T. Sivalingam, S. Ali, N. H. Mahmood, N. Rajatheva and M. Latva-Aho, "Terahertz Sensing using Deep Neural Network for Material Identification," 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Gandhinagar, Gujarat, India, 2022, pp. 1-5, doi: 10.1109/ANTS56424.2022.10227731
Terahertz sensing using deep neural network for material identification
|Author:||Sivalingam, Thushan1; Ali, Samad1; Mahmood, Nurul Huda1;|
1Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230906120334
Institute of Electrical and Electronic Engineers,
|Publish Date:|| 2023-09-06
Terahertz (THz) spectrum is identified as a potential enabler for advanced sensing and positioning, where THz-Time domain spectroscopy (THz-TDS) is specified for investigating the unique material properties. The transmission THz-TDS measures the light absorption of materials. This paper proposes a novel low-complex deep neural network (DNN)-based multi-class classification architecture to sense a wide variety of materials from the transmission spectroscopy. Based on the spectroscopic measurements made across a chosen THz region of interest, DNN extracts and learns the distinctive crystal structure of materials as features. With sufficient quantities of noisy spectroscopic data and labels, we train and validate the model. In low SNR regions, the proposed DNN classification architecture achieves about 92% success rate, which is greater than those of the state-of-the-art methods.
International Conference on Advanced Networks and Telecommunication Systems
|Pages:||1 - 5|
2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
IEEE International Conference on Advanced Networks and Telecommunications Systems
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 346208) and ALMLBEAM project (grant no. 24303959).
|Academy of Finland Grant Number:||
346208 (Academy of Finland Funding decision)
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.