Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

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We prove global in time well-posedness for perturbations of the 2D stochastic Navier-Stokes equations partial derivative( t)u + u center dot del u = Delta u - del p + sigma + xi, u(0, center dot ) = u(0),div (u) = 0, driven by additive space-time white noi ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

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 ...

The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...

Applications demanding imaging at low-light conditions at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths, such as quantum information science, biophotonics, space imaging, and light detection and ranging (LiDAR), have accelerated the develo ...

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

This paper proposes a novel method to improve georeferencing of airborne laser scanning by improved trajectory estimation using Vehicle Dynamic Model. In Vehicle Dynamic Model (VDM), the relationship between the dynamics of the platform and control inputs ...

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still ...

Ion transport through biological and solid-state nanochannels is known to be a highly noisy process. The power spectrum of current fluctuations is empirically known to scale like the inverse of frequency, following the long-standing yet poorly understood H ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

This paper introduces Wireless IoT-based Noise Cancellation (WINC) which defines a framework for leveraging a wireless network of IoT microphones to enhance active noise cancellation in noise-canceling headphones. The IoT microphones forward ambient noise ...

As the world’s population grows in cities, urban dwellers spend a large amount of time inside their home, making housing health ever more important. Critical for residents’ health, the interactions between indoor residential environments and outdoor enviro ...

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and timeconsuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for foreca ...

The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. ...

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

Chattez avec Graph Search

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

Spectral Estimators for High-Dimensional Matrix Inference

Exploiting the Signal-Leak Bias in Diffusion Models

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

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

Silicon CMOS and InGaAs(P)/InP SPADs for NIR/SWIR detection

Functional data analysis with rough sample paths?

Model-based constraints for trajectory determination of quad-copters: Design, calibration & merits for direct orientation

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Disentangling 1/f noise from confined ion dynamics

Universal and adaptive methods for robust stochastic optimization

Multi-agent Learning with Privacy Guarantees

WINC: A Wireless IoT Network for Multi-Noise Source Cancellation

Housing, street and health: a new systemic research framework

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

A Shape Derivative Approach to Domain Simplification

An equilibrated flux a posteriori error estimator for defeaturing problems

Analysis-aware defeaturing of complex geometries with Neumann features

Regularization for distributionally robust state estimation and prediction