Advanced digital signal processing for coherent optical OFDM transmissions

Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has been actively considered as a potential candidate for long-haul transmission and 400 Gb/s to 1 Tb/s Ethernet transport because of its high spectral efficiency, efficient implementation, flexibility and robustness against linear impairments such as chromatic dispersion and polarization mode dispersion. However, due to the long symbol duration and narrow subcarrier spacing, CO-OFDM systems are sensitive to laser phase noise and fibre nonlinearity induced penalties. As a result, the development of CO-OFDM transmission technology crucially relies on efficient techniques to compensate for the laser phase noise and fibre nonlinearity impairments. In this thesis, high performance and low complexity digital signal processing techniques for laser phase noise and fibre nonlinearity compensation in CO-OFDM transmissions are demonstrated. For laser phase noise compensation, three novel techniques, namely quasipilot-aided, decision-directed-free blind and multiplier-free blind are introduced. For fibre nonlinear compensation, two novel techniques which are referred to as phase conjugated pilots and phase conjugated subcarrier coding, are proposed. All these abovementioned digital signal processing techniques offer high performances and flexibilities while requiring relatively low complexities in comparison with other existing phase noise and nonlinear compensation techniques. As a result of the developments of these digital signal processing techniques, CO-OFDM technology is expected to play a significant role in future ultra-high capacity optical network. In addition, this thesis also presents preliminary study on nonlinear Fourier transform based transmission schemes in which OFDM is a highly suitable modulation format. The obtained result paves the way towards a truly flexible nonlinear wave-division multiplexing system that allows the current nonlinear transmission limitations to be exceeded.

Signal processing techniques for reducing the impact of fiber nonlinearities on system performance

Signal processing techniques for mitigating intra-channel and inter-channel fiber nonlinearities are reviewed. More detailed descriptions of three specific examples highlight the diversity of the electronic and optical approaches that have been investigated.

Analysing time series structure with hidden Markov models

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.

Quantitative risk assessment of groundwater quality utilizing GIS technology and coupled groundwater models

The thesis presents a two-dimensional Risk Assessment Method (RAM) where the assessment of risk to the groundwater resources incorporates both the quantification of the probability of the occurrence of contaminant source terms, as well as the assessment of the resultant impacts. The approach emphasizes the need for a greater dependency on the potential pollution sources, rather than the traditional approach where assessment is based mainly on the intrinsic geo-hydrologic parameters. The risk is calculated using Monte Carlo simulation methods whereby random pollution events were generated to the same distribution as historically occurring events or a priori potential probability distribution. Integrated mathematical models then simulate contaminant concentrations at the predefined monitoring points within the aquifer. The spatial and temporal distributions of the concentrations were calculated from repeated realisations, and the number of times when a user defined concentration magnitude was exceeded is quantified as a risk. The method was setup by integrating MODFLOW-2000, MT3DMS and a FORTRAN coded risk model, and automated, using a DOS batch processing file. GIS software was employed in producing the input files and for the presentation of the results. The functionalities of the method, as well as its sensitivities to the model grid sizes, contaminant loading rates, length of stress periods, and the historical frequencies of occurrence of pollution events were evaluated using hypothetical scenarios and a case study. Chloride-related pollution sources were compiled and used as indicative potential contaminant sources for the case study. At any active model cell, if a random generated number is less than the probability of pollution occurrence, then the risk model will generate synthetic contaminant source term as an input into the transport model. The results of the applications of the method are presented in the form of tables, graphs and spatial maps. Varying the model grid sizes indicates no significant effects on the simulated groundwater head. The simulated frequency of daily occurrence of pollution incidents is also independent of the model dimensions. However, the simulated total contaminant mass generated within the aquifer, and the associated volumetric numerical error appear to increase with the increasing grid sizes. Also, the migration of contaminant plume advances faster with the coarse grid sizes as compared to the finer grid sizes. The number of daily contaminant source terms generated and consequently the total mass of contaminant within the aquifer increases in a non linear proportion to the increasing frequency of occurrence of pollution events. The risk of pollution from a number of sources all occurring by chance together was evaluated, and quantitatively presented as risk maps. This capability to combine the risk to a groundwater feature from numerous potential sources of pollution proved to be a great asset to the method, and a large benefit over the contemporary risk and vulnerability methods.

Is the generation of neo-antigenic determinants by free radicals central to the development of autoimmune rheumatoid disease?

Biomolecules are susceptible to many different post-translational modifications that have important effects on their function and stability, including glycosylation, glycation, phosphorylation and oxidation chemistries. Specific conversion of aspartic acid to its isoaspartyl derivative or arginine to citrulline leads to autoantibody production in models of rheumatoid disease, and ensuing autoantibodies cross-react with native antigens. Autoimmune conditions associate with increased activation of immune effector cells and production of free radical species via NADPH oxidases and nitric oxide synthases. Generation of neo-antigenic determinants by reactive oxygen and nitrogen species ROS and RNS) may contribute to epitope spreading in autoimmunity. The oxidation of amino acids by peroxynitrite, hypochlorous acid and other reactive oxygen species (ROS) increases the antigenicity of DNA, LDL and IgG, generating ligands for which autoantibodies show higher avidity. This review focuses on the evidence for ROS and RNS in promoting the autoimmune responses observed in diseases rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). It considers the evidence for ROS/RNS-induced antigenicity arising as a consequence of failure to remove or repair ROS/RNS damaged biomolecules and suggests that an associated defect, probably in T cell signal processing or/or antigen presentation, is required for the development of disease.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

Recent developments in all-optical nonlinear data processing

Photonic technologies for data processing in the optical domain are expected to play a major role in future high-speed communications. Nonlinear effects in optical fibres have many attractive features and great, but not yet fully explored potential for optical signal processing. Here we provide an overview of our recent advances in developing novel techniques and approaches to all-optical processing based on fibre nonlinearities.

