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Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning

Publications associées (29)

Digital Twins of Stone Masonry Buildings for Damage Assessment

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

Springer2024

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

EPFL2024

Improving Object Detection under Domain Shifts

Vidit Vidit

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

EPFL2023

In-vivo investigation of electrochemical behavior of Ti and CoCrMo alloy in human synovial fluids

Yueyue Bao

With the development of modern medical techniques, the application of orthopedic implants has been continually increasing in recent years. Passive materials, such as Ti and its alloys as well as CoCrMo alloys, are widely used in artificial joints due to th ...

EPFL2023

Review on Uric Acid Recognition by MOFs with a Future in Machine Learning

Shaswati Mandal

Uric acid (UA) is produced from purine metabolism and serves as a prevalent biomarker for multiple diseases including cancer. Hyperuricemia or hypouricemia can cause multiple dysfunctions throughout the biological processes. Consequently, there is a pressi ...

Amer Chemical Soc2023

Surrogate modelling for seismic collapse risk assessment of steel moment resisting frames

Dimitrios Lignos, Andronikos Skiadopoulos, Nenad Bijelic

With the overall goal of reducing the numerical burden of uncertainty quantification involved in seismic risk assessments, this paper examines opportunities for assessment of seismic collapse capacity and risk using data-driven surrogates as a complement t ...

2023

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Martin Weigert, Benjamin Tobias Gallusser, Max Stieber

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Springer International Publishing Ag2023

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Clémentine Léa Aguet

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

EPFL2023

Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling

Sophia Haussener, Etienne Boutin, Shuo Liu, Evan Fair Johnson

Electrochemical conversion of CO2 to fuels and valuable products is one pathway to reduce CO2 emissions. Electrolyzers using gas diffusion electrodes (GDEs) show much higher current densities than aqueous phase electrolyzers, yet models for multi-physical ...

2023