GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex

Recently, we introduced a new 'GLM-beamformer' technique for MEG analysis that enables accurate localisation of both phase-locked and non-phase-locked neuromagnetic effects, and their representation as statistical parametric maps (SPMs). This provides a useful framework for comparison of the full range of MEG responses with fMRI BOLD results. This paper reports a 'proof of principle' study using a simple visual paradigm (static checkerboard). The five subjects each underwent both MEG and fMRI paradigms. We demonstrate, for the first time, the presence of a sustained (DC) field in the visual cortex, and its co-localisation with the visual BOLD response. The GLM-beamformer analysis method is also used to investigate the main non-phase-locked oscillatory effects: an event-related desynchronisation (ERD) in the alpha band (8-13 Hz) and an event-related synchronisation (ERS) in the gamma band (55-70 Hz). We show, using SPMs and virtual electrode traces, the spatio-temporal covariance of these effects with the visual BOLD response. Comparisons between MEG and fMRI data sets generally focus on the relationship between the BOLD response and the transient evoked response. Here, we show that the stationary field and changes in oscillatory power are also important contributors to the BOLD response, and should be included in future studies on the relationship between neuronal activation and the haemodynamic response. © 2005 Elsevier Inc. All rights reserved.

Various techniques and procedures for refining ERS-1 orbits

The satellite ERS-1 was launched in July 1991 in a period of high solar activity. Sparse laser tracking and the failure of the experimental microwave system (PRARE) compounded the orbital errors which resulted from mismodelling of atmospheric density and hence surface forces. Three attempts are presented here to try and refine the coarse laser orbits of ERS-1, made prior to the availability of the full altimetric dataset. The results of the first attempt indicate that by geometrically modelling the satellite shape some improvement in orbital precision may be made for any satellite; especially one where no area tables already exist. The second and third refinement attempts are based on the introduction of data from some second satellite; in these examples SPOT-2 and TOPEX/Poseidon are employed. With SPOT-2 the method makes use of the orbital similarities to produce along-track corrections for the more fully tracked SPOT-2. Transferring these corrections to ERS-1 produces improvements in the precise orbits thus determined. With TOPEX/Poseidon the greater altitude results in a more precise orbit (gravity field and atmospheric errors are of less importance). Thus, by computing height differences at crossover points of the TOPEX/Poseidon and ERS-1 ground tracks the poorer orbit of ERS-1 may be improved by the addition of derived radial corrections. In the positive light of all three results several potential modification are suggested and some further avenues of investigation indicated.

The use of ERS-1 crossovers and TOPEX/Poseidon repeat pass data for global sea surface variability studies

In previous sea-surface variability studies, researchers have failed to utilise the full ERS-1 mission due to the varying orbital characteristics in each mission phase, and most have simply ignored the Ice and Geodetic phases. This project aims to introduce a technique which will allow the straightforward use of all orbital phases, regardless of orbit type. This technique is based upon single satellite crossovers. Unfortunately the ERS-1 orbital height is still poorly resolved (due to higher air drag and stronger gravitational effects) when compared with that of TOPEX/Poseidon (T/P), so to make best use of the ERS-1 crossover data corrections to the ERS-1 orbital heights are calculated by fitting a cubic-spline to dual-crossover residuals with T/P. This correction is validated by comparison of dual satellite crossovers with tide gauge data. The crossover processing technique is validated by comparing the extracted sea-surface variability information with that from T/P repeat pass data. The two data sets are then combined into a single consistent data set for analysis of sea-surface variability patterns. These patterns are simplified by the use of an empirical orthogonal function decomposition which breaks the signals into spatial modes which are then discussed separately. Further studies carried out on these data include an analysis of the characteristics of the annual signal, discussion of evidence for Rossby wave propagation on a global basis, and finally analysis of the evidence for global mean sea level rise.

Application of ERS-1 and Topex/Poseidon altimetry using precise orbits

The work described in this thesis concerns the application of radar altimetry, collected from the ERS-1 and TOPEX/POSEIDON missions, to precise satellite orbits computed at Aston University. The data is analysed in a long arc fashion to determine range biases, time tag biases, sea surface topographies and to assess the radial accuracy of the generated orbits through crossover analysis. A sea surface variability study is carried out for the North Sea using repeat altimeter profiles from ERS-1 and TOPEX/POSEIDON in order to verify two local U.K. models for ocean tide and storm surge effects. An on-side technique over the English Channel is performed to compute the ERS-1, TOPEX and POSEIDON altimeter range biases by using a combination of altimetry, precise orbits determined by short arc methods, tide gauge data, GPS measurements, geoid, ocean tide and storm surge models. The remaining part of the thesis presents some techniques for the short arc correction of long arc orbits. Validation of this model is achieved by way of comparison with actual SEASAT short arcs. Simulations are performed for the ERS-1 microwave tracking system, PRARE, using the range data to determine time dependent orbit corrections. Finally, a brief chapter is devoted to the recovery of errors in station coordinates by the use of multiple short arcs.

