fabricated transistors as well as the transistor bias-induced instability.

This project aims to tackle some of the fundamental issues with modelling the drivetrain of the wind turbine. Reliability data shows that gearboxes and generators have higher failure rates than expected, in particular the high speed bearings which are shown to be most problematic. Models of gearboxes are generally isolated from the rest of the structure and loading applied at boundaries to the model. This may not adequately represent conditions that bearings encounter and in certain cases bearings fail due to under prediction of loads. A full drivetrain model is created showing how flexibility of certain subcomponents affects loading on other parts of the drivetrain. Stiffness of the gearbox mounts, the bedplate and the coupling is revealed to have a large effect on the loading that high speed bearings experience. Transient loading is also examined to discover if this has a significant effect on high speed bearing loading. Static models have been used to analyse drivetrains using predicted inputs applied at the boundaries but this cannot include any contributions from the structural modes. Transient events such as a grid loss emergency stop are performed on a model of a 2 MW wind turbine and sensitivities of the control strategies are found in relation to LSS torque and rotor speed. Findings show that static models are able to predict general motion of the drivetrain structure so long as the response follows the input conditions; transient models of the drivetrain showed significant differences in the response between loading predicted by static models and transient models. Finally, medium speed drivetrains are analysed showing how design of the drivetrain can reduce the susceptibility of the drivetrain bearings to vibrational modes of the structure. Findings show problematic bearings are removed and drivetrain bearings become insensitive to loading concluding that medium speed drivetrains could improve reliability.

Centralized model predictive control (MPC) is often considered impractical, inflexible and unsuitable for controlling large-scale systems due to several factors such as large computational effort and difficulty to meet all operational objectives. Therefore, industrial large-scale systems are usually controlled by a distributed control framework. In this thesis, novel sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to reduce the complexity of solving optimization problem. In this distributed framework, the overall system is divided into several interconnected subsystems and each subsystem is controlled by local MPC. These local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global performance. The proposed algorithms are applied to an industrial power plant model to improve power generation efficiency. A non-linear dynamic model of Combined Cycle Power Plant (CCPP) using the laws of physics was first developed and simulated using decentralized PID controllers. Then, a supervisory controller using linear constrained MPC was designed to tune the performance of the PID controllers. Next, a supervisory centralized nonlinear model predictive control (NMPC) algorithm based on state-dependent models was developed to control the nonlinear plant over a wide operating range. Finally, two sequential DMPC algorithms based on state-dependent models were developed. The lack of states measurement were handled by designing nonlinear distributed state estimation algorithms using state-dependent differential Riccati equation (SDDRE) Kalman filter. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.