The Synthesis of Arbitrary Stable Dynamics in Non-linear Neural Networks II: Feedback and Universality

We wish to construct a realization theory of stable neural networks and use this theory to model the variety of stable dynamics apparent in natural data. Such a theory should have numerous applications to constructing specific artificial neural networks with desired dynamical behavior. The networks used in this theory should have well understood dynamics yet be as diverse as possible to capture natural diversity. In this article, I describe a parameterized family of higher order, gradient-like neural networks which have known arbitrary equilibria with unstable manifolds of known specified dimension. Moreover, any system with hyperbolic dynamics is conjugate to one of these systems in a neighborhood of the equilibrium points. Prior work on how to synthesize attractors using dynamical systems theory, optimization, or direct parametric. fits to known stable systems, is either non-constructive, lacks generality, or has unspecified attracting equilibria. More specifically, We construct a parameterized family of gradient-like neural networks with a simple feedback rule which will generate equilibrium points with a set of unstable manifolds of specified dimension. Strict Lyapunov functions and nested periodic orbits are obtained for these systems and used as a method of synthesis to generate a large family of systems with the same local dynamics. This work is applied to show how one can interpolate finite sets of data, on nested periodic orbits.

Addressing non-linear hardware limitations and extending network coverage area for power aware wireless sensor networks

Science Foundation Ireland (07/CE/11147); Irish Research Council for Science Engineering and Technology (Embark Initiative)

Computing explanations for interactive constraint-based systems

Constraint programming has emerged as a successful paradigm for modelling combinatorial problems arising from practical situations. In many of those situations, we are not provided with an immutable set of constraints. Instead, a user will modify his requirements, in an interactive fashion, until he is satisfied with a solution. Examples of such applications include, amongst others, model-based diagnosis, expert systems, product configurators. The system he interacts with must be able to assist him by showing the consequences of his requirements. Explanations are the ideal tool for providing this assistance. However, existing notions of explanations fail to provide sufficient information. We define new forms of explanations that aim to be more informative. Even if explanation generation is a very hard task, in the applications we consider, we must manage to provide a satisfactory level of interactivity and, therefore, we cannot afford long computational times. We introduce the concept of representative sets of relaxations, a compact set of relaxations that shows the user at least one way to satisfy each of his requirements and at least one way to relax them, and present an algorithm that efficiently computes such sets. We introduce the concept of most soluble relaxations, maximising the number of products they allow. We present algorithms to compute such relaxations in times compatible with interactivity, achieving this by indifferently making use of different types of compiled representations. We propose to generalise the concept of prime implicates to constraint problems with the concept of domain consequences, and suggest to generate them as a compilation strategy. This sets a new approach in compilation, and allows to address explanation-related queries in an efficient way. We define ordered automata to compactly represent large sets of domain consequences, in an orthogonal way from existing compilation techniques that represent large sets of solutions.

Gröbner basis techniques for certain problems in coding and systems theory

There is much common ground between the areas of coding theory and systems theory. Fitzpatrick has shown that a Göbner basis approach leads to efficient algorithms in the decoding of Reed-Solomon codes and in scalar interpolation and partial realization. This thesis simultaneously generalizes and simplifies that approach and presents applications to discrete-time modeling, multivariable interpolation and list decoding. Gröbner basis theory has come into its own in the context of software and algorithm development. By generalizing the concept of polynomial degree, term orders are provided for multivariable polynomial rings and free modules over polynomial rings. The orders are not, in general, unique and this adds, in no small way, to the power and flexibility of the technique. As well as being generating sets for ideals or modules, Gröbner bases always contain a element which is minimal with respect tot the corresponding term order. Central to this thesis is a general algorithm, valid for any term order, that produces a Gröbner basis for the solution module (or ideal) of elements satisfying a sequence of generalized congruences. These congruences, based on shifts and homomorphisms, are applicable to a wide variety of problems, including key equations and interpolations. At the core of the algorithm is an incremental step. Iterating this step lends a recursive/iterative character to the algorithm. As a consequence, not all of the input to the algorithm need be available from the start and different "paths" can be taken to reach the final solution. The existence of a suitable chain of modules satisfying the criteria of the incremental step is a prerequisite for applying the algorithm.

