An investigation into the network structures of eThylene propylene terpolymer elastomers

A series of ethylene propylene terpolymer vulcanizates, prepared by varying termonomer type, cure system, cure time and cure temperature, are characterized by determining the number and type of cross-links present. The termonomers used represent the types currently available in commercial quantities. Characterization is carried out by measuring the C1 constant of the Mooney Rivlin Saunders equation before and after treatment with the chemical probes propane-2-thiol/piperidine and n-hexane thiol/piperidine, thus making it possible to calculate the relative proportions of mono-sulphidic, di-sulphidic and poly- sulphidic cross-links. The cure systems used included both sulphur and peroxide formulations. Specific physical properties are determined for each network and an attempt is made to correlate observed changes in these with variations in network structure. A survey of the economics of each formulation based on a calculated efficiency parameter for each cure system is included. Values of C1 are calculated from compression modulus data after the reliability of the technique when used with ethylene propylene terpolymers had been established. This is carried out by comparing values from both compression and extension stress strain measurements for natural rubber vulcanizates and by assessing the effects of sample dimensions and the degree of swelling. The technique of compression modulus is much more widely applicable than previously thought. The basic structure of an ethylene propylene terpolymer network appears to be independent of the type of cure system used ( sulphur based systems only), the proportions of constituent cross-links being nearly constant.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Parallel implementation of image segmentation algorithms on a network of transputers

Image segmentation is one of the most computationally intensive operations in image processing and computer vision. This is because a large volume of data is involved and many different features have to be extracted from the image data. This thesis is concerned with the investigation of practical issues related to the implementation of several classes of image segmentation algorithms on parallel architectures. The Transputer is used as the basic building block of hardware architectures and Occam is used as the programming language. The segmentation methods chosen for implementation are convolution, for edge-based segmentation; the Split and Merge algorithm for segmenting non-textured regions; and the Granlund method for segmentation of textured images. Three different convolution methods have been implemented. The direct method of convolution, carried out in the spatial domain, uses the array architecture. The other two methods, based on convolution in the frequency domain, require the use of the two-dimensional Fourier transform. Parallel implementations of two different Fast Fourier Transform algorithms have been developed, incorporating original solutions. For the Row-Column method the array architecture has been adopted, and for the Vector-Radix method, the pyramid architecture. The texture segmentation algorithm, for which a system-level design is given, demonstrates a further application of the Vector-Radix Fourier transform. A novel concurrent version of the quad-tree based Split and Merge algorithm has been implemented on the pyramid architecture. The performance of the developed parallel implementations is analysed. Many of the obtained speed-up and efficiency measures show values close to their respective theoretical maxima. Where appropriate comparisons are drawn between different implementations. The thesis concludes with comments on general issues related to the use of the Transputer system as a development tool for image processing applications; and on the issues related to the engineering of concurrent image processing applications.

A simulation study of a localised computer network

An investigation is carried out into the design of a small local computer network for eventual implementation on the University of Aston campus. Microprocessors are investigated as a possible choice for use as a node controller for reasons of cost and reliability. Since the network will be local, high speed lines of megabit order are proposed. After an introduction to several well known networks, various aspects of networks are discussed including packet switching, functions of a node and host-node protocol. Chapter three develops the network philosophy with an introduction to microprocessors. Various organisations of microprocessors into multicomputer and multiprocessor systems are discussed, together with methods of achieving reliabls computing. Chapter four presents the simulation model and its implentation as a computer program. The major modelling effort is to study the behaviour of messages queueing for access to the network and the message delay experienced on the network. Use is made of spectral analysis to determine the sampling frequency while Sxponentially Weighted Noving Averages are used for data smoothing.

OTDM network processing using all-optical and electro-optical devices

The current optical communications network consists of point-to-point optical transmission paths interconnected with relatively low-speed electronic switching and routing devices. As the demand for capacity increases, then higher speed electronic devices will become necessary. It is however hard to realise electronic chip-sets above 10 Gbit/s, and therefore to increase the achievable performance of the network, electro-optic and all-optic switching and routing architectures are being investigated. This thesis aims to provide a detailed experimental analysis of high-speed optical processing within an optical time division multiplexed (OTDM) network node. This includes the functions of demultiplexing, 'drop and insert' multiplexing, data regeneration, and clock recovery. It examines the possibilities of combining these tasks using a single device. Two optical switching technologies are explored. The first is an all-optical device known as 'semiconductor optical amplifier-based nonlinear optical loop mirror' (SOA-NOLM). Switching is achieved by using an intense 'control' pulse to induce a phase shift in a low-intensity signal propagating through an interferometer. Simultaneous demultiplexing, data regeneration and clock recovery are demonstrated for the first time using a single SOA-NOLM. The second device is an electroabsorption (EA) modulator, which until this thesis had been used in a uni-directional configuration to achieve picosecond pulse generation, data encoding, demultiplexing, and 'drop and insert' multiplexing. This thesis presents results on the use of an EA modulator in a novel bi-directional configuration. Two independent channels are demultiplexed from a high-speed OTDM data stream using a single device. Simultaneous demultiplexing with stable, ultra-low jitter clock recovery is demonstrated, and then used in a self-contained 40 Gbit/s 'drop and insert' node. Finally, a 10 GHz source is analysed that exploits the EA modulator bi-directionality to increase the pulse extinction ratio to a level where it could be used in an 80 Gbit/s OTDM network.

Beta frequency neuronal network activity in the primary motor cortex

In this study I investigated the mechanisms of neuronal network oscillatory activity in rat M1 using pharmacological manipulations and electrical stimulation protocols, employing the in vitro brain slice technique in rat and magnetoencephalography (MEG) in man. Co-application of kainic acid and carbachol generated in vitro beta oscillatory activity in all layers in M1. Analyses indicated that oscillations originated from deep layers and indicated significant involvement of GABAA receptors and gap junctions. A modulatory role of GABAB, NMDA, and dopamine receptors was also evident. Intracellular recordings from fast-spiking (FS) GABAergic inhibitory cells revealed phase-locked action potentials (APs) on every beta cycle. Glutamatergic excitatory regular-spiking (RS) and intrinsically-bursting (IB) cells both received phase locked inhibitory postsynaptic potentials, but did not fire APs on every cycle, suggesting the dynamic involvement of different pools of neurones in the overall population oscillations. Stimulation evoked activity at high frequency (HFS; 125Hz) evoked gamma oscillations and reduced ongoing beta activity. 20Hz stimulation promoted theta or gamma oscillations whilst 4Hz stimulation enhanced beta power at theta frequency. I also investigated the modulation of pathological slow wave (theta and beta) oscillatory activity using magnetoencephalography. Abnormal activity was suppressed by sub-sedative doses of GABAA receptor modulator zolpidem and the observed desynchronising effect correlated well with improved sensorimotor function. These studies indicate a fundamental role for inhibitory neuronal networks in the patterning beta activity and suggest that cortical HFS in PD re-patterns abnormally enhanced M1 network activity by modulating the activity of FS cells. Furthermore, pathological oscillation may be common to many neuropathologies and may be an important future therapeutic target.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.

Analyzing MSI rules for the USA extracted from a feedforward neural network

This paper introduces a mechanism for generating a series of rules that characterize the money price relationship for the USA, defined as the relationship between the rate of growth of the money supply and inflation. Monetary component data is used to train a selection of candidate feedforward neural networks. The selected network is mined for rules, expressed in human-readable and machine-executable form. The rule and network accuracy are compared, and expert commentary is made on the readability and reliability of the extracted rule set. The ultimate goal of this research is to produce rules that meaningfully and accurately describe inflation in terms of the monetary component dataset.

Deschooling society? A lifelong learning network for sustainable communities, urban regeneration and environmental technologies

The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make social learning effective. Such a conception informs the new Lifelong Learning Network Consortium on Sustainable Communities, Urban Regeneration and Environmental Technologies established and led by the Lifelong Learning Centre at Aston University. This paper offers a radical, reflective and political evaluation of its first year in development arguing that networked learning of this type could prefigure a new model for lifelong learning and sustainable education that renders the city itself a creative medium for transformative learning and sustainability.

