Bit Precision Analysis for Compressed Sensing

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Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

Smart contracts have emerged as the most promising foundations for applications of the blockchain technology. Even though smart contracts are expected to serve as the backbone of the next-generation web, they have several limitations that hinder their wide ...

We prove a sharp quantitative version of the Faber–Krahn inequality for the short-time Fourier transform (STFT). To do so, we consider a deficit which measures by how much the STFT of a function fails to be optimally concentrated on an arbitrary set of pos ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

The high computational costs of deep convolutional neural networks hinder their deployment in real-world applications, including pulmonary nodule detection from CT scans where large 3D image sizes amplify the issue. This paper presents a novel 3D method to ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

The locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm is a popular approach for computing a few smallest eigenvalues and the corresponding eigenvectors of a large Hermitian positive definite matrix A. In this work, we propose a mix ...

Fast camera imaging is used to study ion acoustic waves propagating azimuthally in a magnetized plasma column. The high-speed image sequences are analyzed using proper orthogonal decomposition and 2D Fourier transform, allowing to evaluate the assets and d ...

Euclid's photometric galaxy cluster survey has the potential to be a very competitive cosmological probe. The main cosmological probe with observations of clusters is their number count, within which the halo mass function (HMF) is a key theoretical quanti ...

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization o ...

Measuring the size of cellulose nanomaterials can be challenging, especially in the case of branched and entangled cellulose nanofibrils (CNFs). The International Organization for Standardization, Technical Committee 6, Task Group 1-Cellulosic Nanomaterial ...

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data -driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are base ...

The vibrational response of solid materials and structural components is substantially governed by their mechanical and geometrical properties. Low-frequency vibrations and modal frequencies are sensitive to global geometrical deviations, while high-freque ...

This study presents a novel framework for evaluating the luminance measurement capabilities of High Dynamic Range (HDR) sensor cameras in indoor glare conditions. Results indicate that the practical usage range of the CSEM VIP camera is significantly lower ...

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electron ...

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

The Schur decomposition of a square matrix A is an important intermediate step of state-of-the-art numerical algorithms for addressing eigenvalue problems, matrix functions, and matrix equations. This work is concerned with the following task: Compute a (m ...

Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm. However, many applications involve an underlying continuous signal, and a more natural choi ...

Bit Precision Analysis for Compressed Sensing

Chattez avec Graph Search

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Stability of the Faber-Krahn inequality for the short-time Fourier transform

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Overflow-free compute memories for edge AI acceleration

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

A mixed precision LOBPCG algorithm

Nonlinear interactions of ion acoustic waves explored using fast imaging decompositions

Euclid preparation XXIV. Calibration of the halo mass function in λ(ν)CDM cosmologies

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

Setting priorities in CNF particle size measurement: What is needed vs. what is feasible

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Vibrational NDT with Under-sampled Data through Physics-informed Neural Networks

Performance investigation of cameras using HDR sensors for daylight glare evaluations

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Iterative refinement of Schur decompositions

pyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration