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Intrigued by some recent results on impulse response estimation by kernel and nonparamet-ric techniques, we revisit the old problem of transfer function estimation from input-output measurements. We formulate a classical regularization approach, focused on finite impulse response (FIR) models, and find that regularization is necessary to cope with the high(More)
In this paper, a new particle filter (PF) which we refer to as the decentralized PF (DPF) is proposed. By first decomposing the state into two parts, the DPF splits the filtering problem into two nested subproblems and then handles the two nested subproblems using PFs. The DPF has the advantage over the regular PF that the DPF can increase the level of(More)
Given a linear system in a real or complex domain, linear regression aims to recover the model parameters from a set of observations. Recent studies in compressive sensing have successfully shown that under certain conditions, a linear program, namely, 1-minimization, guarantees recovery of sparse parameter signals even when the system is underdetermined.(More)
This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem and takes the form of a least-squares problem with a sum-of-norms regularization, a generalization of the 1-regularization. The(More)
Segmentation of time-varying systems and signals into models whose parameters are piecewise constant in time is an important and well studied problem. It is here formulated as a least-squares problem with sum-of-norms regularization over the state parameter jumps, a generalization of 1-regularization. A nice property of the suggested formulation is that it(More)
The phase retrieval problem has a long history and is an important problem in many areas of optics. Theoretical understanding of phase retrieval is still limited and fundamental questions such as uniqueness and stability of the recovered solution are not yet fully understood. This paper provides several additions to the theoretical understanding of sparse(More)
We present a novel approach to interactive and concurrent volume visualization of functional Magnetic Resonance Imaging (fMRI). While the patient is in the scanner, data is extracted in real-time using state-of-the-art signal processing techniques. The fMRI signal is treated as light emission when rendering a patient-specific high resolution reference MRI(More)
— We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived(More)
—We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the(More)