A Multistage Deep Learning Network for Trace Explosive Residues Detection in SERS Chips-Reference-Cited by-同舟云学术

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A Multistage Deep Learning Network for Trace Explosive Residues Detection in SERS Chips

Published:2023-12-15 Issue:24 Volume:23 Page:31493-31505
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhang Feng¹^ORCID,Yang Jianchun¹^ORCID,Zhang Xinyu¹^ORCID,Su Shuaiwu¹^ORCID,Luo Jiayang¹^ORCID,Li Jiahao¹^ORCID,Li Xueming²

Affiliation:

1. Key Laboratory for Optoelectronic Technology and Systems of Education Ministry of Chinam, College of Optoelectronic Engineering, Chongqing University, Chongqing, China

2. College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China

Funder

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10360293/10316254.pdf?arnumber=10316254

Reference44 articles.

1. The Tendency of Terrorist Organizations to Explosive Attacks: An Institutional Theory Perspective

2. Identification of inorganic oxidizing salts in homemade explosives using Fourier transform infrared spectroscopy

3. Simultaneous determination of inorganic anions and cations in explosive residues by ion chromatography

4. Identification of Inorganic Improvised Explosive Devices Using Sequential Injection Capillary Electrophoresis and Contactless Conductivity Detection

5. Remote Raman spectroscopy of explosive precursors

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