UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields
Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications.
Perez, Fabian +4 more
openaire +2 more sources
Multiple Longitudinal Tracts in the Cephalopod Arm Sensorimotor System
Octopuses have a rich behavioral repertoire, coordinating complex movements along the length of an arm. The neural circuits controlling these behaviors are poorly understood. We employ tract‐tracing to investigate longitudinal tracts in the octopus arm.
Cassady S. Olson, Clifton W. Ragsdale
wiley +1 more source
Neural Network‐Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy
We construct a neural network for the classification of bromazolam and fluorofentanyl in illicit opioid samples. The model outperforms a random forest classifier and shows elevated performance for low concentration samples. ABSTRACT Community‐based drug checking services are challenged in their ability to reliably detect low concentration adulterants ...
Joshua Jai +4 more
wiley +1 more source
Deep Learning Integration in Optical Microscopy: Advancements and Applications
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari +5 more
wiley +1 more source
Cryptic Paleomagnetic Complexity in the Ediacaran Egersund Dikes
Abstract The Ediacaran Period (∼635–539 Ma) represents a critical interval in Earth's evolution, yet its paleomagnetic record remains complex and contentious. One of the few Ediacaran paleomagnetic results from Baltica considered robust is a pole from the ca.
Yi Xue +5 more
wiley +1 more source
SSF-Net: A Spatial–Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing
In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities.
Bin Wang +4 more
doaj +1 more source
Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning
Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions.
Zeng Li, Jie Chen, Susanto Rahardja
doaj +1 more source
Noise Effect on Linear Spectral Unmixing
Abstract Using hyperspectral reflectance data collected from six types of surface covers, we synthesized linear mixtures and used them to test the sensitivity of two linear unmixing algorithms to simulated additive noise. We found both algorithms were highly sensitive to noise. This may considerably limit their use in remote sensing.
P. Gong, A. Zhang
openaire +1 more source
Ediacara Obscura: Unveiling Hidden Magnetisations in the Fen Complex, Southern Norway
Abstract Paleomagnetic directions found in Ediacaran (635–539 Ma) rocks are widely dispersed, which has led to conflicting hypotheses about tectonic regimes and geomagnetic field behavior during this period, and raised doubts about the fidelity of the paleomagnetic record.
Justin A. D. Tonti‐Filippini +8 more
wiley +1 more source
DMAE-HU: A novel deep multitasking autoencoder for hybrid hyperspectral unmixing in remote sensing
Hyperspectral unmixing (HU) is crucial for extracting material information from hyperspectral images (HSI) obtained through remote sensing. Although linear unmixing methods are widely used due to their simplicity, they only address linear mixing effects.
Suresh Aala +8 more
doaj +1 more source

