Results 191 to 200 of about 1,791,426 (298)

Recent Advances in Programmable Metasurfaces and Meta‐Devices

Advanced Electronic Materials, EarlyView.
Programmable metasurfaces enable various novel functionalities by dynamically tuning electromagnetic wavefronts. This article provides a comprehensive review of recent advances in microwave and terahertz programmable metasurfaces, covering electrical, thermal, optical, and mechanical control mechanisms.
Linda Shao, Chong He, Nan Wang, Liming Si, Weiren Zhu   +4 more
wiley   +1 more source

Programmable photonic neural engine with all-optical nonlinear activation and 40,000 connections. [PDF]

Sci Adv
Sun H, Zhu X, Azaña J.
europepmc   +1 more source

Ultra‐Low Power Consumption and Highly Durability in Sm:HfO2 Thin Film Ferroelectric Memristor for Edge Detection

Advanced Electronic Materials, EarlyView.
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with
Tengyu Li, YongRui Wang, Weifeng Zhang, Jikang Xu, Xiaobing Yan   +4 more
wiley   +1 more source

CNN Sample-Size Effects Across Biomedical Datasets: A Reliability Pattern in Overfitting, Ranking, and Monotonicity. [PDF]

Bioengineering (Basel)
Sgarro GA, Mendikowski M, Santoro D, Grilli L.   +3 more
europepmc   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales. [PDF]

PLoS Comput Biol
Varetti S, Goldt S, Piasini E.
europepmc   +1 more source

Effects of Doping, Disorder, and Compensation on Electron Conduction in Si‐Doped k‐Ga2O3 Close to the Metal‐to‐Insulator Transition

Advanced Electronic Materials, EarlyView.
Self‐compensation effects attributed to doping‐dependent shift‐defects at APBs Validation of Hall data for VRH transport near the MIT Persistence of VRH transport for any SiH4 flow in Si‐doped κ‐Ga2O3 due to high compensation coupling transport and EPR data as strategy to study electronic properties and doping T‐dependence of transport data agrees with
Antonella Parisini, Hans Jürgen von Bardeleben, Ildicò Cora, Zsolt Fogarassy, Paola Alippi, Vincenzo Fiorentini, Ian Vickridge, Piero Mazzolini, Alessio Bosio, Roberto Fornari   +9 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

ZSCAN-DDIE: an interpretable zero-shot learning method for the prediction of drug-drug interaction events using biomedical text. [PDF]

Bioinform Adv
Gao S, Gan Z, Han F, Cai Y.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source