OptiMoS: Optimal Sensing for Mobile Sensors

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Publications associées (29)

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Cristina Ramona Cudalbu

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

FRONTIERS MEDIA SA2023

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

David Atienza Alonso, Silvio Zanoli, Alexandre Sébastien Julien Levisse, Tomas Teijeiro Campo, Flavio Ponzina

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

2023

Modelling of metal alloys in realistic conditions with machine learning

Nataliya Lopanitsyna

Computer simulations based on statistical methods have emerged as a powerful tool for studying structure-property relationships at the atomistic level. However, to provide reliable insights into materials in realistic conditions, it is essential to accurat ...

EPFL2023

In situ detection of multitarget impurities on contact lens by electrochemical scanning probe

Fang Liu, Tzu-En Lin, Yingdi Zhu

Identifying multitarget impurities on contact lenses is challenging because traditional methods such as polymerase chain reaction cannot be used on the unknown target. Other sampling methods often require expensive antibodies or dyes. Therefore, this resea ...

ELSEVIER SCIENCE SA2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Jürg Alexander Schiffmann, Cyril Picard, Faez Ahmed

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Amer Soc Mechanical Engineers2023

The inductive bias of deep learning: Connecting weights and functions

Guillermo Ortiz Jimenez

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

EPFL2023

First detection of the BAO signal from early DESI data

Anand Stéphane Raichoor, Andrei Variu, Cheng Zhao, David Schlegel

We present the first detection of the baryon acoustic oscillations (BAOs) signal obtained using unblinded data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). From a selected sam ...

Oxford Univ Press2023

Vibrational NDT with Under-sampled Data through Physics-informed Neural Networks

Olga Fink

The vibrational response of solid materials and structural components is substantially governed by their mechanical and geometrical properties. Low-frequency vibrations and modal frequencies are sensitive to global geometrical deviations, while high-freque ...

2023

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