Publications associées à Compressed sensing : a universal energy-efficient compression scheme for biosignals on wireless body sensor nodes

Publications associées (23)

Fiducial-cosmology-dependent systematics for the DESI 2024 BAO analysis

Jiaxi Yu, Antoine Philippe Jacques Rocher

When measuring the Baryon Acoustic Oscillations (BAO) scale from galaxy surveys, one typically assumes a fiducial cosmology when converting redshift measurements into comoving distances and also when defining input parameters for the reconstruction algorit ...

IOP Publishing Ltd2025

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

Luca Furieri, Andrea Martin

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

2024

WILD SOLUTIONS TO SCALAR EULER-LAGRANGE EQUATIONS

Carl Johan Peter Johansson

. We study very weak solutions to scalar Euler-Lagrange equations associated with quadratic convex functionals. We investigate whether W1,1 solutions are necessarily W 1,2 Nash and Schauder applicable. We answer this question positively for a suitable clas ...

Amer Mathematical Soc2024

Event Data Stream Compression Based on Point Cloud Representation

Touradj Ebrahimi, Bowen Huang

The Dynamic Vision System (DVS) is a novel image acquisition system that works only when there is a brightness change in a pixel, resulting in a stream of events including timestamps, spatial coordinates and the sign of the bright- ness change (increase or ...

2023

Augmenting Deep Classifiers with Polynomial Neural Networks

Grigorios Chrysos

Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been achieved by a variety of recently proposed architectures, the majority of which ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Christopher Arnold Criscitiello, Nicolas Boumal

We describe the first gradient methods on Riemannian manifolds to achieve accelerated rates in the non-convex case. Under Lipschitz assumptions on the Riemannian gradient and Hessian of the cost function, these methods find approximate first-order critical ...

SPRINGER2022

Benchmarking of objective quality metrics for point cloud compression

Touradj Ebrahimi, Evangelos Alexiou, Davi Nachtigall Lazzarotto

Point cloud is a promising imaging modality for the representation of 3D media. The vast volume of data associated with it requires efficient compression solutions, with lossy algorithms leading to larger bit-rate savings at the expense of visual impairmen ...

IEEE2021

Oligomodal metamaterials with multifunctional mechanics

Aleksi Antoine Bossart

Mechanical metamaterials are artificial composites that exhibit a wide range of advanced functionalities such as negative Poisson's ratio, shape shifting, topological protection, multistability, extreme strength-to-density ratio, and enhanced energy dissip ...

National Academy of Sciences2021

Dynamic Model Pruning with Feedback

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Luis Felipe Barba Flores

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

2020

Linear Speedup In Saddle-Point Escape For Decentralized Non-Convex Optimization

Ali H. Sayed, Stefan Vlaski

Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to no ...

IEEE2020

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Ali H. Sayed, Sulaiman A S A E Alghunaim

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

Graph-Based Detection of Seams In 360-Degree Images

Pascal Frossard, Francesca De Simone, Roberto Gerson De Albuquerque Azevedo, Sohyeong Kim

In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly occur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) project ...

2019

Dictionary Compression in Point Cloud Data Management

Anastasia Ailamaki, Mirjana Pavlovic

Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies such as dense image matching and airborne LiDAR scanning. With the increase in volume and precision, point cloud data offers a ...

ASSOC COMPUTING MACHINERY2019

Linear-parameter-varying approximation of nonlinear dynamics for model predictive flow control of urban multi-region systems

Nikolaos Geroliminis, Mohammadreza Saeedmanesh, Anastasios Kouvelas

An alternative approach for real-time network- wide traffic control in cities that has recently gained a lot of interest is perimeter flow control. The focus of the current work is to study two aspects that are not covered in the perimeter control literatu ...

2019

A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

Robert Gerhard Jérôme Luce

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust met ...

Ieee-Inst Electrical Electronics Engineers Inc2018

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

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

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

Data-Driven Controller Design for Atomic-Force Microscopy

Alireza Karimi, Georg Fantner, Adrian Pascal Nievergelt, Christoph Manuel Kammer

A novel method to design data-driven, fixed-structure controllers with H2 and H∞ performance objectives is presented. The control design problem is transformed into a convex optimization problem with linear matrix inequality constraints, which can be solve ...

2017

Complexity Certification of the Fast Alternating Minimization Algorithm for Linear MPC

Colin Neil Jones, Melanie Nicole Zeilinger, Ye Pu

In this technical note, the fast alternating minimization algorithm (FAMA) is proposed to solve model predictive control (MPC) problems with polytopic and second-order cone constraints. Two splitting strategies with efficient implementations for MPC proble ...

2017

Active control for mid-span connection of a deployable tensegrity footbridge

Ian Smith, Nicolas Willy Veuve, Seif Dalil Safaei

Tensegrity structures are spatial self-stressed pin jointed structures where compression components (struts) are surrounded by tension elements. This paper describes a near full-scale deployable tensegrity footbridge that deploys from both sides and connec ...

2016

A totally unimodular view of structured sparsity

Volkan Cevher, Marwa El Halabi

This paper describes a simple framework for structured sparse recovery based on convex optimization. We show that many interesting structured sparsity models can be naturally represented by linear matrix inequalities on the support of the unknown parameter ...

2015