S OF SCIENTIFIC REPORTS AVAILABLE

We introduce a technique for identification of systems with arbitrarily time-variant responses from samples of their input and output signals, and without using any prior information about the dynamics of the unknown system response. Our technique is based on the use of optimized averaging filters for the estimation of time-variant second order moments. We demonstrate the utility of our approac...

In a recent paper by Vencheh and Esmaeilzadeh, a super-efficiency model based on RDM model is formulated where some input and output data are negative. However, we show that the formulation in the paper is not always feasible or bounded for its purpose and cannot produce the numerical results claimed. Moreover, we demonstrate that the super-efficiency model based on RDM model is unable to provi...

In system identi cation, estimates of the unknown system model orders are often required. An algorithm for estimating model orders is described which looks at input/output data covariance matrix eigenvectors. When model orders are overestimated, zeros appear in the noise subspace eigenvectors. The number of zeros present can be used to estimate model orders.

Daeman and Rijmen had derived the distributions of correlations between linear combinations of the input and output of uniform random functions and uniform random permutations. We generalise their results by deriving the distributions of correlations between arbitrary combinations of the input and the output of uniform random functions and uniform random permutations.

Models of both speech perception and speech production typically postulate a processing level that involves some form of phonological processing. There is disagreement, however, on the question of whether there are separate phonological systems for speech input versus speech output. We review a range of neuroscientific data that indicate that input and output phonological systems partially over...