Results 121 to 130 of about 1,553,514 (242)
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
Advanced Electronic Materials, EarlyView.This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source
IEEE AccessNowadays, video streaming has become a popular form of multimedia for communication and entertainment. The rapid traffic explosion spurred by the development of emerging video-centric services such as e-science, virtual reality, and video conference has ...
Tien Vu Huu +3 more
doaj +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 +4 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj +4 more
wiley +1 more source
Advanced Energy Materials, EarlyView.A coaxial, membrane‐integrated hollow fiber electrode assembly (HFEA) is developed for continuous marine carbon mineralization. By utilizing a sub‐millimeter inter‐electrode gap, the HFEA minimizes Ohmic losses, achieving 50% energy reduction and over 85% DIC removal.
Inhwan Park +5 more
wiley +1 more source
A stress‐balance‐based critical Weber number for droplets and bubbles
AIChE Journal, EarlyView.Abstract Predicting droplet and bubble breakup in turbulent flows is critical for optimizing multiphase flow processes such as emulsification and atomization. Traditional models based on a fixed critical Weber number (Wec$$ {We}_c $$) may not capture interfacial‐stress dominance in low‐viscosity fluids or viscous resistance in high‐viscosity droplets ...
Supratim S. Dipta +4 more
wiley +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance
Advanced Intelligent Discovery, EarlyView.Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source