Ali Hormati

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Related publications (20)

Sensing and Recovery under Sparsity Constraints : Theory and Applications

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Distributed Sensing of Signals Linked by Sparse Filtering

Martin Vetterli, Olivier Roy, Ali Hormati, Yue Lu

Certain aspects of the present disclosure provide methods for distributed sensing and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. ...

2010

Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

Martin Vetterli, Olivier Roy, Ali Hormati, Yue Lu

We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin to a Slepian-Wolf setup, but in the sampling rather than the lossless compres ...

2010

Temperature Monitoring During Tissue Freezing Using Ultrasound Speed Measurements

Martin Vetterli, Ali Hormati, Ivana Jovanovic

A major limitation of thermal therapies is the lack of detailed thermal information needed to monitor the therapy. Temperatures are routinely measured invasively with thermocouples, but only sparse measurements can be made. Ultrasound tomography is an attr ...

2009

Distributed Sensing of Signals Linked by Sparse Filtering

Martin Vetterli, Olivier Roy, Ali Hormati, Yue Lu

We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. A general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requir ...

2009

Distributed Sensing of Signals under a Sparse Filtering Model

Martin Vetterli, Olivier Roy, Ali Hormati, Yue Lu

We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. Two different scenarios are considered: In the case of universal reconstruction, we look for a sensing and recove ...

2009

Compressed Sensing with Probabilistic Measurements: A Group Testing Solution

Martin Vetterli, Amin Karbasi, Ali Hormati, Mahdi Cheraghchi Bashi Astaneh

Detection of defective members of large populations has been widely studied in the statistics community under the name ``group testing'', a problem which dates back to World War~II when it was suggested for syphilis screening. There the main interest is to ...

2009

Distributed Compressed Sensing: Sparsity Models and Reconstruction Algorithms Using Annihilating Filter

Martin Vetterli, Ali Hormati

Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some common parts, and some variations. The question is how to acquire such signals and ...

2008

Time of flight estimation method using beamforming for acoustic tomography

Martin Vetterli, Olivier Roy, Ali Hormati, Ivana Jovanovic

It is disclosed an acoustic tomography method to improve the time of flight estimation, said method comprising the steps of: sequentially triggering a set of N transmitters so as to generate a sequence of N acoustic waves through a volume being scanned; re ...

2008

Efficient and Stable Acoustic Tomography Using Sparse Reconstruction Methods

Martin Vetterli, Luciano Sbaiz, Ali Hormati, Ivana Jovanovic

We study an acoustic tomography problem and propose a new inversion technique based on sparsity. Acoustic tomography observes the parameters of the medium that influence the speed of sound propagation. In the human body, the parameters that mostly influenc ...

2007

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