Results 121 to 130 of about 5,853,511 (292)

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Improvement of Supervised Shape Retrieval by Learning the Manifold Space

International Journal of Information and Communication Technology Research, 2012
Manifold learning is the technique that aims for finding a constructive way to embed the data from a highdimensional space into a low-dimensional one based on non-linear approaches.
Mohammad Ali Zare Chahooki, Nasrollah Moghadam Charkari   +1 more
doaj  

Multi-view data visualisation via manifold learning [PDF]

PeerJ Computer Science
Non-linear dimensionality reduction can be performed by manifold learning approaches, such as stochastic neighbour embedding (SNE), locally linear embedding (LLE) and isometric feature mapping (ISOMAP).
Theodoulos Rodosthenous, Vahid Shahrezaei, Marina Evangelou   +2 more
doaj   +2 more sources

Electron–Matter Interactions During Electron Beam Nanopatterning

Advanced Functional Materials, EarlyView.
This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima, Prathiba Thamizhavel, Taylor J. Woehl   +2 more
wiley   +1 more source

Toward Scalable Solutions for Silver‐Based Gas Diffusion Electrode Fabrication for the Electrochemical Conversion of CO2 – A Perspective

Advanced Functional Materials, EarlyView.
In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken, K. Ashoke Raman, Alexander Bauer, Violeta Karyofylli, Eva Jodat, André Karl, Rüdiger‐A. Eichel   +6 more
wiley   +1 more source

Deep Nets for Local Manifold Learning

Frontiers in Applied Mathematics and Statistics, 2018
The problem of extending a function f defined on a training data C on an unknown manifold 𝕏 to the entire manifold and a tubular neighborhood of this manifold is considered in this paper. For 𝕏 embedded in a high dimensional ambient Euclidean space ℝD, a
Charles K. Chui, Hrushikesh N. Mhaskar
doaj   +1 more source

Learning on Manifolds [PDF]

, 2010
Mathematical formulation of certain natural phenomena exhibits group structure on topological spaces that resemble the Euclidean space only on a small enough scale, which prevents incorporation of conventional inference methods that require global vector norms.
openaire   +1 more source

Recomposable Layered Metasurfaces for Wavelength‐Multiplexed Optical Encryption via Modular Diffractive Deep Neural Networks

Advanced Functional Materials, EarlyView.
Modular diffractive deep neural network metasurfaces encode and reconstruct holograms across layer combinations and wavelengths, enabling secure, multifunctional operation. Each layer acts independently yet composes jointly, yielding up to m(2N −1) channels for m wavelengths and N layers.
Cherry Park, Youngsun Jeon, Seokho Lee, Yeseul Kim, Junsuk Rho   +4 more
wiley   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source