Temujin Gautama

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We introduce a new technique for estimating the optical flow field, starting from image sequences. As suggested by Fleet and Jepson (1990), we track contours of constant phase over time, since these are more robust to variations in lighting conditions and deviations from pure translation than contours of constant amplitude. Our phase-based approach proceeds(More)
The dynamical properties of electroencephalogram (EEG) segments have recently been analyzed by Andrzejak and co-workers for different recording regions and for different brain states, using the nonlinear prediction error and an estimate of the correlation dimension. In this paper, we further investigate the nonlinear properties of the EEG signals using two(More)
A novel ‘Delay Vector Variance’ (DVV) method for detecting the presence of determinism and nonlinearity in a time series is introduced. The method is based upon the examination of local predictability of a signal. Additionally, it spans the complete range of local linear models due to the standardisation to the distribution of pairwise distances between(More)
A new method to extend the empirical mode decomposition (EMD) into the complex domain is proposed. Unlike the existing method for EMD in the complex domain, this is achieved in a generic way so that the mathematical development of this method mirrors the algorithm defined for EMD in the real domain. The so derived intrinsic mode functions (IMFs) are complex(More)
A novel method for determining the set of parameters for a phase space representation of a time series is proposed. Based upon the differential entropy, both the optimal embedding dimension , and time lag , are simultaneously determined. The choice of these parameters is closely related to the length of the optimal tap input delay line of an adaptive filter(More)
Most statistical signal nonlinearity analyses adopt the Monte-Carlo approach proposed by Theiler and co-workers, namely the ‘surrogate data’ method. A surrogate time series, or ‘surrogate’ for short, is generated as a realisation of the null hypothesis of linearity. A measure (‘test statistic’) is computed for the original time series and it is compared to(More)
The delay vector variance (DVV) method, which analyzes the nature of a time series with respect to the prevalence of deterministic or stochastic components, is introduced. Due to the standardization within the DVV method, it is possible both to statistically test for the presence of nonlinearities in a time series, and to visually inspect the results in a(More)
When using topographic maps for clustering purposes, which is now being considered in the data mining community, it is crucial that the maps are free of topological defects. Otherwise, a contiguous cluster could become split into separate clusters. We introduce a new algorithm for monitoring the degree of topology preservation of kernel-based maps during(More)
A novel stable and robust algorithm for training of finite impulse response adaptive filters is proposed. This is achieved based on a convex combination of the least mean square (LMS) and a recently proposed generalised normalised gradient descent (GNGD) algorithm. In this way, the desirable fast convergence and stability of GNGD is combined with the(More)
In the General Linear Model (GLM) framework for the statistical analysis of fMRI data, the problem of temporal autocorrelations in the residual signal (after regression) has been frequently addressed in the open literature. There exist various methods for correcting the ensuing bias in the statistical testing, among which the prewhitening strategy, which(More)