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Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units

Publications associées (29)

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

AMER PHYSICAL SOC2023

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Zheng Dang, Zhen Wei, Jiancheng Yang, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

Early decoding of walking tasks with minimal set of EMG channels

Silvestro Micera, Federica Barberi, Eugenio Anselmino, Francesco Iberite

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decod ...

IOP Publishing Ltd2023

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

Mario Paolone, Willem Lambrichts

This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

IEEE2023

Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

Jonathan Koh Boon Han

Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. Here, we develop a joint model for the occurrence intensity and t ...

2023

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

Meritxell Bach Cuadra, Hamza Kebiri

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...

Springer2023

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

Tiffany Abitbol

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 ...

TECH ASSOC PULP PAPER IND INC2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Chao Cui, Florent Gérard Krzakala, Lenka Zdeborová

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

Tobias Kober, Davide Piccini

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

Wiley2023