Identification of multi-input systems: variance analysis and input design issues

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Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

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 ...

The ITER Electron Cyclotron Resonance Heating and Current Drive (ECRH&CD) system relies on 1 MW 170 GHz Gyrotrons to provide the mm-wave power needed for plasma heating, current drive, and magneto-hydrodynamic control. The design and modeling of the contro ...

We study the hitting probabilities of the solution to a system of d stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive (d-6)\documentclass[12pt]{minimal} \usepackage{ ...

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

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 ...

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 ...

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 ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

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 ...

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 ...

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 ...

We study the compact support property for solutions of the following stochastic partial differential equations: partial derivative tu=aijuxixj(t,x)+biuxi(t,x)+cu+h(t,x,u(t,x))F-center dot(t,x),(t,x)is an element of(0,infinity)xRd,where F-center dot is a sp ...

DNA mechanics plays a crucial role in many biological processes, including nucleosome positioning and protein-DNA interactions. It is believed that nature employs epigenetic modifications in DNA to further regulate gene expression. Moreover, double-strande ...

The nitrogen-vacancy (NV) center in diamond is a powerful and versatile quantum sensor for diverse quantities. In particular, relaxometry (or T1), can be used to detect magnetic noise at the nanoscale. For experiments with single NV centers the analysis of ...

Identification of multi-input systems: variance analysis and input design issues

Chattez avec Graph Search

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

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

Sensitivity Analysis of the Lumped Thermal Model of the EU 170 GHz Gyrotron Magnetron Injection Gun

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

The Privacy Power of Correlated Noise in Decentralized Learning

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

Universal and adaptive methods for robust stochastic optimization

Multi-agent Learning with Privacy Guarantees

Disentangling 1/f noise from confined ion dynamics

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Distributionally Robust Linear Quadratic Control

Robust Collaborative Learning with Linear Gradient Overhead

Functional data analysis with rough sample paths?

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

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

THE COMPACT SUPPORT PROPERTY FOR SOLUTIONS TO THE STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS WITH COLORED NOISE

cgNA+: A sequence-dependent coarse-grain model of double-stranded nucleic acids

Optimizing Data Processing for Nanodiamond Based Relaxometry