Results 61 to 70 of about 5,679 (259)
Explainability Feature Bands Adaptive Selection for Hyperspectral Image Classification
Hyperspectral remote sensing images are widely used in resource exploration, urban planning, natural disaster assessment, and feature classification. Aiming at the problems of poor interpretability of feature classification algorithms for hyperspectral ...
Jirui Liu +5 more
doaj +1 more source
Micromachined Double‐Membrane Mechanically Tunable Metamaterial for Thermal Infrared Filtering
Herein, a mechanically tunable double‐layer plasmonic metamaterial leveraging the extraordinary optical transmission effect observed in subwavelength arrays of openings within thin metal layers is presented. The concept is experimentally validated by integrating the proposed metamaterial structure into an electrostatic parallel‐plate actuator to create
Oleg Bannik +7 more
wiley +1 more source
Cartilage injury promotes local fibrinogen deposition, which accelerates monosodium urate crystallization and activates integrin‐mediated matrix‐degradation. This self‐amplifying cycle drives gout‐related cartilage erosion. Disrupting fibrinogen deposition or restoring the cartilage barrier could interrupt this vicious cycle.
Hanlin Xu +7 more
wiley +1 more source
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification [PDF]
Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both.
Hua, Yuansheng +5 more
core +1 more source
This work visualizes local variations in luminescence and relates them to nanoscale chemical inhomogeneities in single InxGa1−xN quantum wells. We demonstrate that the elemental segregation is only inherent to quantum wells with high indium content. Density functional theory shows this to be the result of strain‐induced phase stabilization, explaining ...
Jing‐Yang Chung +12 more
wiley +1 more source
Semi-supervised hyperspectral band selection via spectral-spatial hypergraph model [PDF]
Band selection is an essential step toward effective and efficient hyperspectral image classification. Traditional supervised band selection methods are often hindered by the problem of lacking enough training samples. To address this problem, we propose
Zhou, Jun +4 more
core +1 more source
Deep Learning Enables Identification of Antimicrobial Peptides Through Mechanochromic Fingerprints
We demonstrate a new platform for antimicrobial peptide identification by combining polydiacetylene, hyperspectral imaging, and deep learning. The trained model classifies distinct spectral fingerprints into 14 peptide classes with 96.79% accuracy, revealing previously hidden molecular information beyond conventional colorimetric sensing.
Jiali Chen +4 more
wiley +2 more sources
A serum‐free, air–liquid interface organotypic slice culture model preserves human post‐mortem corpus callosum tissue, successfully recovering from slicing trauma to reflect the donor's underlying disease state. Pairing label‐free Coherent anti‐Stokes Raman scattering (CARS) microscopy with k‐means clustering enables objective, high‐resolution ...
Kasra Roya‐Kouchaki +5 more
wiley +1 more source
Incorporating band selection in the spatial selection of spectral endmembers
The impact of band selection on endmember selection is seldom explored in the analysis of hyperspectral imagery. This study incorporates the N-dimensional Spectral Solid Angle (NSSA) band selection tool into the Spectral-Spatial Endmember Extraction ...
Yaqian Long, Benoit Rivard, Derek Rogge
doaj +1 more source
Super‐multiplexed Label‐free Raman Imaging (SLRI) enables 2D/3D metabolic mapping of intact Drosophila testes. Moving beyond descriptive morphology, it establishes a multidimensional tool for tissue metabolic remodeling, and offers a generalizable platform for complex tissue analysis, with implications extending to development and disease. ABSTRACT The
Jiaxin Li +23 more
wiley +1 more source

