Results 151 to 160 of about 104,118 (299)

Granular computing in mosaicing of images from capsule endoscopy. [PDF]

Nat Comput, 2015
Maciura L, Bazan JG.
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Granular computing with multiple granular layers for brain big data processing. [PDF]

Brain Inform, 2014
Wang G, Xu J.
europepmc   +1 more source

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source

Photonic Engineering Enables All‐Passive Upconversion Imaging with Low‐Intensity Near‐Infrared Light

Advanced Functional Materials, EarlyView.
A passive upconversion imaging system enables the observation of scenes illuminated by low‐intensity incoherent near‐infrared light from 750 to 930 nm, by converting it into the visible without the use of external power. The upconverter is enabled by triplet–triplet annihilation in a bulk heterojunction, with absorption enhanced by plasmonic resonators
Rabeeya Hamid, Demeng Feng, Pournima Narayanan, Justin S. Edwards, Manchen Hu, Emma Belliveau, Minjeong Kim, Shenwei Yin, Sanket Deshpande, Chenghao Wan, Linda Pucurimay, David A. Czaplewski, Daniel N. Congreve, Mikhail A. Kats   +13 more
wiley   +1 more source

Some Granular Computing Based Machine Learning Algorithms

Transactions on Fuzzy Sets and Systems
Granular computing has emerged as a new computational method that is beneficial when dealing with large amounts of data. In recent years, several machine learning models based on the granular framework have been developed, outperforming traditional ...
Vijay R. Tiwari
doaj  

Exploring multi-granularity balance strategy for class incremental learning via three-way granular computing. [PDF]

Brain Inform
Xian Y, Yu H, Wang Y, Wang G.
europepmc   +1 more source

Granular computing classification algorithms based on distance measures between granules from the view of set. [PDF]

Comput Intell Neurosci, 2014
Liu H, Liu C, Wu CA.
europepmc   +1 more source

Magnetic and Structural Response Tuned by Coexisting Mn Concentration‐Dependent Phases in MnBi2Te4 Thin Film Grown on GaAs(001) by Molecular Beam Epitaxy

Advanced Functional Materials, EarlyView.
The study explores structural and magnetic properties of one of the most recent topological quantum materials (MnBi2Te4). The Mn‐poor structure leads to stacking faults (quintuple layer ‐ QL of Bi2Te3 formation instead of a septuple layer ‐ SL of MnBi2Te4), resulting in a coexistence between weak antiferromagnetism and ferromagnetism.
Wesley F. Inoch, Sergio L. A. Mello, Gilberto Rodrigues‐Junior, Sukarno O. Ferreira, Marcelo M. Sant'Anna, Benjamin R. Salles, Mateus T. Souza, Sérgio L. Morelhão, Maybi F. Sampaio, Bráulio S. Archanjo, Leonarde N. Rodrigues   +10 more
wiley   +1 more source

In Situ Study of Resistive Switching in a Nitride‐Based Memristive Device

Advanced Functional Materials, EarlyView.
In situ TEM biasing experiment demonstrates the volatile I‐V characteristic of MIM lamella device. In situ STEM‐EELS Ti L2/L3 ratio maps provide direct evidence of the oxygen vacancies migrations under positive/negative electrical bias, which is critical for revealing the RS mechanism for the MIM lamella device.
Di Zhang, Rohan Dhall, Matthew M. Schneider, Cun Li, Chengyu Song, Sundar Kunwar, Hongyi Dou, Natanii R. Yazzie, Henry Tran, Daniel Appuing, Jim Ciston, Nicholas G. Cucciniello, Pinku Roy, Michael T. Pettes, John Watt, Winson Kuo, Haiyan Wang, Ye Cao, Rodney J. McCabe, Aiping Chen   +19 more
wiley   +1 more source