Spatial and energetic mode dynamics of cold atomic systems

In this thesis we relate the formal description of various cold atomic systems in the energy eigenbasis, to the observable spatial mode dynamics. Herein the `spatial mode dynamics' refers to the direction of photon emission following the spontaneous emission of an excited fermion in the presence of a same species and spin ideal anisotropic Fermi sea in its internal ground state. Due to the Pauli principle, the presence of the ground state Fermi sea renders the phase space, anisotropic and only partially accessible, thereby a ecting the direction of photon emission following spontaneous emission. The spatial and energetic mode dynamics also refers to the quantum `tunneling' interaction between localised spatial modes, synonymous with double well type potentials. Here we relate the dynamics of the wavefunction in both the energetic and spatial representations. Using this approach we approximate the relationship between the spatial and energetic representations of a wavefunction spanning three spatial and energetic modes. This is extended to a process known as Spatial Adiabatic Passage, which is a technique to transport matter waves between localised spatial modes. This approach allows us to interpret the transport of matter waves as a signature of a geometric phase acquired by the one of the internal energy eigenstates of the system during the cyclical evolution. We further show that this geometric phase may be used to create spatial mode qubit and qutrit states.

Energy storage systems for wave energy converters and microgrids

The thesis initially gives an overview of the wave industry and the current state of some of the leading technologies as well as the energy storage systems that are inherently part of the power take-off mechanism. The benefits of electrical energy storage systems for wave energy converters are then outlined as well as the key parameters required from them. The options for storage systems are investigated and the reasons for examining supercapacitors and lithium-ion batteries in more detail are shown. The thesis then focusses on a particular type of offshore wave energy converter in its analysis, the backward bent duct buoy employing a Wells turbine. Variable speed strategies from the research literature which make use of the energy stored in the turbine inertia are examined for this system, and based on this analysis an appropriate scheme is selected. A supercapacitor power smoothing approach is presented in conjunction with the variable speed strategy. As long component lifetime is a requirement for offshore wave energy converters, a computer-controlled test rig has been built to validate supercapacitor lifetimes to manufacturer’s specifications. The test rig is also utilised to determine the effect of temperature on supercapacitors, and determine application lifetime. Cycle testing is carried out on individual supercapacitors at room temperature, and also at rated temperature utilising a thermal chamber and equipment programmed through the general purpose interface bus by Matlab. Application testing is carried out using time-compressed scaled-power profiles from the model to allow a comparison of lifetime degradation. Further applications of supercapacitors in offshore wave energy converters are then explored. These include start-up of the non-self-starting Wells turbine, and low-voltage ride-through examined to the limits specified in the Irish grid code for wind turbines. These applications are investigated with a more complete model of the system that includes a detailed back-to-back converter coupling a permanent magnet synchronous generator to the grid. Supercapacitors have been utilised in combination with battery systems for many applications to aid with peak power requirements and have been shown to improve the performance of these energy storage systems. The design, implementation, and construction of coupling a 5 kW h lithium-ion battery to a microgrid are described. The high voltage battery employed a continuous power rating of 10 kW and was designed for the future EV market with a controller area network interface. This build gives a general insight to some of the engineering, planning, safety, and cost requirements of implementing a high power energy storage system near or on an offshore device for interface to a microgrid or grid.

Development and assessment of new sensor systems in food packaging applications

The use of optical sensor technology for non-invasive determination of key quality pack parameters improved package/product quality. This technology can be used for optimization of packaging processes, improvement of product shelf-life and maintenance of quality. In recent years, there has been a major focus on O2 and CO2 sensor development as these are key gases used in modified atmosphere packaging (MAP) of food. The first and second experimental chapters (chapter 2 and 3) describe the development of O2, pH and CO2 solid state sensors and its (potential) use for food packaging applications. A dual-analyte sensor for dissolved O2 and pH with one bi-functional reporter dye (meso-substituted Pd- or Ptporphyrin) embedded in plasticized PVC membrane was developed in chapter 2. The developed CO2 sensor in chapter 3 was comprised of a phosphorescent reporter dye Pt(II)- tetrakis(pentafluorophenyl) porphyrin (PtTFPP) and a colourimetric pH indicator α-naphtholphthalein (NP) incorporated in a plastic matrix together with a phase transfer agent tetraoctyl- or cetyltrimethylammonium hydroxide (TOA-OH or CTA-OH). The third experimental chapter, chapter 4, described the development of liquid O2 sensors for rapid microbiological determination which are important for improvement and assurance of food safety systems. This automated screening assay produced characteristic profiles with a sharp increase in fluorescence above the baseline level at a certain threshold time (TT) which can be correlated with their initial microbial load and was applied to various raw fish and horticultural samples. Chapter 5, the fourth experimental chapter, reported upon the successful application of developed O2 and CO2 sensors for quality assessment of MAP mushrooms during storage for 7 days at 4°C.

