Noise and gain optimisation in bi-directionally pumped dispersion compensating amplifier modules

We perform optimisation of bi-directionally pumped dispersion compensating Raman amplifier modules. Optimal forward and backward pump powers for basic configurations using different commercially available fibers are presented for both single- and multi-channel systems. Optical signal-to-noise ratio improvement of up to 8 dB is achieved as a result of optimisation. © 2003 Published by Elsevier B.V.

Optimization of gain and SNR in bi-directionally pumped dispersion compensating amplifier modules

In this paper, we present an analysis and optimisation of the performance of bi-directionally pumped dispersion compensation modules acting as simultaneous Raman amplifiers, with optimal configurations for operation with different fibers commercially available. The ratio between forward and backward pump powers for minimum noise influence is obtained in each case, with improvements in the SNR of up to 8 dB when compared to a purely backward-pumped case.

Nonlinearity management in fibre links with distributed amplification

An approach to nonlinearity management in optical transmission lines with periodic dispersion compensation and distributed Raman amplification was presented. The optimization of a three-step dispersion map with forward and backward pumped distributed amplification was examined. The optimization was performed using the analytical solution obtained under the assumption of undepleted pumps and without inclusion of double Rayleigh Scattering (DRS), and by means of a full numerical approach accounting for all important effects. It was found that both procedures led to the same final solution.

Strongly localized moving discrete dissipative breather-solitons in Kerr nonlinear media supported by intrinsic gain

We investigate the mobility of nonlinear localized modes in a generalized discrete Ginzburg-Landau-type model, describing a one-dimensional waveguide array in an active Kerr medium with intrinsic, saturable gain and damping. It is shown that exponentially localized, traveling discrete dissipative breather-solitons may exist as stable attractors supported only by intrinsic properties of the medium, i.e., in the absence of any external field or symmetry-breaking perturbations. Through an interplay by the gain and damping effects, the moving soliton may overcome the Peierls-Nabarro barrier, present in the corresponding conservative system, by self-induced time-periodic oscillations of its power (norm) and energy (Hamiltonian), yielding exponential decays to zero with different rates in the forward and backward directions. In certain parameter windows, bistability appears between fast modes with small oscillations and slower, large-oscillation modes. The velocities and the oscillation periods are typically related by lattice commensurability and exhibit period-doubling bifurcations to chaotically "walking" modes under parameter variations. If the model is augmented by intersite Kerr nonlinearity, thereby reducing the Peierls-Nabarro barrier of the conservative system, the existence regime for moving solitons increases considerably, and a richer scenario appears including Hopf bifurcations to incommensurately moving solutions and phase-locking intervals. Stable moving breathers also survive in the presence of weak disorder. © 2014 American Physical Society.

Performance improvement of broadband distributed Raman amplifier using bidirectional pumping with first and dual order forward pumps

In this paper, a new bidirectional pumping scheme with dual order forward pumps is proposed. Performance is compared numerically with conventional bidirectional and backward only pumping schemes for a 70 nm bandwidth, 61.5 km distributed Raman amplifier. We demonstrate that it is possible to design a flat gain spectrum with improved noise figure and OSNR, as well as a low gain ripple (<1 dB).

The interaction between master production scheduling and detailed capacity requirements planning : closing the loop in manufacturing resource planning (MRPII) systems

The widespread implementation of Manufacturing Resource Planning (MRPII) systems in this country and abroad and the reported dissatisfaction with their use formed the initial basis of this piece of research which concentrates on the fundamental theory and design of the Closed Loop MRPII system itself. The dissertation concentrates on two key aspects namely; how Master Production Scheduling is carried out in differing business environments and how well the `closing of the loop' operates by checking the capcity requirements of the different levels of plans within an organisation. The main hypothesis which is tested is that in U.K. manufacturing industry, resource checks are either not being carried out satisfactorily or they are not being fed back to the appropriate plan in a timely fashion. The research methodology employed involved initial detailed investigations into Master Scheduling and capacity planning in eight diverse manufacturing companies. This was followed by a nationwide survey of users in 349 companies, a survey of all the major suppliers of Production Management software in the U.K. and an analysis of the facilities offered by current software packages. The main conclusion which is drawn is that the hypothesis is proved in the majority of companies in that only just over 50% of companies are attempting Resource and Capacity Planning and only 20% are successfully feeding back CRP information to `close the loop'. Various causative factors are put forward and remedies are suggested.