Orbit and altimetric corrections for the ERS satellites through analysis of single and dual satellite crossovers

Due to the failure of PRARE the orbital accuracy of ERS-1 is typically 10-15 cm radially as compared to 3-4cm for TOPEX/Poseidon. To gain the most from these simultaneous datasets it is necessary to improve the orbital accuracy of ERS-1 so that it is commensurate with that of TOPEX/Poseidon. For the integration of these two datasets it is also necessary to determine the altimeter and sea state biases for each of the satellites. Several models for the sea state bias of ERS-1 are considered by analysis of the ERS-1 single satellite crossovers. The model adopted consists of the sea state bias as a percentage of the significant wave height, namely 5.95%. The removal of ERS-1 orbit error and recovery of an ERS-1 - TOPEX/Poseidon relative bias are both achieved by analysis of dual crossover residuals. The gravitational field based radial orbit error is modelled by a finite Fourier expansion series with the dominant frequencies determined by analysis of the JGM-2 co-variance matrix. Periodic and secular terms to model the errors due to atmospheric density, solar radiation pressure and initial state vector mis-modelling are also solved for. Validation of the dataset unification consists of comparing the mean sea surface topographies and annual variabilities derived from both the corrected and uncorrected ERS-1 orbits with those derived from TOPEX/Poseidon. The global and regional geographically fixed/variable orbit errors are also analysed pre and post correction, and a significant reduction is noted. Finally the use of dual/single satellite crossovers and repeat pass data, for the calibration of ERS-2 with respect to ERS-1 and TOPEX/Poseidon is shown by calculating the ERS-1/2 sea state and relative biases.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Employment relations in India

This paper examines the employment relations (ERs) scenario in Indian organisations. The investigation is based on a questionnaire survey of 137 Indian firms in the manufacturing sector. The analysis of existing literature highlights the role of three key actors (management, unions, and the state) in the management of ERs in Indian organisations. It also shows the significant impact of the competitive pressures created by the liberalisation of the Indian economy in the changing nature of ERs in Indian firms. The study has key implications both for academicians and for practitioners.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.

Global non-dynamic refinement of radial orbit error for altimetric earth satellites

The accuracy of altimetrically derived oceanographic and geophysical information is limited by the precision of the radial component of the satellite ephemeris. A non-dynamic technique is proposed as a method of reducing the global radial orbit error of altimetric satellites. This involves the recovery of each coefficient of an analytically derived radial error correction through a refinement of crossover difference residuals. The crossover data is supplemented by absolute height measurements to permit the retrieval of otherwise unobservable geographically correlated and linearly combined parameters. The feasibility of the radial reduction procedure is established upon application to the three day repeat orbit of SEASAT. The concept of arc aggregates is devised as a means of extending the method to incorporate longer durations, such as the 35 day repeat period of ERS-1. A continuous orbit is effectively created by including the radial misclosure between consecutive long arcs as an infallible observation. The arc aggregate procedure is validated using a combination of three successive SEASAT ephemerides. A complete simulation of the 501 revolution per 35 day repeat orbit of ERS-1 is derived and the recovery of the global radial orbit error over the full repeat period is successfully accomplished. The radial reduction is dependent upon the geographical locations of the supplementary direct height data. Investigations into the respective influences of various sites proposed for the tracking of ERS-1 by ground-based transponders are carried out. The potential effectiveness on the radial orbital accuracy of locating future tracking sites in regions of high latitudinal magnitude is demonstrated.

Satellite borne radar altimetry : empirical orbit refinement and ocean signal recovery techniques

Measurements of the sea surface obtained by satellite borne radar altimetry are irregularly spaced and contaminated with various modelling and correction errors. The largest source of uncertainty for low Earth orbiting satellites such as ERS-1 and Geosat may be attributed to orbital modelling errors. The empirical correction of such errors is investigated by examination of single and dual satellite crossovers, with a view to identifying the extent of any signal aliasing: either by removal of long wavelength ocean signals or introduction of additional error signals. From these studies, it was concluded that sinusoidal approximation of the dominant one cycle per revolution orbit error over arc lengths of 11,500 km did not remove a significant mesoscale ocean signal. The use of TOPEX/Poseidon dual crossovers with ERS-1 was shown to substantially improve the radial accuracy of ERS-1, except for some absorption of small TOPEX/Poseidon errors. The extraction of marine geoid information is of great interest to the oceanographic community and was the subject of the second half of this thesis. Firstly through determination of regional mean sea surfaces using Geosat data, it was demonstrated that a dataset with 70cm orbit error contamination could produce a marine geoid map which compares to better than 12cm with an accurate regional high resolution gravimetric geoid. This study was then developed into Optimal Fourier Transform Interpolation, a technique capable of analysing complete altimeter datasets for the determination of consistent global high resolution geoid maps. This method exploits the regular nature of ascending and descending data subsets thus making possible the application of fast Fourier transform algorithms. Quantitative assessment of this method was limited by the lack of global ground truth gravity data, but qualitative results indicate good signal recovery from a single 35-day cycle.

