In this paper the sampled signal reconstruction problem is formulated and solved as the sampled-data H smoothing problem, in which an analog reconstruction error is minimized. Both infinite (non-causal reconstructors) and finite (reconstructors with relaxed causality) preview cases are considered. The optimal reconstructors are in the form of the cascade of a discrete-time smoother and a genera...

Many problems in signal processing and statistical inference are based on finding a sparse solution to an undetermined linear system. The reference approach to this problem of finding sparse signal representations, on overcomplete dictionaries, leads to convex unconstrained optimization problems, with a quadratic term l2, for the adjustment to the observed signal, and a coefficient vector l1-no...

This paper describes the use of stochastic simulation techniques to reconstruct biomedical signals not directly measurable. In particular, a deconvolution problem with an uncertain clearance parameter is considered. The problem is addressed using a Monte Carlo Markov Chain method, called the Gibbs Sampling, in which the joint posterior probability distribution of the stochastic parameters is es...

Compression of digital ElectroCardioGram (ECG) signals has traditionally been tackled by heuristical approaches. Recently, it has been demonstrated [1] that exact optimization algorithms outclass these heuristical approaches by a wide margin with respect to reconstruction error. As opposed to traditional time-domain algorithms, where some heuristic is used to extract representative signal sampl...

In this paper, we address the problem of sparse signal reconstruction using compressive sampling (CS) in the presence of unknown multiplicative perturbations. Such perturbations cause mismatch between the true signal basis and that in the measurements. We propose an algorithm which iteratively determines active bases, estimates the mismatch in the identified active bases, and adjusts the CS rec...

This article focuses on techniques for acoustic noise reduction, signal filters and source reconstruction. For noise reduction, bandpass filters and cross correlations are found to be efficient and fast ways to improve the signal to noise ratio and identify a possible neutrino-induced acoustic signal. The reconstruction of the position of an acoustic point source in the sea is performed by usin...

This paper explores the possibility of processing noisy speech using signal reconstruction algorithrns frorn Fourier Transform (FT) phase and rnagnitude. Algorithrns have been proposed in the literature for signal reconstruction frorn FT phase alone, or, frorn FT rnagnitude with additional inforrnation in the form of 1-bit phase or signal values. More recently, algorithrns have been proposed fo...

This work solves the signal reconstruction problem involving nonuniform filter bank systems with rational decimation factors and noises. Three main nonuniform filter bank systems, i.e., filter-block decimator (FBD) structure, upsamplerfilter-downsampler (UFD) structure, and tree structure, are included in this study. According to different operating conditions, two different signal reconstructi...

Compressive sensing (CS) is a novel framework which exploits both the sparsity and the intra-correlation of the signal in structural health monitoring (SHM) based on wireless sensor networks (WSNs). It contains sparse signal representation, the measurement matrix selection and the reconstruction algorithm. The SHM signal is recovered by M measurements following the restricted isometry constant ...