Preferential estimation

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We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced ...

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 ...

Self-exciting point processes, widely used to model arrival phenomena in nature and society, are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded only as bin counts on a finite partition of the observatio ...

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligen ...

The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed a ...

Orthogonal maps are the solutions of the mathematical model of paper-folding, also called the origami problem. They consist of a system of first-order fully nonlinear equations involving the gradient of the solution. The Dirichlet problem for orthogonal ma ...

This thesis develops models of activity and destination choices in pedestrian facilities from WiFi traces. We adapt the activity-based travel demand analysis of urban mobility to pedestrians and to digital footprints. We are interested in understanding the ...

We investigate a stochastic signal-processing framework for signals with sparse derivatives, where the samples of a Levy process are corrupted by noise. The proposed signal model covers the well-known Brownian motion and piecewise-constant Poisson process; ...

Productivity, quality, safety, and environmental concerns have driven major advancements in the development of process analyzers. Analyzers generate measurement data that are useful for characterizing product and process attributes (key variables), thereby ...

Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...

Preferential estimation

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Homothetic Tube Model Predictive Control With Multi-Step Predictors

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

Estimation of Self-Exciting Point Processes from Time-Censored Data

Real-Time Localization for Closed-Loop Control of Assistive Furniture

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

Numerical Approximation Of Orthogonal Maps

Activity choice modeling for pedestrian facilities

MMSE Estimation of Sparse Levy Processes

Subspace Correction Methods in Multivariate Calibration

Nonideal sampling and regularized interpolation of noisy data