Magnetoencephalography:technical improvements in image co-registration and studies of visual cortical oscillations

The work presented in this thesis is divided into two distinct sections. In the first, the functional neuroimaging technique of Magnetoencephalography (MEG) is described and a new technique is introduced for accurate combination of MEG and MRI co-ordinate systems. In the second part of this thesis, MEG and the analysis technique of SAM are used to investigate responses of the visual system in the context of functional specialisation within the visual cortex. In chapter one, the sources of MEG signals are described, followed by a brief description of the necessary instrumentation for accurate MEG recordings. This chapter is concluded by introducing the forward and inverse problems of MEG, techniques to solve the inverse problem, and a comparison of MEG with other neuroimaging techniques. Chapter two provides an important contribution to the field of research with MEG. Firstly, it is described how MEG and MRI co-ordinate systems are combined for localisation and visualisation of activated brain regions. A previously used co-registration methods is then described, and a new technique is introduced. In a series of experiments, it is demonstrated that using fixed fiducial points provides a considerable improvement in the accuracy and reliability of co-registration. Chapter three introduces the visual system starting from the retina and ending with the higher visual rates. The functions of the magnocellular and the parvocellular pathways are described and it is shown how the parallel visual pathways remain segregated throughout the visual system. The structural and functional organisation of the visual cortex is then described. Chapter four presents strong evidence in favour of the link between conscious experience and synchronised brain activity. The spatiotemporal responses of the visual cortex are measured in response to specific gratings. It is shown that stimuli that induce visual discomfort and visual illusions share their physical properties with those that induce highly synchronised gamma frequency oscillations in the primary visual cortex. Finally chapter five is concerned with localization of colour in the visual cortex. In this first ever use of Synthetic Aperture Magnetometry to investigate colour processing in the visual cortex, it is shown that in response to isoluminant chromatic gratings, the highest magnitude of cortical activity arise from area V2.

A study of personality and leadership in a technical environment

l, This report presents the findings of a study of individual personalities of Naval Officers, Chief Petty Officers and Petty Officers serving in different environments within the Ministry of Defence and the Fleet. This sample was used to establish norms for the Cattell 16 PF Questionnaire, and these are compared with other occupational norms discussed in the literature. 2. The results obtained on psychometric measures were related to other data collected about the work and the formal organisation. This was in its turn related to problems facing the Navy because of changes in technology which have occurred or which are now taking place and are expected to make an impact in the future. 3. A need is recognised for a way of simulating the effects of proposed changes within the manpower field of the Royal Navy and a simulation model is put forward and discussed. 4. The use of psychometric measures in selection for entry and for special tasks is examined, Particular reference is made to problems of group formation in the context of leadership in a technical environment. 5. The control of the introduction of change is discussed in the recognition that people represent an increasingly important resource which is critical to the continuing life of the total organisation. 6. Conclusions are drawn from the various strands of the research and recommendations are made both for line management and for subsequent research programmes.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

The effects of yoked prisms on body posture and egocentric perception in a normal population

The principal theme of this thesis is the effect of yoked prisms on body posture and egocentric perception. Yoked prisms have been clinically used in the management of a variety of visual and neuro-motor dysfunctions. Most studies have been conducted in pathological populations by studying the effects of prismatic adaptation, without distinguishing short and long term effects. In this study, postural and perceptual prismatic effects have been studied by preventing prism adaptation. A healthy population was selected in order to investigate the immediate prismatic effects, when there is no obvious benefit from their use for the individual. Posturography was used to assess changes in weight distribution and shifts in centre of pressure (barycentre). In addition, photographic analyses were used to assess effects on posture on the x and z axis. Experiments with space board and visual midline shift were used for the evaluation of spatial perception and egocentric localisation. One pair of 8 Δ yoked prisms base left (BL) and one pair of 8 Δ yoked prisms base up (BU) were applied randomly and compared to a pair of plano lenses. Results suggest that immediate prismatic effects take place on a perceptual level and are reflected on an altered body posture respectively without significant changes in weight distribution. Yoked prisms BL showed a rightward rotational effect on spatial perception by expanding space on the z axis when viewing through the base of the prism and constricting space through the apex of the prism. Body posture responded respectively to what was visually perceived by altering posture. A rightward shift and tilt of the head was recorded along with the hips shift and shoulders tilt in the dame direction. Additionally, right shoulder shifted backwards and an angular midline shift to the right was recorded. The egocentric localisation was affected by shifting the midline perception to the left. Yoked prisms BU resulted on a head shift forward and a reduction of the head-neck angle by bringing the chin closer to the chest. The egocentric localisation was altered on the vertical axis providing subjects the perception that their eye level was higher during the experiment. In conclusion, yoked prisms seemed to induce changes in body posture, mainly in the upper body and head, without any significant changes in weight distribution. These changes are partially reflected in spatial perception tests and egocentric localisation before any prismatic adaptation takes place.

Improved robust control of nonlinear stochastic systems using uncertain models

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Is the clustering of neurofibrillary tangles in Alzheimer's patients related to the cells of origin of specific cortico-cortical projections?

The spatial pattern of cellular neurofibrillary tangles (NFT) was studied in the supra- and infragranular layers of various cortical regions in cases of Alzheimer's disease (AD). The objective was to test the hypothesis that NFT formation was associated with the cells of origin of specific cortico-cortical projections. The novel feature of the study was that pattern analysis enabled the dimension and spacing of NFT clusters along the cortical ribbon to be estimated. In the majority of brain regions studied, NFT occurred in clusters of neurons which were regularly spaced along the cortical strip. This pattern is consistent with the predicted distribution of the cells of origin of specific cortico-cortico projections. Mean NFT cluster size varied from 250 to > 12800 microns in different cortical tissues suggesting either variation in the size of the cell clusters or a dynamic process in the development of NFT in relation to these cell clusters. The formation of NFT in cell clusters which may give rise to the feed-forward and feed-back cortico-cortical projections suggests a possible route of spread of NFT pathology in AD between cortical regions and from the cortex to subcortical areas.

Digital image watermarking

In recent years, interest in digital watermarking has grown significantly. Indeed, the use of digital watermarking techniques is seen as a promising mean to protect intellectual property rights of digital data and to ensure the authentication of digital data. Thus, a significant research effort has been devoted to the study of practical watermarking systems, in particular for digital images. In this thesis, a practical and principled approach to the problem is adopted. Several aspects of practical watermarking schemes are investigated. First, a power constaint formulation of the problem is presented. Then, a new analysis of quantisation effects on the information rate of digital watermarking scheme is proposed and compared to other approaches suggested in the literature. Subsequently, a new information embedding technique, based on quantisation, is put forward and its performance evaluated. Finally, the influence of image data representation on the performance of practical scheme is studied along with a new representation based on independent component analysis.

Exploiting uncertainty in nonlinear stochastic control problem

This work introduces a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. Convergence of the output error for the proposed control method is verified by using a Lyapunov function. Several simulation examples are provided to demonstrate the efficiency of the developed control method. The manner in which such a method is extended to nonlinear multi-variable systems with different delays between the input-output pairs is considered and demonstrated through simulation examples.