Municipal benchmarking:organisational learning and network performance in the public sector

The main purpose of this dissertation is to assess the relation between municipal benchmarking and organisational learning with a specific emphasis on benchlearning and performance within municipalities and between groups of municipalities in the building and housing sector in the Netherlands. The first and main conclusion is that this relation exists, but that the relative success of different approaches to dimensions of change and organisational learning are a key explanatory factor for differences in the success of benchlearning. Seven other important conclusions could be derived from the empirical research. First, a combination of interpretative approaches at the group level with a mixture of hierarchical and network strategies, positively influences benchlearning. Second, interaction among professionals at the inter-organisational level strengthens benchlearning. Third, stimulating supporting factors can be seen as a more important strategy to strengthen benchlearning than pulling down barriers. Fourth, in order to facilitate benchlearning, intrinsic motivation and communication skills matter, and are supported by a high level of cooperation (i.e., team work), a flat organisational structure and interactions between individuals. Fifth, benchlearning is facilitated by a strategy that is based on a balanced use of episodic (emergent) and systemic (deliberate) forms of power. Sixth, high levels of benchlearning will be facilitated by an analyser or prospector strategic stance. Prospectors and analysers reach a different learning outcome than defenders and reactors. Whereas analysers and prospectors are willing to change policies when it is perceived as necessary, the strategic stances of defenders and reactors result in narrow process improvements (i.e., single-loop learning). Seventh, performance improvement is influenced by functional perceptions towards performance, and these perceptions ultimately influence the elements adopted. This research shows that efforts aimed at benchlearning and ultimately improved service delivery, should be directed to a multi-level and multi-dimensional approach addressing the context, content and process of dimensions of change and organisational learning.

A simulation study of service differentiation for the bandwidth request scheme in fixed IEEE 802.16 network

Various flexible mechanisms related to quality of service (QoS) provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the IEEE 802.16 standards. Among the mechanisms, contention based bandwidth request scheme can be used to indicate bandwidth demands to the base station for the non-real-time polling and best-effort services. These two services are used for most applications with unknown traffic characteristics. Due to the diverse QoS requirements of those applications, service differentiation (SD) is anticipated over the contention based bandwidth request scheme. In this paper we investigate the SD with the bandwidth request scheme by means of assigning different channel access parameters and bandwidth allocation priorities at different packets arrival probability. The effectiveness of the differentiation schemes is evaluated by simulations. It is observed that the initial backoff window can be efficient in SD, and if combined with the bandwidth allocation priority, the SD performances will be better.

Aβ(1-42) induced metabolic effects in a stem cell derived neuron and astrocyte network.

Alzheimer’s Disease (AD) is the most common form of dementia currently affecting more than 35 million people worldwide. Hypometabolism is a major feature of AD and appears decades before cognitive decline and pathological lesions. This has a detrimental impact on the brain which has a high energy demand. Current models of AD fail to mimic all the features of the disease, which has an impact on the development of new therapies. Human stem cell derived models of the brain have attracted a lot of attention in recent years as a tool to study neurodegenerative diseases. In this thesis, neurons and astrocytes derived from the human embryonal carcinoma cell line (NT2/D1) were utilised to determine the metabolic coupling between neurons and astrocytes with regards to responses to hypoglycaemia, neuromodulators and increase in neuronal activity. This model was then used to investigate the effects of Aß(1-42) on the metabolism of these NT2-derived co-cultures as well as pure astrocytes. Additionally primary cortical mixed neuronal and glial cultures were utilised to compare this model to a widely accepted in vitro model used in Alzheimer’s disease research. Co-cultures were found to respond to Aß(1-42) in similar way to human and in vivo models. Hypometabolism was characterised by changes in glucose metabolism, as well as lactate, pyruvate and glycogen. This led to a significant decrease in ATP and the ratio of NAD+/NADH. These results together with an increase in calcium oscillations and a decrease in GSH/GSSG ratio, suggests Aß-induces metabolic and oxidative stress. This situation could have detrimental effects in the brain which has a high energy demand, especially in terms of memory formation and antioxidant capacity.