The contribution of enterprise systems to core capabilities: the case of asset lifecycle management in the utilities sector

The desire to obtain competitive advantage is a motivator for implementing Enterprise Resource Planning (ERP) Systems (Adam & O’Doherty, 2000). However, while it is accepted that Information Technology (IT) in general may contribute to the improvement of organisational performance (Melville, Kraemer, & Gurbaxani, 2004), the nature and extent of that contribution is poorly understood (Jacobs & Bendoly, 2003; Ravichandran & Lertwongsatien, 2005). Accordingly, Henderson and Venkatraman (1993) assert that it is the application of business and IT capabilities to develop and leverage a firm’s IT resources for organisational transformation, rather than the acquired technological functionality, that secures competitive advantage for firms. Application of the Resource Based View of the firm (Wernerfelt, 1984) and Dynamic Capabilities Theory (DCT) (Teece and Pisano (1998) in particular) may yield insights into whether or not the use of Enterprise Systems enhances organisations’ core capabilities and thereby obtains competitive advantage, sustainable or otherwise (Melville et al., 2004). An operational definition of Core Capabilities that is independent of the construct of Sustained Competitive Advantage is formulated. This Study proposes and utilises an applied Dynamic Capabilities framework to facilitate the investigation of the role of Enterprise Systems. The objective of this research study is to investigate the role of Enterprise Systems in the Core Dynamic Capabilities of Asset Lifecycle Management. The Study explores the activities of Asset Lifecycle Management, the Core Dynamic Capabilities inherent in Asset Lifecycle Management and the footprint of Enterprise Systems on those Dynamic Capabilities. Additionally, the study explains the mechanisms by which Enterprise Systems sustain the Exploitability and the Renewability of those Core Dynamic Capabilities. The study finds that Enterprise Systems contribute directly to the Value, Exploitability and Renewability of Core Dynamic Capabilities and indirectly to their Inimitability and Non-substitutability. The study concludes by presenting an applied Dynamic Capabilities framework, which integrates Alter (1992)’s definition of Information Systems with Teece and Pisano (1998)’s model of Dynamic Capabilities to provide a robust diagnostic for determining the sustained value generating contributions of Enterprise Systems. These frameworks are used in the conclusions to frame the findings of the study. The conclusions go on to assert that these frameworks are free - standing and analytically generalisable, per Siggelkow (2007) and Yin (2003).

Correlations in low dimensional quantum systems

In this thesis I present the work done during my PhD in the area of low dimensional quantum gases. The chapters of this thesis are self contained and represent individual projects which have been peer reviewed and accepted for publication in respected international journals. Various systems are considered, the first of which is a two particle model which possesses an exact analytical solution. I investigate the non-classical correlations that exist between the particles as a function of the tunable properties of the system. In the second work I consider the coherences and out of equilibrium dynamics of a one-dimensional Tonks-Girardeau gas. I show how the coherence of the gas can be inferred from various properties of the reduced state and how this may be observed in experiments. I then present a model which can be used to probe a one-dimensional Fermi gas by performing a measurement on an impurity which interacts with the gas. I show how this system can be used to observe the so-called orthogonality catastrophe using modern interferometry techniques. In the next chapter I present a simple scheme to create superposition states of particles with special emphasis on the NOON state. I explore the effect of inter-particle interactions in the process and then characterise the usefulness of these states for interferometry. Finally I present my contribution to a project on long distance entanglement generation in ion chains. I show how carefully tuning the environment can create decoherence-free subspaces which allows one to create and preserve entanglement.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Modified cyclodextrins as novel non-viral vectors for neuronal siRNA delivery: focus on Huntington’s disease

Huntington’s Disease (HD) is a rare autosomal dominant neurodegenerative disease caused by the expression of a mutant Huntingtin (muHTT) protein. Therefore, preventing the expression of muHTT by harnessing the specificity of the RNA interference (RNAi) pathway is a key research avenue for developing novel therapies for HD. However, the biggest caveat in the RNAi approach is the delivery of short interfering RNA (siRNAs) to neurons, which are notoriously difficult to transfect. Indeed, despite the great advances in the field of nanotechnology, there remains a great need to develop more effective and less toxic carriers for siRNA delivery to the Central Nervous System (CNS). Thus, the aim of this thesis was to investigate the utility of modified amphiphilic β-cyclodextrins (CDs), oligosaccharide-based molecules, as non-viral vectors for siRNA delivery for HD. Modified CDs were able to bind and complex siRNAs forming nanoparticles capable of delivering siRNAs to ST14A-HTT120Q cells and to human HD fibroblasts, and reducing the expression of the HTT gene in these in vitro models of HD. Moreover, direct administration of CD.siRNA nanoparticles into the R6/2 mouse brain resulted in significant HTT gene expression knockdown and selective alleviation of rotarod motor deficits in this mouse model of HD. In contrast to widely used transfection reagents, CD.siRNA nanoparticles only induced limited cytotoxic and neuroinflammatory responses in multiple brain-derived cell-lines, and also in vivo after single direct injections into the mouse brain. Alternatively, we have also described a PEGylation-based formulation approach to further stabilise CD.siRNA nanoparticles and progress towards a systemic delivery nanosystem. Resulting PEGylated CD.siRNA nanoparticles showed increased stability in physiological saltconditions and, to some extent, reduced protein-induced aggregation. Taken together, the work outlined in this thesis identifies modified CDs as effective, safe and versatile siRNA delivery systems that hold great potential for the treatment of CNS disorders, such as HD.

