Abstract
The convenience of the motor-driven treadmill makes it an attractive instrument for investigating rat locomotion. However, no data are available to indicate whether hindlimb treadmill kinematic findings may be compared or generalized to overground locomotion. In this investigation, we compared overground and treadmill locomotion for differences in the two-dimensional angular kinematics and temporal and spatial measurements for the hindlimb. Ten female rats were evaluated at the same speed for natural overground and treadmill walking. The walking velocity, swing duration and stride length were statistically indistinguishable between the two testing conditions. Significant differences were found between overground and treadmill locomotion for step cycle duration and stance phase duration parameters. During the stance phase of walking, the angular movement of the hip, knee and ankle joints were significantly different in the two conditions, with greater flexion occurring on the overground. Despite this, the sagittal joint movements of the hindlimb were similar between the two walking conditions, with only three parameters being significantly different in the swing. Hip height and angle-angle cyclograms were also only found to display subtle differences. This study suggests that reliable kinematic measurements can be obtained from the treadmill gait analysis in rats.

Abstract
In this paper, we presented an application developed to measure the quality of a set of reconstructed high frame rate sequences. The main goal of the developed application is to allow the simultaneous visualization of both original and reconstructed sequences and measure the quality of the reconstructed frames.

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This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP) analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitted to the multivariate dataset of observations. The extension of the POP methodology to autoregressions of higher order, although increasing the difficulties in estimation, allows one to model a larger class of complex systems. Here, sea level variability in the North Atlantic is modelled by a third order multivariate autoreerressive model estimated by stepwise least squares. Eigen-decomposition of the fitted model yields physically-interpretable seasonal modes. The leading autoregressive mode is an annual oscillation and exhibits a very homogeneous spatial structure in terms of amplitude reflecting the large scale coherent behaviour of the annual pattern in the Northern hemisphere. The phase structure reflects the seesaw pattern between the western and eastern regions in the tropical North Atlantic associated with the trade winds regime. The second mode is close to a semi-annual oscillation. Multivariate autoregressive models provide a useful framework for the description of time-varying fields while enclosing a predictive potential.