Terahertz Frequency Domain Spectroscopy Identification System Based on Decision Trees

Ryniec, Radoslaw; Zagrajek, Przemysław; Pałka, Norbert

doi:10.12693/aphyspola.122.891

Cited by 8 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the problems under study can easily get prohibitively complex for simple signal processing techniques, hence the motivation for machine learning. Instead of comparing the exact values of channel measurements, we can set thresholds to check the presence or absence of specific spikes and build decision trees for classification [44]. Furthermore, when a small number of gases/materials is being tested, the corresponding sparsity can be leveraged in compressive sensing techniques.…”

Section: F the Role Of Thz Frequency-domain Spectroscopymentioning

confidence: 99%

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Helal

Sarieddeen

Dahrouj

et al. 2022

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are being proposed for future wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing and localization capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing (JCAS). We thereby motivate the corresponding use-cases, and present a handful of contextual future research directions.

show abstract

Section: F the Role Of Thz Frequency-domain Spectroscopymentioning

confidence: 99%

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Helal

Sarieddeen

Dahrouj

et al. 2022

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

show abstract

“…Instead of comparing the exact values of channel measurements, we can set thresholds to check the presence or absence of specific spikes and build decision trees for classification [44]. Furthermore, when a small number of gases/materials is being tested, the corresponding sparsity can be leveraged in compressive sensing techniques.…”

Section: B Thz Frequency-domain Spectroscopymentioning

confidence: 99%

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Helal¹,

Sarieddeen²,

Dahrouj³

et al. 2021

Preprint

View full text Add to dashboard Cite

Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are shaping the future of wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing; we motivate the corresponding usecases, and we present few future research directions in the field.

show abstract

“…Several methods can be used to solve for the absorption coefficients inH, including optimal maximum likelihood detectors, variations of compressed sensing techniques, as well as machine learning algorithms. For instance, instead of comparing the exact values of channel measurements, we can set thresholds to check the presence or absence of specific spikes and build decision trees for classification [324]. Note that for sensing purposes, x can be assumed to be a random vector.…”

Section: A Thz Sensing Imaging and Localizationmentioning

confidence: 99%

An Overview of Signal Processing Techniques for Terahertz Communications

Sarieddeen¹,

Alouini²,

Al-Naffouri³

2020

Preprint

View full text Add to dashboard Cite

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

show abstract

Terahertz Frequency Domain Spectroscopy Identification System Based on Decision Trees

Cited by 8 publications

References 5 publications

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

An Overview of Signal Processing Techniques for Terahertz Communications

Contact Info

Product

Resources

About