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.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Sparsity for Ultrafast Material Identification ; day:24 ; month:01 ; year:2024 ; extent:7
Laser & photonics reviews ; (24.01.2024) (gesamt 7)
- Urheber
-
Qu, Yurui
Zhou, Qingyi
Xiang, Jin
Yu, Zongfu
- DOI
-
10.1002/lpor.202300069
- URN
-
urn:nbn:de:101:1-2024012514240351149187
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:21 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Qu, Yurui
- Zhou, Qingyi
- Xiang, Jin
- Yu, Zongfu
Ähnliche Objekte (12)
Reconstruction using local sparsity
Consistency, Robustness and Sparsity for Learning Algorithms
A Unifying Framework for Sparsity-Constrained Optimization
Sparsity-based reconstruction methods for optoacoustic tomography
Sparsity and stability for minimum-variance portfolios
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
Sparsity Constraints and Regularization for Nonlinear Inverse Problems
Reconstruction using local sparsity
Consistency, Robustness and Sparsity for Learning Algorithms
A Unifying Framework for Sparsity-Constrained Optimization
Sparsity-based reconstruction methods for optoacoustic tomography
Sparsity and stability for minimum-variance portfolios
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
Sparsity Constraints and Regularization for Nonlinear Inverse Problems
Reconstruction using local sparsity
Consistency, Robustness and Sparsity for Learning Algorithms
A Unifying Framework for Sparsity-Constrained Optimization
Sparsity-based reconstruction methods for optoacoustic tomography
Sparsity and stability for minimum-variance portfolios
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