Ali Nasiri Amini

Noninvasive estimation of tissue temperature via high-resolution spectral analysis techniques

We address the noninvasive temperature estimation from pulse-echo radio frequency signals from standard diagnostic ultrasound imaging equipment. In particular, we investigate the use of a high-resolution spectral estimation method for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach, employing generalized second-order statistics, is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods Seip and Ebbini (1995). Furthermore, temperature estimates from the new algorithm are compared with results from the more commonly used echo shift method described in Simon et al. (1998).

Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy

The goal of the present study is to employ the source imaging methods such as cortical current density estimation for the classification of left- and right-hand motor imagery tasks, which may be used for brain-computer interface (BCI) applications. The scalp recorded EEG was first preprocessed by surface Laplacian filtering, time-frequency filtering, noise normalization and independent component analysis. Then the cortical imaging technique was used to solve the EEG inverse problem. Cortical current density distributions of left and right trials were classified from each other by exploiting the concept of Von Neumann entropy. The proposed method was tested on three human subjects (180 trials each) and a maximum accuracy of 91.5% and an average accuracy of 88% were obtained. The present results confirm the hypothesis that source analysis methods may improve accuracy for classification of motor imagery tasks. The present promising results using source analysis for classification of motor imagery enhances our ability of performing source analysis from single trial EEG data recorded on the scalp, and may have applications to improved BCI systems.

Avoiding ambiguity in beamspace processing

In direction finding of narrow-band signals using antenna arrays with a large number of elements, the so-called beamspace matrix is often used to project the measurements into a lower dimension subspace. This reduces computation time and allows for parallel processing. On the other hand, beamspace preprocessing may introduce ambiguity, i.e., spurious estimated directions. We show that when the null space of the beamspace matrix is suitably designed, ambiguity within any sector of interest can be avoided.

Tunable line spectral estimators based on state-covariance subspace analysis

Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator

Weight Selection in Feedback Design With Degree Constraints

We present an approach for feedback design which is based on recent developments in analytic interpolation with a degree constraint. Performance is cast as an interpolation problem with bounded analytic functions. Minimizers of a certain weighted-entropy functional provide interpolants having degree less than the number of constraints. The choice of weight parameterizes all such bounded degree solutions. However, the relationship between the weights and the shape of corresponding transfer functions is not direct. Thus, in this paper we develop a formalism that guides weight selection.

Weight Selection in Interpolation with a Dimensionality Constraint

The topic of the paper relates to a recent parametrization of analytic interpolants with a bound on their dimension, as solutions to certain weighted entropy minimization problems. The analytic interpolation problem arises in the context of shaping closed-loop transfer functions via a suitable choice of controller. Our goal is to shed light on how the choice of weights affects the shape of the corresponding closed-loop transfer functions. Further, given a desirable shape, we indicate how a suitable weight can be obtained as a solution of a certain quasi-convex problem

Sensitivity shaping with degree constraint via convex optimization

We present an approach for shaping closed-loop operators while keeping their Mcmillan degree bounded by the sum of unstable plant-poles and non-minimum phase plant-zeros. We make use of recent developments in analytic interpolation with degree constraint and we focus on the paradigm of sensitivity minimization. The sensitivity function can be obtained as the minimizer of a convex weighted-entropy functional. It is the choice of this weight that we formulate as a convex optimization problem in this paper

Noninvasive tissue temperature estimation via state-covariance spectral estimation

We address noninvasive temperature estimation from pulse-echo radio frequency signals obtained by standard diagnostic ultrasound imaging equipment. In particular, we use a novel spectral estimation technique based on state-covariance subspace analysis for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods. The experimental results validate the theoretical prediction of existing harmonically-related peaks in the echo signal spectrum.

Statistical analysis of state-covariance subspace-estimation methods

We present a statistical analysis of subspace-based methods for the retrieval of sinusoids. The formalism described previously encompasses the modern nonlinear methods of MUSIC and ESPRIT and yields methods of even higher resolution. These rely on eigendecomposition of state-covariances of linear systems, as opposed to eigendecomposition of Toeplitz matrices (originating from antenna arrays or tapped delay-lines and treated). We focus on the variability of estimates when the theory is applied to sampled covariances obtained from finite observation records, and in particular, we provide an expression for the variance of the angle operator between estimated and exact signal subspaces.