Results 141 to 150 of about 36,894 (295)
Chemically Doped Conductive Polymers for Wearable Health Monitoring
Among conductive polymers, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), polyaniline (PANI), and polypyrrole (PPy) are the most studied and applied. Chemical doping significantly boosts intrinsic conductivity and mechanical robustness.
Mengdi Zuo +5 more
wiley +1 more source
Probabilistic learning on manifold for optimization under uncertainties
Plenary LectureInternational audienceThis paper presents a challenging problem devoted to the probabilistic learning on manifold for the optimization under uncertainties and a novel idea for solving it.
Ghanem, Roger, Soize, Christian
core +1 more source
Non-local manifold tangent learning
We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suffer from the curse of dimensionality, at the dimension of the true ...
Yoshua Bengio +3 more
core
Smart Closed‐Loop Systems in Personalized Healthcare: Advances and Outlook
A smart closed‐loop e‐textile integrates multimodal sensing, onboard processing, wireless communication, and wearable power to enable real‐time physiological/biochemical monitoring and feedback‐controlled therapy. ABSTRACT Smart textiles represent a revolutionary frontier in healthcare, seamlessly blending fabric and advanced technologies to create ...
Safoora Khosravi +12 more
wiley +1 more source
Fabric‐Based Wearable Robotic Exoskeleton Gloves: Advancements and Challenges
This review highlights interdisciplinary technological advances in fabric‐based robotic gloves, focusing on progress in design, fabrication, actuation, sensing, control, and power and energy requirements. It also addresses performance testing and validation, including biomechanical, strength, functional, user experience, and durability assessments, to ...
Ayse Feyza Yilmaz +2 more
wiley +1 more source
Estimation of smooth vector fields on manifolds by optimization on Stiefel group
Real data are usually characterized by high dimensionality. However, real data obtained from real sources, due to the presence of various dependencies between data points and limitations on their possible values, form, as a rule, form a small part of the
E.N. Abramov, Yu.A. Yanovich
doaj
ABSTRACT Quantifying oral polymorphonuclear neutrophils (oPMNs) is a clinically validated approach for assessing periodontal inflammation. However, current methods, such as manual hemocytometry and flow cytometry, are time‐consuming (>3 h), require invasive sampling, and depend on staining and complex instrumentation, making them unsuitable for point ...
Mohsen Hassani +9 more
wiley +1 more source
Megaman: Scalable Manifold Learning in Python
Manifold Learning (ML) is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus, ML algorithms are most applicable to highdimensional data and require large sample sizes to accurately estimate the ...
James Mcqueen +3 more
core
Canonical normalizing flows for manifold learning
Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible ...
Flouris, Kyriakos, Konukoglu, Ender
core
An automation interface for environmental scanning electron microscopy (ESEM) enables simultaneous, interlaced data sets via frame‐by‐frame parameter changes. Demonstrated on oscillatory hydrogen oxidation over cobalt (Co) foil, dual‐magnification imaging bridges mesoscopic to microscopic length scales, capturing alternating views of surface dynamics ...
Maurits Vuijk +7 more
wiley +1 more source

