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

Abstract

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.

Terahertz Sensing using Deep Neural Network for Material Identification

“…Consider a THz sensing system to identify a set N of N materials with a transmission spectroscopy technique to measure the absorption of each material. Each material sample is powdered from the source components, then pressed into solid pieces 1 , and tested in THz-TDS experimental setup as shown in [7]. As mentioned earlier, each measurement is compared with and without the sample at the test probe, namely the transmitted and incident pulse, respectively.…”

“…The ratio between the transmitted and received signals illustrates the material properties. Specifically, two varieties of THz-TDS techniques are available to measure the aforementioned properties: transmission and reflection spectroscopy [4], [7]. Here, transmission spectroscopy refers to measuring the amount of light absorption, whereas reflection spectroscopy measures light reflection or scattering.…”

“…Several studies explore material identification using conventional feature extraction techniques and deep learning [7], [13]. A material identification using approximate entropy and deep neural network (DNN) is presented in [13], where 14 different materials are investigated for identification.…”

“…Also, the proposed approach shows 80.4% accuracy in detecting the materials without noise. A recent study on THz sensing based on signal processing and ML is presented in [7], where pre-processing, feature extraction, and performance analysis of THz spectroscopy are discussed. Correspondingly, authors in [7] compared different pre-processing techniques and existing ML algorithms for 5 materials.…”

“…A recent study on THz sensing based on signal processing and ML is presented in [7], where pre-processing, feature extraction, and performance analysis of THz spectroscopy are discussed. Correspondingly, authors in [7] compared different pre-processing techniques and existing ML algorithms for 5 materials. How these show that a low-complex classification algorithm without additional preprocessing for numerous materials is a promising opening for THz sensing.…”