Sparsity for Ultrafast Material Identification
Abstract: Mid‐infrared spectroscopy is often used to identify material. Thousands of spectral points are measured in a time‐consuming process using expensive table‐top instrument. However, material identification is a sparse problem, which in theory can be solved with just a few measurements. Here the sparsity of the problem is exploited to develop an ultra‐fast, portable, and inexpensive method to identify materials. In a single‐shot, a mid‐infrared camera can identify materials based on their spectroscopic signatures. This method does not require prior calibration, making it robust and versatile in handling a broad range of materials.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Sparsity for Ultrafast Material Identification ; day:24 ; month:01 ; year:2024 ; extent:7
Laser & photonics reviews ; (24.01.2024) (gesamt 7)
- Creator
-
Qu, Yurui
Zhou, Qingyi
Xiang, Jin
Yu, Zongfu
- DOI
-
10.1002/lpor.202300069
- URN
-
urn:nbn:de:101:1-2024012514240351149187
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 15.08.2025, 7:21 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Qu, Yurui
- Zhou, Qingyi
- Xiang, Jin
- Yu, Zongfu
Other Objects (12)
Reconstruction using local sparsity
Consistency, Robustness and Sparsity for Learning Algorithms
Sparsity-based reconstruction methods for optoacoustic tomography
Sparsity and stability for minimum-variance portfolios
A Unifying Framework for Sparsity-Constrained Optimization
The elastic net : stability for sparsity methods
Sparsity : graphs, structures, and algorithms
Phase Retrieval with Sparsity Constraints
Projections, Learning, and Sparsity for Efficient Data Processing
Sparsity constraints and regularization for nonlinear inverse problems
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions
Exploring feature sparsity for out-of-distribution detection
Reconstruction using local sparsity
Consistency, Robustness and Sparsity for Learning Algorithms
Sparsity-based reconstruction methods for optoacoustic tomography
Sparsity and stability for minimum-variance portfolios
A Unifying Framework for Sparsity-Constrained Optimization
The elastic net : stability for sparsity methods
Sparsity : graphs, structures, and algorithms
Phase Retrieval with Sparsity Constraints
Projections, Learning, and Sparsity for Efficient Data Processing
Sparsity constraints and regularization for nonlinear inverse problems
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions
Exploring feature sparsity for out-of-distribution detection
Reconstruction using local sparsity
Consistency, Robustness and Sparsity for Learning Algorithms
Sparsity-based reconstruction methods for optoacoustic tomography
Sparsity and stability for minimum-variance portfolios
A Unifying Framework for Sparsity-Constrained Optimization
The elastic net : stability for sparsity methods
Sparsity : graphs, structures, and algorithms
Phase Retrieval with Sparsity Constraints
Projections, Learning, and Sparsity for Efficient Data Processing
Sparsity constraints and regularization for nonlinear inverse problems
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions