This paper presents a geometric approach to estimating subspaces as elements of complex Grassmann-manifold, with each subspace represented by its unique, complex projection matrix. Variation between the subspaces is modeled by rotating their projection matrices via the action of unitary matrices [elements of the unitary group U( )]. Subspace estimation or tracking then corresponds to inferences...

| This paper presents a geometric approach to estimating subspaces as elements of complex Grassmann-manifold, with each subspace represented by its unique, complex projection matrix. Variation between the sub-spaces is modeled by rotating their projection matrices via the action of unitary matrices (elements of the unitary group (U(n))). Subspace estimation or tracking then corresponds to the i...

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discr...

The Gaussian statistic model, despite its mathematical elegance, is found to be too factitious for many real world signals, as manifested by its unsatisfactory performance when applied to non-Gaussian signals. Traditional non-Gaussian signal processing techniques, on the other hand, are usually associated with high complexities and low data efficiencies. This thesis addresses the problem of opt...

Computational vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insufficient use of structure in the posterior probability. The squared error penalty of the MMSE estimator does not reflect typical ...

In most previous research work on data detection in mobile radio systems the noise is assumed to be Gaussian distributed with known noise variance, and a separate variance estimation is required in realistic scenarios. In order to avoid the additional variance estimator and to obtain better system performance, a novel optimum minimum mean square error (MMSE) detector exploiting noise statistics...

In this paper two different channel estimation techniques for a mobile beyond 3G MC-CDMA systems are evaluated. These techniques are the robust MMSE estimation with pre-estimator of autocorrelation channel matrix and the delay profile based estimation. Simulation results, carried out over a realistic propagation channel, compare the proposed channel estimation algorithms, in terms of the mean s...

A novel hybrid channel estimator is proposed for multiple-input multiple-output orthogonal frequencydivision multiplexing (MIMO-OFDM) system with per-subcarrier transmit antenna selection having optimal power allocation among subcarriers. In practice, antenna selection information is transmitted through a binary symmetric control channel with a crossover probability. Linear minimum mean-square ...

In this paper, the channel estimation methods for OFDM systems based on comb-type pilot sub-carrier arrangement are investigated. The channel estimation algorithm based on combtype pilots is divided into pilot signal estimation and channel interpolation. The pilot signal estimation based on LS or MMSE criteria, together with channel interpolation based on piecewise-linear interpolation or piece...

OBJECTIVE To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE). METHODS We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (...