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

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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 ...

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 ...

. 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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

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

Chattez avec Graph Search

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

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

WILD SOLUTIONS TO SCALAR EULER-LAGRANGE EQUATIONS

Event Data Stream Compression Based on Point Cloud Representation

Augmenting Deep Classifiers with Polynomial Neural Networks

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

Benchmarking of objective quality metrics for point cloud compression

Oligomodal metamaterials with multifunctional mechanics

Dynamic Model Pruning with Feedback

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

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

Graph-Based Detection of Seams In 360-Degree Images

Dictionary Compression in Point Cloud Data Management

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

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

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

Data-Driven Controller Design for Atomic-Force Microscopy

Complexity Certification of the Fast Alternating Minimization Algorithm for Linear MPC

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

Structure Tensor Total Variation