Integrated component scheduling models for chip shooter machines

This paper focuses on minimizing printed circuit board (PCB) assembly time for a chipshootermachine, which has a movable feeder carrier holding components, a movable X–Y table carrying a PCB, and a rotary turret with multiple assembly heads. The assembly time of the machine depends on two inter-related optimization problems: the component sequencing problem and the feeder arrangement problem. Nevertheless, they were often regarded as two individual problems and solved separately. This paper proposes two complete mathematical models for the integrated problem of the machine. The models are verified by two commercial packages. Finally, a hybrid genetic algorithm previously developed by the authors is presented to solve the model. The algorithm not only generates the optimal solutions quickly for small-sized problems, but also outperforms the genetic algorithms developed by other researchers in terms of total assembly time.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Performance data indicate summation for pictorial depth-cues in slanted surfaces

Over recent years much has been learned about the way in which depth cues are combined (e.g. Landy et al., 1995). The majority of this work has used subjective measures, a rating scale or a point of subjective equality, to deduce the relative contributions of different cues to perception. We have adopted a very different approach by using two interval forced-choice (2IFC) performance measures and a signal processing framework. We performed summation experiments for depth cue increment thresholds between pairs of pictorial depth cues in displays depicting slanted planar surfaces made from arrays of circular 'contrast' elements. Summation was found to be ideal when size-gradient was paired with contrast-gradient for a wide range of depth-gradient magnitudes in the null stimulus. For a pairing of size-gradient and linear perspective, substantial summation (> 1.5 dB) was found only when the null stimulus had intermediate depth gradients; when flat or steeply inclined surfaces were depicted, summation was diminished or abolished. Summation was also abolished when one of the target cues was (i) not a depth cue, or (ii) added in conflict. We conclude that vision has a depth mechanism for the constructive combination of pictorial depth cues and suggest two generic models of summation to describe the results. Using similar psychophysical methods, Bradshaw and Rogers (1996) revealed a mechanism for the depth cues of motion parallax and binocular disparity. Whether this is the same or a different mechanism from the one reported here awaits elaboration.

The application of parallel processing methods to vibrational analysis

The trend in modal extraction algorithms is to use all the available frequency response functions data to obtain a global estimate of the natural frequencies, damping ratio and mode shapes. Improvements in transducer and signal processing technology allow the simultaneous measurement of many hundreds of channels of response data. The quantity of data available and the complexity of the extraction algorithms make considerable demands on the available computer power and require a powerful computer or dedicated workstation to perform satisfactorily. An alternative to waiting for faster sequential processors is to implement the algorithm in parallel, for example on a network of Transputers. Parallel architectures are a cost effective means of increasing computational power, and a larger number of response channels would simply require more processors. This thesis considers how two typical modal extraction algorithms, the Rational Fraction Polynomial method and the Ibrahim Time Domain method, may be implemented on a network of transputers. The Rational Fraction Polynomial Method is a well known and robust frequency domain 'curve fitting' algorithm. The Ibrahim Time Domain method is an efficient algorithm that 'curve fits' in the time domain. This thesis reviews the algorithms, considers the problems involved in a parallel implementation, and shows how they were implemented on a real Transputer network.

Dissipative nonlinear structures in fiber optics

Optical fiber materials exhibit a nonlinear response to strong electric fields, such as those of optical signals confined within the small fiber core. Fiber nonlinearity is an essential component in the design of the next generation of advanced optical communication systems, but its use is often avoided by engineers because of its intractability. The application of nonlinear technologies in fiber optics offers new opportunities for the design of photonic systems and devices. In this chapter, we make an overview of recent progress in mathematical theory and practical applications of temporal dissipative solitons and self-similar nonlinear structures in optical fiber systems. The design of all-optical high-speed signal processing devices, based on nonlinear dissipative structures, is discussed.

Projection and interpolation based techniques for structured and impulsive noise filtering

In this chapter we present the relevant mathematical background to address two well defined signal and image processing problems. Namely, the problem of structured noise filtering and the problem of interpolation of missing data. The former is addressed by recourse to oblique projection based techniques whilst the latter, which can be considered equivalent to impulsive noise filtering, is tackled by appropriate interpolation methods.

Sparse signal representation by adaptive non-uniform B-spline dictionaries on a compact interval

Non-uniform B-spline dictionaries on a compact interval are discussed in the context of sparse signal representation. For each given partition, dictionaries of B-spline functions for the corresponding spline space are built up by dividing the partition into subpartitions and joining together the bases for the concomitant subspaces. The resulting slightly redundant dictionaries are composed of B-spline functions of broader support than those corresponding to the B-spline basis for the identical space. Such dictionaries are meant to assist in the construction of adaptive sparse signal representation through a combination of stepwise optimal greedy techniques.

Nonlinear pulse shaping in fibres for pulse generation and optical processing

The development of new all-optical technologies for data processing and signal manipulation is a field of growing importance with a strong potential for numerous applications in diverse areas of modern science. Nonlinear phenomena occurring in optical fibres have many attractive features and great, but not yet fully explored, potential in signal processing. Here, we review recent progress on the use of fibre nonlinearities for the generation and shaping of optical pulses and on the applications of advanced pulse shapes in all-optical signal processing. Amongst other topics, we will discuss ultrahigh repetition rate pulse sources, the generation of parabolic shaped pulses in active and passive fibres, the generation of pulses with triangular temporal profiles, and coherent supercontinuum sources. The signal processing applications will span optical regeneration, linear distortion compensation, optical decision at the receiver in optical communication systems, spectral and temporal signal doubling, and frequency conversion. © Copyright 2012 Sonia Boscolo and Christophe Finot.