Results 91 to 100 of about 35,627 (298)

Inland excess water mapping using hyperspectral imagery [PDF]

Geographica Pannonica, 2016
Geographica Pannonica; Vol 20, No 4, 2016.
Csendes Bálint, Mucsi László
openaire   +3 more sources

Terahertz Chiral Recognition Based on Metasurfaces

Advanced Photonics Research, EarlyView.
A novel terahertz chiral sensing method based on a multilayer metallic metasurface is proposed. The left‐handed circularly polarized and right‐handed circularly polarized components in the linearly polarized illuminating light into two focal points and convert them back into linear polarization for convenient measurement.
Xuyang Ji, Yufei Liu, Shuang Sun, Zehao He, Yuyao Yang, Yan Zhang   +5 more
wiley   +1 more source

Real‐Time In Vivo Monitoring of Anastomotic Intestinal Ischemia Using Implantable Resorbable Organic Sensors

Advanced Science, EarlyView.
Resorbable impedance sensors are successfully implanted into porcine small intestinal anastomoses. Impedance was recorded for 2 hours prior, and 2 hours following ischemia induction, and a significant drop in tissue impedance was observed. Abstract Anastomotic failure remains one of the most severe complications in gastrointestinal surgery.
Dennis Wahl, Finn Jaekel, Julia Henne, Richard Kantelberg, Daniel C. Freund, Eberhard Grambow, Brigitte Vollmar, Amelie R. Zitzmann, Hans Kleemann, Jochen Hampe, Sebastian Hinz, Clemens Schafmayer, Karl Leo   +12 more
wiley   +1 more source

Cohort‐Scale Spatial Autocorrelation for Tumor Prediction in Mid‐Infrared Pathology and Spatial Biomarker Discovery Using MALDI Imaging Lipidomics

Advanced Science, EarlyView.
Computational annotation of various tissue types in heterogeneous samples such as colorectal cancer liver metastasis (CRLM) using spatial autocorrelation analysis on non‐destructive mid‐infrared (MIR) imaging data enabled correlative multimodal mass spectrometry imaging (MSI) for spatial investigation of lipid tumor marker candidates. The method can be
Miriam F. Rittel, Nikolas Ebert, Denis Abu Sammour, Sebastian Graf, Björn C. Fröhlich, Emrullah Birgin, Shad A. Mohammed, Nuh N. Rahbari, Axel Wellmann, Oliver Wasenmüller, Cleo‐Aron Weis, Stefan Schmidt, Carsten Hopf   +12 more
wiley   +1 more source

Real‐Time High‐Definition Hyperspectral Endoscopy via Spatial‐Temporal Low‐Frequency‐Stochastic Spectral Encoding

Advanced Science, EarlyView.
A real‐time, high‐definition hyperspectral endoscopy is enabled by developing a spatial‐temporal spectral encoding approach based on low‐frequency stochastic filters combined with an encoding‐guided attention network. It provides hyperspectral image of in vivo tissue with fine superficial features, enables visualization of rapid and subtle ...
Xiaowei Liu, Jiakang Shao, Julin Xiao, Chenying Yang, Jiahe Zhang, Xiaoyu Yang, Xiang Hao, Ying Gu, Xu Liu, Yizhou Tan, Ji Qi, Qing Yang   +11 more
wiley   +1 more source

Generative Artificial Intelligence Shaping the Future of Agri‐Food Innovation

AgriFood: Journal of Agricultural Products for Food, EarlyView.
Emerging use cases of generative artificial intelligence in agri‐food innovation. ABSTRACT The recent surge in generative artificial intelligence (AI), typified by models such as GPT, diffusion models, and large vision‐language architectures, has begun to influence the agri‐food sector.
Jun‐Li Xu, Dimitrios Argyropoulos, Tamíris da Costa   +2 more
wiley   +1 more source

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

Remote Sensing, 2018
When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery ...
Cong Wang, Lei Zhang, Wei Wei, Yanning Zhang   +3 more
doaj   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Drones: Innovative Technology for Use in Precision Pest Management. [PDF]

, 2020
Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well ...
de Lange, Elvira S, Heldens, Wieke B, Iost Filho, Fernando H, Kong, Zhaodan   +3 more
core   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source