Results 221 to 230 of about 18,244,520 (347)

Energy-efficient integer-only vs. floating-point FLBMF filters for low-power embedded image denoising. [PDF]

Sci Rep
Kiage BN, Kimwele MW, Cheruiyot WK, Wekesa JS.   +3 more
europepmc   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Infrared Image Denoising Algorithm Based on Wavelet Transform and Self-Attention Mechanism. [PDF]

Sensors (Basel)
Li H, Zhang Y, Yang L, Zhang H.
europepmc   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening. [PDF]

J Appl Clin Med Phys
Chen SS, Liu HH, Yang CC.
europepmc   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising. [PDF]

IEEE Trans Med Imaging
Xia M, Xie H, Liu Q, Zhou B, Wang H, Li B, Rominger A, Li Q, Badawi RD, Shi K, El Fakhri G, Liu C.   +11 more
europepmc   +1 more source

Neurotransmitter‐Defined Degeneration Patterns in Sporadic and C9orf72‐Associated Amyotrophic Lateral Sclerosis: Predilection to GABAergic, Serotonergic, Opioid, Glutamatergic, Endocannabinoid, and Microglial Systems—Implications for Therapy Development

Annals of Neurology, EarlyView.
Objective Amyotrophic lateral sclerosis (ALS) has a markedly distinctive clinical and neuroradiological signature, with the preferential involvement of specific brain networks and the apparent sparing of others. The molecular underpinnings of the strikingly selective anatomical vulnerability have not been fully elucidated to date despite the potential ...
Marlene Tahedl, We Fong Siah, Jasmin Lope, Jennifer C. Hengeveld, Mark A. Doherty, Russell L. McLaughlin, Orla Hardiman, Ee Ling Tan, Alison Toomey, Jana Kleinerova, Peter Bede   +10 more
wiley   +1 more source

Noise-augmented deep denoising: A method to boost CT image denoising networks. [PDF]

Med Phys
Kristof G, Eulig E, Kachelrieß M.
europepmc   +1 more source

Multimodal Characterization of Glymphatic‐Related Magnetic Resonance Imaging Markers in Huntington's Disease: A Multi‐Cohort Retrospective Study

Annals of Neurology, EarlyView.
Objective To characterize magnetic resonance imaging (MRI)‐based glymphatic surrogates in Huntington's disease (HD) using MRI measures of perivascular diffusivity and structural perivascular alterations across multiple large cohorts. Methods We analyzed 2,731 MRI sessions from 880 participants across 3 large retrospective HD cohorts.
Alexia Solomon, Zexi Wang, Francesco Sanvito, Zheng Zhou, Adys Mendizabal, Jingwen Yao   +5 more
wiley   +1 more source