Real-time compensation of hysteresis in piezoelectric-stack actuator tracking a stochastic reference

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Thin-laminate composites with thicknesses below 200 mu m hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusi ...

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 ...

Stochastic phenomena are often described by Langevin equations, which serve as a mesoscopic model for microscopic dynamics. It has been known since the work of Parisi and Sourlas that reversible (or equilibrium) dynamics present supersymmetries (SUSYs). Th ...

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Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

Human motion prediction, the task of predicting future 3D human poses given a sequence of observed ones, has been mostly treated as a deterministic problem. However, human motion is a stochastic process: Given an observed sequence of poses, multiple future ...

A feedback control design is proposed for stochastic systems with finite second moment which aims at maximising the region of attraction of the equilibrium point. Polynomial Chaos (PC) expansions are employed to represent the stochastic closed loop system ...

The use of a Bayesian filter (e.g., Kalman filter) for the fusion of information from satellite positioning and inertial navigation is a common approach in many applications, where the knowledge of position, velocity, and attitude in space are of great int ...

Inertial sensors are increasingly being employed in different types of applications. The reduced cost and the extremely small size makes them the number-one-choice in miniature embedded devices like phones, watches, and small unmanned aerial vehicles. The ...

In this paper, we study the Besov regularity of Levy white noises on the d-dimensional torus. Due to their rough sample paths, the white noises that we consider are defined as generalized stochastic fields. We, initially, obtain regularity results for gene ...

We establish in the world of stochastic processes a theoretical relation between sparsity and wavelets. The underlying principle is to treat stochastic processes as generalized functions, which facilitates the study of their properties in a transform domai ...

Although our work lies in the field of random processes, this thesis was originally motivated by signal processing applications, mainly the stochastic modeling of sparse signals. We develop a mathematical study of the innovation model, under which a signal ...

We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. O ...

We propose and analyze a novel Multi Index Monte Carlo (MIMC) method for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The MIMC method is both a s ...

This thesis is devoted to the derivation of error estimates for partial differential equations with random input data, with a focus on a posteriori error estimates which are the basis for adaptive strategies. Such procedures aim at obtaining an approximati ...

Feature extraction from a multichannel compressed neural signal is introduced in this brief. Compressive sensing (CS) is an efficient method for reducing the transmission data rate of sparse biological signals and lowering the power consumption of resource ...

We analyze the stochastic initial-boundary value problem for the elastic wave equation with random coefficients and deterministic data. We propose a stochastic collocation method for computing statistical moments of the solution or statistics of some given ...

The advection diffusion equation is one of the most widespread equations in physics. It arises quite often in the context of sediment transport, e.g., for describing time and space variations in the particle activity (the solid volume of particles in motio ...

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating de ...

Hermite splines are commonly used for interpolating data when samples of the derivative are available, in a scheme called Hermite interpolation. Assuming a suitable statistical model, we demonstrate that this method is actually optimal for reconstructing r ...

Real-time compensation of hysteresis in piezoelectric-stack actuator tracking a stochastic reference

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Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Functional data analysis with rough sample paths?

Supersymmetries in nonequilibrium Langevin dynamics

Thermally-Compensated Magnetic Core Loss Model for Time-Domain Simulations of Electrical Circuits

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

A Stochastic Conditioning Scheme for Diverse Human Motion Prediction

Feedback Control Design Maximizing the Region of Attraction of Stochastic Systems Using Polynomial Chaos Expansion

Calibration Aspects of INS Navigation

An overview of a new sensor calibration platform

On the Besov regularity of periodic Levy noises

Optimal Representations of Sparse Stochastic Processes with Applications in Image Processing

Gaussian versus Sparse Stochastic Processes : Construction, Regularity, Compressibility

Adaptive-Rate Reconstruction of Time-Varying Signals with Application in Compressive Foreground Extraction

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A posteriori error estimation for partial differential equations with random input data

A Fully-Integrated IC with 0.85-uW/Channel Consumption for Epileptic iEEG Detection

Analysis and computation of the elastic wave equation with random coefficients

Stochastic interpretation of the advection diffusion equation and its relevance to bed load transport

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

Statistical Optimality of Hermite Splines