Results 71 to 80 of about 4,953 (252)

Convolutional-Neural-Network-Based Onboard Band Selection for Hyperspectral Data With Coarse Band-to-Band Alignment

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands.
David Llaveria Godoy, Nicolas Longepe, Gabriele Meoni, Roberto del Prete, Adriano Camps   +4 more
doaj   +1 more source

A Local Potential-Based Clustering Algorithm for Unsupervised Hyperspectral Band Selection

IEEE Access, 2019
Unsupervised band selection plays an increasingly important role in a hyperspectral image (HSI) classification because of inadequate labeling samples.
Zhaokui Li, Lin Huang, Jinrong He, Cuiwei Liu, Xiangbin Shi, Deyuan Zhang   +5 more
doaj   +1 more source

Tackling cancer stemness with nanotechnology in the era of precision medicine

BMEMat, EarlyView.
Precise customization of nanoparticles (NPs) enables active targeting of cancer stem cells (CSCs), thereby improving drug delivery and therapeutic efficacy. NP‐based probing enhances CSC detection through imaging and liquid biopsy, whereas diverse therapeutic payloads improve therapeutic outcomes.
Shaolei Guo, Shun Yao, Lihui Meng, Chunyuan Chen, Yuechu Lucinda Lu, Rong Lu, Kashif Rafiq Zahid, Umar Raza, Qingzhi Wu, Wenli Chen   +9 more
wiley   +1 more source

An Unsupervised Hyperspectral Band Selection Method Based on Regional Structure Preservation and Two-Stage Lightweight Greedy Search

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral band selection is a critical step for reducing data dimensionality, suppressing spectral redundancy, and improving the efficiency of subsequent analysis.
Lin Hu, Yu Lu, Jiankai Hu, Shu Gan, Xiping Yuan, Tao Guo, Guanglong Li   +6 more
doaj   +1 more source

Supervised Embedded Methods for Hyperspectral Band Selection

Hyperspectral Imaging (HSI) captures rich spectral information across contiguous wavelength bands, supporting applications in precision agriculture, environmental monitoring, and autonomous driving. However, its high dimensionality poses computational challenges, particularly in real-time or resource-constrained settings.
Yaniv Zimmer, Ofir Lindenbaum, Oren Glickman   +2 more
openaire   +3 more sources

Artificial Intelligence in Ophthalmology: From Methodological Advances to Clinical Translation and Future Directions

Eye &ENT Research, EarlyView.
ABSTRACT Artificial intelligence (AI) is reshaping ophthalmology from task‐specific image analysis toward multimodal, longitudinal, and clinically integrated decision support. This narrative review summarizes the methodological evolution of ophthalmic AI, including traditional machine learning, task‐specific deep learning, self‐supervised learning ...
Yuxin Liu, Hanruo Liu
wiley   +1 more source

Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

Remote Sensing
As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of ...
Yulei Wang, Haipeng Ma, Yuchao Yang, Enyu Zhao, Meiping Song, Chunyan Yu   +5 more
doaj   +1 more source

Flexible wearable electronics for cardiovascular monitoring from surface signals to deep physiological insights

FlexMat, EarlyView.
This review organizes flexible wearable electronics for cardiovascular monitoring into four interconnected information layers: surface electrophysiology, hemodynamic sensing, vascular imaging, and biofluid biomarker analysis. This framework clarifies how electrical rhythm, vascular loading, structural and flow‐related features, and biochemical states ...
Qiao Chen, Hongwen Chen, Yuanyuan Li, Pingan Yang, Rui Li, Zhihao Zhou   +5 more
wiley   +1 more source

Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network

Revue Française de Photogrammétrie et de Télédétection, 2018
Hyperspectral images provide rich  spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality.
Mateus Habermann, Vincent Frémont, Elcio Hideiti Shiguemori   +2 more
doaj   +1 more source

PbS colloidal quantum dots imagers: From single devices to arrays, flexible platforms, and CMOS‐integrated systems

FlexMat, EarlyView.
Abstract Imagers operating in the visible to near‐infrared (NIR) range play an important role in applications such as industrial sorting, security surveillance, and autonomous driving. However, conventional imaging technologies based on silicon and III–V semiconductors suffer from several limits, including restricted spectral range, high fabrication ...
Xin Hong, Songdi Zhang, Runtian Han, Peng Wang, Wenjie Deng   +4 more
wiley   +1 more source