Development and application of magnetoencephalographic methods in the investigation of the human visual system

This thesis is an exploration of the organisation and functioning of the human visual system using the non-invasive functional imaging modality magnetoencephalography (MEG). Chapters one and two provide an introduction to the ‘human visual system and magnetoencephalographic methodologies. These chapters subsequently describe the methods by which MEG can be used to measure neuronal activity from the visual cortex. Chapter three describes the development and implementation of novel analytical tools; including beamforming based analyses, spectrographic movies and an optimisation of group imaging methods. Chapter four focuses on the use of established and contemporary analytical tools in the investigation of visual function. This is initiated with an investigation of visually evoked and induced responses; covering visual evoked potentials (VEPs) and event related synchronisation/desynchronisation (ERS/ERD). Chapter five describes the employment of novel methods in the investigation of cortical contrast response and demonstrates distinct contrast response functions in striate and extra-striate regions of visual cortex. Chapter six use synthetic aperture magnetometry (SAM) to investigate the phenomena of visual cortical gamma oscillations in response to various visual stimuli; concluding that pattern is central to its generation and that it increases in amplitude linearly as a function of stimulus contrast, consistent with results from invasive electrode studies in the macaque monkey. Chapter seven describes the use of driven visual stimuli and tuned SAM methods in a pilot study of retinotopic mapping using MEG; finding that activity in the primary visual cortex can be distinguished in four quadrants and two eccentricities of the visual field. Chapter eight is a novel implementation of the SAM beamforming method in the investigation of a subject with migraine visual aura; the method reveals desynchronisation of the alpha and gamma frequency bands in occipital and temporal regions contralateral to observed visual abnormalities. The final chapter is a summary of main conclusions and suggested further work.

Mean sea surface modelling from multi-satellite altimetry utilising frequency domain techniques

A technique is presented for the development of a high precision and resolution Mean Sea Surface (MSS) model. The model utilises Radar altimetric sea surface heights extracted from the geodetic phase of the ESA ERS-1 mission. The methodology uses a modified Le Traon et al. (1995) cubic-spline fit of dual ERS-1 and TOPEX/Poseidon crossovers for the minimisation of radial orbit error. The procedure then uses Fourier domain processing techniques for spectral optimal interpolation of the mean sea surface in order to reduce residual errors within the model. Additionally, a multi-satellite mean sea surface integration technique is investigated to supplement the first model with additional enhanced data from the GEOSAT geodetic mission.The methodology employs a novel technique that combines the Stokes' and Vening-Meinsz' transformations, again in the spectral domain. This allows the presentation of a new enhanced GEOSAT gravity anomaly field.

Techniques for determining bias and stability in satellite altimeters

The case for monitoring large-scale sea level variability is established in the context of the estimation of the extent of anthropogenic climate change. Satellite altimeters are identified as having the potential to monitor this change with high resolution and accuracy. Possible sources of systematic errors and instabilities in these instruments which would be hurdles to the most accurate monitoring of such ocean signals are examined. Techniques for employing tide gauges to combat such inaccuracies are proposed and developed. The tide gauge at Newhaven in Sussex is used in conjunction with the nearby satellite laser ranger and high-resolution ocean models to estimate the absolute bias of the TOPEX, Poseidon, ERS 1 and ERS 2 altimeters. The theory which underlies the augmentation of altimeter measurements with tide gauge data is developed. In order to apply this, the tide gauges of the World Ocean Circulation Experiment are assessed and their suitability for altimeter calibration is determined. A reliable subset of these gauges is derived. A method of intra-altimeter calibration is developed using these tide gauges to remove the effect of variability over long time scales. In this way the long-term instability in the TOPEX range measurement is inferred and the drift arising from the on-board ultra stable oscillator is thus detected. An extension to this work develops a method for inter-altimeter calibration, allowing the systematic differences between unconnected altimeters to be measured. This is applied to the TOPEX and ERS 1 altimeters.