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—The theory of compressed sensing suggests that successful inversion of an image of the physical world (broadly defined to include speech signals, radar/sonar returns, vibration records, sensor array snapshot vectors, 2-D images, and so on) for its source modes and amplitudes can be achieved at measurement dimensions far lower than what might be expected… (More)

- Gitta Kutyniok, Ali Pezeshki, Robert Calderbank, Taotao Liu, T Liu
- 2007

We consider estimating a random vector from its measurements in a fusion frame, in presence of noise and subspace erasures. A fusion frame is a collection of subspaces, for which the sum of the projection operators onto the subspaces is bounded below and above by constant multiples of the identity operator. We first consider the linear minimum mean-squared… (More)

—The problem of two-channel constrained least squares (CLS) filtering under various sets of constraints is considered, and a general set of solutions is derived. For each set of constraints, the solution is determined by a coupled (asymmetric) generalized eigenvalue problem. This eigenvalue problem establishes a connection between two-channel CLS filtering… (More)

—We derive eigenvalue beamformers to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown. The unknown orientation may be fixed, in which case the signal covariance is rank-1, or it may be random, in which case the signal covariance is multirank. We present a… (More)

- Louis Scharf, Ali Pezeshki, Barry Van Veen, Henry Cox, Olivier Besson
- 2006

— We derive multi-rank generalizations of the MVDR beamformer to separate an unknown signal of interest in the presence of interference and noise. The spatial signature of the signal is assumed to lie in a known linear subspace, but the orientation of the signal in that subspace is otherwise unknown. The unknown orientation may be fixed for a sequence of… (More)

- Ali Pezeshki, Louis L Scharf, John K Thomas, Barry D Van Veen
- 2006

—In this correspondence, our aim is to establish a connection between low-rank detection, low-rank estimation, and canonical coordinates. The key to this connection is the observation that Gauss-Gauss detectors and estimators share canonical coordinates in the case where the underlying model is a signal-plus-noise model. We show that in Gauss–Gauss… (More)

a r t i c l e i n f o a b s t r a c t Article history: Available online xxxx In this work, multiple radar waveforms are simultaneously transmitted, emitted from different antennas. The goal is to process the returns in such a way that the overall ambiguity function is a sum of individual ambiguity functions, such that the sum better approximates the ideal… (More)

- Ramin Zahedi, Ali Pezeshki, Edwin K P Chong
- 2010

— Detecting a sparse signal in noise is fundamentally different from reconstructing a sparse signal, as the objective is to optimize a detection performance criterion rather than to find the sparsest signal that satisfies a linear observation equation. In this paper, we consider the design of low-dimensional (compressive) measurement matrices for detecting… (More)

BACKGROUND
Green tea is one of the most popular beverages in the world. It is believed to have beneficial effects in the prevention and treatment of many diseases, one of which is nonalcoholic fatty liver disease (NAFLD). The present study investigated the effects of consumption of green tea in NAFLD patients.
METHODS
This study was a double-blind,… (More)

—We describe a method of constructing a sequence (pulse train) of phase-coded waveforms, for which the ambiguity function is free of range sidelobes along modest Doppler shifts. The constituent waveforms are Golay complementary waveforms which have ideal ambiguity along the zero Doppler axis but are sensitive to nonzero Doppler shifts. We extend this… (More)