Comparative evaluation of zoomed and parallel diffusion-weighted imaging: A phantom study. [PDF]
Iwase A +4 more
europepmc +1 more source
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
Airborne Warning System Direction-Finding Based on Real-Time SNR Correction. [PDF]
Ding J +7 more
europepmc +1 more source
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley +1 more source
Rapid Quantitative Determination of Enantiomeric Excess by Vibrational Circular Dichroism Using Noise Based Spectral Selection. [PDF]
Jackson AM, Bows JR, Nash GR.
europepmc +1 more source
Designing Wire Mazes for Replicating Natural Echoes to Study Bat Biosonar Function
A validated framework combining efficient physical modeling (multiple scattering model) and deep learning is presented to guide wire‐maze design for bat biosonar studies. This approach rapidly generates large datasets to test acoustic distinguishability among wire arrangements.
Chunlin Jia +3 more
wiley +1 more source
A Hybrid Frequency Estimation Framework for Long-Range FMCW LiDAR Under Low-SNR Conditions. [PDF]
Fang Y +6 more
europepmc +1 more source
A Scalable and Resource‐Efficient Pipelined p‐Computer for Probabilistic Ising Machines
(a) Block diagram of the portfolio optimization problem: given M assets, the goal is to determine the optimal weights w that maximize the expected return (based on the mean historical assets return u), while minimizing the risk, quantified by the assets covariance matrix S.
Deborah Volpe +9 more
wiley +1 more source
DFRSeisNet: Fluctuation-Prior-Regularized Background Noise Attenuation for Seismic Signal Denoising. [PDF]
Deng F, Pang L, Wang S, Peng W.
europepmc +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
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 +5 more
wiley +1 more source

