Publications related to A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

A compressed beamforming framework for ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson, Olivier Bernard

Classical beamforming methods, based on Delay- And-Sum (DAS) require an extensive number of samples and delay calculations to obtain high-quality images. Compressed Beamforming (CB) proposes an alternative to DAS, based on compressed sensing, which aims at ...

Ieee2016

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In recent works, sparse models and convex optimization techniques have been applied to radio-interferometric (RI) imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. In this talk, I will review our latest contribut ...

2015

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Alireza Karimi, Achille Nicoletti, Zlatko Emedi

In this paper, a new method for designing robust fixed-order

\mathcal{H}_{\infty}

discrete-time controllers is presented. The controller structure is a two-degree of freedom polynomial controller of the

RST

type. A data-driven approach is implemented f ...

IEEE2015

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

Abstract. The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has ...

2015

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has heretofor ...

2015

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Alireza Karimi

In this paper, a new algorithm is proposed for the design of a family of controllers to be used within an adaptive switching control scheme. The resulting switching controller is able to attenuate the effects of disturbances having uncertain and possibly t ...

2015

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing framework in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on multiple unknown (but sparse) signals and formulate the ...

Institute of Electrical and Electronics Engineers2014

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If the acquired data is to be sent to a far-away base station, collaborative beamforming performed by the sensors may help to distribute the communication load amon ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Convex Optimization for Big Data

Volkan Cevher

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniqu ...

Institute of Electrical and Electronics Engineers2014

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

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A compressed beamforming framework for ultrafast ultrasound imaging

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Convex Optimization for Big Data

Learning with tensors: a framework based on convex optimization and spectral regularization

A compressed beamforming framework for ultrafast ultrasound imaging

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Learning with tensors: a framework based on convex optimization and spectral regularization

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Convex Optimization for Big Data

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability