Results 141 to 150 of about 14,545,214 (235)

Gradient‐Conductivity Architecture Enables Synchronous Enhancement of Sensitivity and Detection Range in Multifunctional All‐Paper Sensors

Advanced Functional Materials, EarlyView.
A gradient‐engineered all‐paper sensor is fabricated by integrating MXene and in situ grown AgNPs within hierarchical cellulose networks. The device breaks the sensitivity–detection range trade‐off through cascaded conductive pathways, enabling ultrahigh pressure sensitivity, humidity–pressure decoupled dual‐mode sensing, and outstanding EMI shielding,
Ao Li, Jun Xu, Dezhong Xu, Zhaohui Zhang, Jiyi Li, Daxian Cao, Kefu Chen, Mizi Fan   +7 more
wiley   +1 more source

Driver Primary School newsletter

, 2008
Made available via the Publications (Legal Deposit) Act 2004 (NT).This publication contains many links to external sites.

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Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Bio‐Inspired Artificial Ionic Mechanoreceptor

Advanced Functional Materials, EarlyView.
A skin‐inspired artificial mechanoreceptor based on ionic interactions is presented for biomimetic tactile sensing. Pressure‐driven ionic redistribution within microfluidic channels generates a self‐powered electrical signal without external bias. The generated waveform exhibits mechanoreceptor‐like temporal features, including overshoot and undershoot,
Mohammad Akbari, Jeppe Don, Lasse A. N. Pedersen, John Noee, Rassoul Tabassian   +4 more
wiley   +1 more source

Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle.

, 2006
This thesis is concerned with modeling of driver behavior based on data collected from real traffic using an advanced instrumented vehicle. In particular, the focus is on driver-following behavior (often called car-following in transport science) for microscopic simulation of road traffic systems.
openaire   +2 more sources

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

Multistage, Self‐Sensing Artificial Muscles With Coordination From Liquid Crystal Elastomer‐Graphene Composite Fibers

Advanced Functional Materials, EarlyView.
Inspired by skeletal muscles’ precision and endurance, CoilLCE integrates a self‐sensing liquid crystal elastomer/graphene artificial muscle with an embedded Joule‐heating copper coil, enabling closed‐loop multistage actuation with programmable intermediate states (strain accuracy of 2%), and antagonistic coordination (32% total strain). These features
Ziyun Zhang, Xuelin Wu, Yinding Chi, Qingya Huang, Yaoye Hong, Antonio Proctor Martinez, Jingxian Wu, Bingzhi He, Cynthia Sung, Shu Yang   +9 more
wiley   +1 more source

Driver Primary School newsletter

, 2011
Made available via the Publications (Legal Deposit) Act 2004 (NT).This publication contains many links to external sites.

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Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Driver Primary School newsletter

, 2009
Made available via the Publications (Legal Deposit) Act 2004 (NT).This publication contains many links to external sites.

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