Results 71 to 80 of about 5,679 (259)

Deep‐Learning‐Driven High‐Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon‐Limited Conditions

Advanced Science, EarlyView.
Highly overlapping fluorescent signals become distinguishable in photon‐limited living organisms via advanced imaging with intelligent reconstruction. The resulting in vivo hyperspectral imaging capability reveals nanoplastic uptake and circulation in live zebrafish, providing a new approach for studying complex biological and environmental processes ...
Renjian Li, Shutao Wu, Kaixiang Li, Zhenyu An, Yuwen Ben, Guiye LI, Sunil Kumar, James Mcginty, Tawfique Hasan, Songnian Fu, Meng Zhang, LingLing Chen   +11 more
wiley   +1 more source

Segmented Autoencoders for Unsupervised Embedded Hyperspectral Band Selection [PDF]

2018 7th European Workshop on Visual Information Processing (EUVIP), 2018
One of the major challenges in hyperspectral imaging (HSI) is the selection of the most informative wavelengths within the vast amount of data in a hypercube. Band selection can reduce the amount of data and computational cost as well as counteracting the negative effects of redundant and erroneous information. In this paper, we propose an unsupervised,
Tschannerl, Julius, Ren, Jinchang, Zabalza, Jaime, Marshall, Stephen   +3 more
openaire   +2 more sources

Advancing Fruit Bioimpedance Monitoring With Sustainable, Soft, And Bio‐Based Electrodes Beyond ECG

Advanced Electronic Materials, EarlyView.
Electrical impedance spectroscopy enables non‐destructive fruit quality monitoring, but conventional ECG and needle electrodes compromise signal stability, fruit physiology, and sustainability. This perspective highlights the transition toward soft, biocompatible, and biodegradable electrode interfaces based on natural substrates, bio‐derived ...
Sundus Riaz, Ahmed Rasheed, Angelo Zanella, Almudena Rivadeneyra, Paolo Lugli, Luisa Petti, Pietro Ibba   +6 more
wiley   +1 more source

UBSTrack: Unified Band Selection and Multi-Model Ensemble for Hyperspectral Object Tracking [PDF]

Hyperspectral object tracking is notably challenging due to the high-dimensional nature of the data and the necessity of seamlessly integrating spectral, spatial and temporal information.
Islam, Mohammad Aminul, Zhou, Jun, Xing, Wangzhi, Paliwal, Kuldip K, Gao, Yongsheng   +4 more
core   +2 more sources

Hyperspectral band selection based on deep learning: a review

Journal of King Saud University: Computer and Information Sciences
Hyperspectral images, characterized by rich spatial and spectral information, have been increasingly applied across numerous fields. However, the high dimensionality inherent in hundreds of spectral bands results in the “curse of dimensionality ...
Deqiong Ding, Dan Zhuang, Fei Xia, Xiaogao Yang   +3 more
doaj   +1 more source

Hyperspectral Image Visualization Using Band Selection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
This paper investigates hyperspectral image display based on selection of three spectral channels to build a red-green-blue (RGB) composite. A series of band selection algorithms are implemented and compared for this purpose. In particular, three color composition schemes based on visualization-oriented spectral segmentations are proposed.
Hongjun Su, Qian Du 0001, Peijun Du
openaire   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Fast Hyperspectral Band Selection Based on Spatial Feature Extraction [PDF]

, 2018
Hyperspectral images usually consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, the high dimensionality also has some disadvantages, such as the Hughes effect and a high storage demand.
Xianghai Cao, Ji, Yamei, Yamei Ji, Han, Jungong, Ji, Beibei, Jiao, Licheng, Cao, Xianghai, Jungong Han, Wang, Lin, Lin Wang, Licheng Jiao, Beibei Ji   +11 more
core   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source