Publications by Ali Hormati | EPFL Graph Search

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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 ...

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 ...

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are indiv ...

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 ...

In sensor networks and communication systems, one is often confronted with sampling multiple sparse signals having a common support set. Multipath channels in a multiple-input multiple-output (MIMO) wireless communication setting is an interesting example ...

Identification of defective members of large populations has been widely studied in the statistics community under the name of group testing. It involves grouping subsets of items into different pools and detecting defective members based on the set of tes ...

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 ...

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 ...

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 ...

We present preliminary results obtained using a time domain wave-based reconstruction algorithm for an ultrasound transmission tomography scanner with a circular geometry. While a comprehensive description of this type of algorithm has already been given e ...

A device and method for estimating multipath jointly sparse channels. The method comprises receiving a number K of signal components by a number P of receiving antennas, where P≧2. The method further comprises estimating the sparsity condition of the multi ...

We present a bent ray reconstruction algorithm for an ultrasound tomography (UT) scanner designed for breast screening. The scanner consists of a circular array of transmitters and receivers which encloses the object to be imaged. By solving a nonlinear sy ...

We propose an algorithm (SCS-FRI) to estimate multipath channels with Sparse Common Support (SCS) based on Finite Rate of Inno- vation (FRI) sampling. In this setup, theoretical lower-bounds are derived, and simulation in a Rayleigh fading environment show ...

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 ...

According to a first aspect of the present invention there is provided a method of estimating, jointly, a set of multipath channels having a common path support, the method comprising the steps of, estimating jointly the common path support of the set of m ...

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. ...

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 ...

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 ...

Embodiments include a method for sending a selected number of pilots (20) to a sparse channel having a channel impulse response limited in time comprising sending the selected number of the pilots (20). The pilots (20) are equally spaced in the frequency d ...

The present paper proposes and studies an algorithm to estimate chan- nels with a sparse common support (SCS). It is a generalization of the classical sampling of signals with Finite Rate of Innovation (FRI) and thus called SCS-FRI. It is applicable to OFD ...

Ali Hormati

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Sensing and Recovery under Sparsity Constraints : Theory and Applications

Compressed Sensing with Probabilistic Measurements: A Group Testing Solution

Estimation of Sparse MIMO Channels with Common Support

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

Compressive Sampling of Multiple Sparse Signals Having Common Support Using Finite Rate of Innovation Principles

Group Testing with Probabilistic Tests: Theory, Design and Application

Distributed Sensing of Signals under a Sparse Filtering Model

Temperature Monitoring During Tissue Freezing Using Ultrasound Speed Measurements

Distributed Sensing of Signals Linked by Sparse Filtering

Sound Speed Estimation Using Wave-based Ultrasound Tomography: Theory and GPU Implementation

Fast and robust estimation of jointly sparse channels

Robust Ultrasound Travel-time Tomography Using the Bent Ray Model

Estimating Sparse MIMO Channels Having Common Support

Efficient and Stable Acoustic Tomography Using Sparse Reconstruction Methods

ESTIMATING SPARSE MIMO CHANNELS HAVING COMMON SUPPORT

Distributed Sensing of Signals Linked by Sparse Filtering

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

Time of flight estimation method using beamforming for acoustic tomography

Methods and apparatus for estimating a sparse channel

Sampling of Sparse Channels with Common Support