Publications by Luca Baldassarre

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Volkan Cevher, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

A power and area effcient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case ...

2018

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

Volkan Cevher, Catherine Dehollain, Mahsa Shoaran, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre, Kerim Türe, Gürkan Yilmaz, Franco Maloberti

Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system’s area and power. In this paper, we ...

2018

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Volkan Cevher, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case ...

ASSOC COMPUTING MACHINERY2018

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Volkan Cevher, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

This work presents an area and power efficient encoding system for wireless implantable devices capable of monitoring the electrical activity of the brain. Such devices are becoming an important tool for understanding, real-time monitoring, and potentially ...

2017

Learning-based subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

Group-Sparse Model Selection: Hardness and Relaxations

Volkan Cevher, Anastasios Kyrillidis, Luca Baldassarre, Nirav Bhan

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of ``interpretable" signals along with ...

Institute of Electrical and Electronics Engineers2016

Convex block-sparse linear regression with expanders - provably

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Luca Baldassarre, Bubacarr Bah

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-ti ...

2016

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Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding and potentially treating mental diseases such as epilepsy and depression. While such devices exist, it is still necessa ...

Ieee2016

Learning-Based Compressive Subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The problem of recovering a structured signal

\mathbf{x} \in \mathbb{C}^p

from a set of dimensionality-reduced linear measurements

\mathbf{b} = \mathbf {A}\mathbf {x}

arises in a variety of applications, such as medical imaging, spectroscopy, Fourier o ...

Ieee-Inst Electrical Electronics Engineers Inc2016

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We consider the problem of finding a K-sparse approximation of a signal, such that the support of the approximation is the union of sets from a given collection, a.k.a. group structure. This problem subsumes the one of finding K-sparse tree approximations. ...

2015

Luca Baldassarre

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An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Learning-based subsampling

Group-Sparse Model Selection: Hardness and Relaxations

Convex block-sparse linear regression with expanders - provably

Learning-Based Compressive Subsampling

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Group-Sparse Model Selection: Hardness and Relaxations

Convex block-sparse linear regression with expanders - provably

Learning-Based Compressive Subsampling

Learning-based subsampling

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems