Results 141 to 150 of about 3,299,544 (255)
Full waveform inversion schemes for 3D density structures [PDF]
Blom, Nienke, Fichtner, Andreas
core +1 more source
Microstructural mapping of polycrystalline metallic alloys is essential for predicting their macroscopic mechanical performance. Among existing techniques, non-destructive subsurface imaging offers a promising but technically challenging pathway for ...
Yongwei Xie +4 more
doaj +1 more source
Synchronization of Analog Neuron Circuits With Digital Memristive Synapses: An Hybrid Approach
An hybrid circuit mimicking neural units coupled using memristive synapses is introduced. The analog neurons provide flexibility and robustness, and the digital memristive coupling guarantees the full reconfigurability of the interconnection. The onset of a synchronized spiking behavior in two circuits mimicking the Izhikevich neuron is discussed from ...
Lamberto Carnazza +3 more
wiley +1 more source
How Does Neural Network Reparametrization Improve Geophysical Inversion?
Full waveform inversion (FWI) is a high‐resolution seismic inversion technique and great efforts have been made to mitigate the multi‐solution problem, such as the traditional total variation (TV) regularization. Different from traditional regularization,
Yuping Wu, Jianwei Ma
doaj +1 more source
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang +3 more
wiley +1 more source
Ambient noise full waveform inversion for noise source distribution
We use full waveform inversion of ambient noise to estimate noise source distributions and model parameters. We derive the sensitivity kernel with respect to a noise source distribution from ambient noise data.
Zhang, Shuo
core
Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks
Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç +2 more
wiley +1 more source
Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh +3 more
wiley +1 more source
A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images
A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski +5 more
wiley +1 more source
Biomimetic 3D Tactile Sensor System With Neuromorphic Encoding for Fascicle‐Level Feedback
A 3D biomimetic tactile sensor system converts skin‐like mechanical interactions into neural stimulation‐ready spike patterns. Embedded slow‐ and fast‐adapting sensors distinguish sustained pressure from transient touch, while neuromorphic encoding preserves their temporal signatures.
Minseok Kim +4 more
wiley +1 more source