Use of oxygen sensors for the non destructive measurement of oxygen in packaged food and beverage products and its impact on product quality and shelf life

The principal objective of this thesis was to investigate the ability of reversible optical O2 sensors to be incorporated into food/beverage packaging systems to continuously monitor O2 levels in a non-destructive manner immediately postpackaging and over time. Residual levels of O2 present in packs can negatively affect product quality and subsequently, product shelf-life, especially for O2-sensitive foods/beverages. Therefore, the ability of O2 sensors to continuously monitor O2 levels present within food/beverage packages was considered commercially relevant in terms of identifying the consequences of residual O2 on product safety and quality over time. Research commenced with the development of a novel range of O2 sensors based on phosphorescent platinum and palladium octaethylporphyrin-ketones (OEPk) in nano-porous high density polyethylene (HDPE), polypropylene (PP) polytetrafluoroethylene (PTFE) polymer supports. Sensors were calibrated over a temperature range of -10°C to +40°C and deemed suitable for food and beverage packaging applications. This sensor technology was used and demonstrated itself effective in determining failures in packaging containment. This was clearly demonstrated in the packaging of cheese string products. The sensor technology was also assessed across a wide range of packaged products; beer, ready-to-eat salad products, bread and convenience-style, muscle-based processed food products. The O2 sensor technology performed extremely well within all packaging systems. The sensor technology adequately detected O2 levels in; beer bottles prior to and following pasteurisation, modified atmosphere (MA) packs of ready-to-eat salad packs as respiration progressed during product storage and MA packs of bread and convenience-style muscle-based products as mycological growth occurred in food packs over time in the presence and absence of ethanol emitters. The use of the technology, in conjunction with standard food quality assessment techniques, showed remarkable usefulness in determining the impact of actual levels of O2 on specific quality attributes. The O2 sensing probe was modified, miniaturised and automated to screen for the determination of total aerobic viable counts (TVC) in several fish species samples. The test showed good correlation with conventional TVC test (ISO:4833:2003), analytical performance and ruggedness with respect to variation of key assay parameters (probe concentration and pipetting volume). Overall, the respirometric fish TVC test was simple to use, possessed a dynamic microbial range (104-107 cfu/g sample), had an accuracy of +/- one log(cfu/g sample) and was rapid. Its ability to assess highly perishable products such as fish for total microbial growth in <12 hr demonstrates commercial potential.

Photoelectrocatalysis: Principles, nanoemitter applications and routes to bio-inspired systems

An overview on processes that are relevant in light-induced fuel generation, such as water photoelectrolysis or carbon dioxide reduction, is given. Considered processes encompass the photophysics of light absorption, excitation energy transfer to catalytically active sites and interfacial reactions at the catalyst/solution phase boundary. The two major routes envisaged for realization of photoelectrocatalytic systems, e.g. bio-inspired single photon catalysis and multiple photon inorganic or hybrid tandem cells, are outlined. For development of efficient tandem cell structures that are based on non-oxidic semiconductors, stabilization strategies are presented. Physical surface passivation is described using the recently introduced nanoemitter concept which is also applicable in photovoltaic (solid state or electrochemical) solar cells and first results with p-Si and p-InP thin films are presented. Solar-to-hydrogen efficiencies reach 12.1% for homoepitaxial InP thin films covered with Rh nanoislands. In the pursuit to develop biologically inspired systems, enzyme adsorption onto electrochemically nanostructured silicon surfaces is presented and tapping mode atomic force microscopy images of heterodimeric enzymes are shown. An outlook towards future envisaged systems is given. © 2010 The Royal Society of Chemistry.

Reformulating time-dependent density functional theory with non-orthogonal localized molecular orbitals.

Time-dependent density functional theory (TDDFT) has broad application in the study of electronic response, excitation and transport. To extend such application to large and complex systems, we develop a reformulation of TDDFT equations in terms of non-orthogonal localized molecular orbitals (NOLMOs). NOLMO is the most localized representation of electronic degrees of freedom and has been used in ground state calculations. In atomic orbital (AO) representation, the sparsity of NOLMO is transferred to the coefficient matrix of molecular orbitals (MOs). Its novel use in TDDFT here leads to a very simple form of time propagation equations which can be solved with linear-scaling effort. We have tested the method for several long-chain saturated and conjugated molecular systems within the self-consistent charge density-functional tight-binding method (SCC-DFTB) and demonstrated its accuracy. This opens up pathways for TDDFT applications to large bio- and nano-systems.

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.