Terahertz optical machine learning for object recognition

Limbacher, Benedikt; Schoenhuber, S.; Wenclawiak, Moritz; Kainz, Martin A.; Andrews, A. M.; Strasser, G.; Darmo, J.; Unterrainer, K.

doi:10.1063/5.0029310

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Additional factors can cause low transparency of many materials in the THz range, in particular, in medicine and security applications, making objects discrimination rather complicated. In these cases, DL can be applied not only to resolve objects but also to enable low-noise measurements and permit reconstruction from a single measurement [433]. Other AI tools, such as adjustable convolutional neural networks, can help alleviate the problem of a low spatial resolution in THz imaging [434].…”

Section: Artificial Intelligence In Thz Imagingmentioning

confidence: 99%

Roadmap of Terahertz Imaging 2021

Valušis

Lisauskas

Yuan

et al. 2021

Sensors

191

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In this roadmap article, we have focused on the most recent advances in terahertz (THz) imaging with particular attention paid to the optimization and miniaturization of the THz imaging systems. Such systems entail enhanced functionality, reduced power consumption, and increased convenience, thus being geared toward the implementation of THz imaging systems in real operational conditions. The article will touch upon the advanced solid-state-based THz imaging systems, including room temperature THz sensors and arrays, as well as their on-chip integration with diffractive THz optical components. We will cover the current-state of compact room temperature THz emission sources, both optolectronic and electrically driven; particular emphasis is attributed to the beam-forming role in THz imaging, THz holography and spatial filtering, THz nano-imaging, and computational imaging. A number of advanced THz techniques, such as light-field THz imaging, homodyne spectroscopy, and phase sensitive spectrometry, THz modulated continuous wave imaging, room temperature THz frequency combs, and passive THz imaging, as well as the use of artificial intelligence in THz data processing and optics development, will be reviewed. This roadmap presents a structured snapshot of current advances in THz imaging as of 2021 and provides an opinion on contemporary scientific and technological challenges in this field, as well as extrapolations of possible further evolution in THz imaging.

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Section: Artificial Intelligence In Thz Imagingmentioning

confidence: 99%

Roadmap of Terahertz Imaging 2021

Valušis

Lisauskas

Yuan

et al. 2021

Sensors

191

View full text Add to dashboard Cite

show abstract

“…Deep learning is able to learn high-level abstract representations of data through multi-level nonlinear transformations. ML and DL share many common applications, such as image recognition, [1][2][3][4] object detection, [5][6][7] face recognition [8][9][10][11][12] and other tasks. However, they differ in some ways.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Zhou,

Song,

et al. 2024

Zeitschrift anorg allge chemie

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Hydrogel flexible sensors are widely used in wearable devices, health care, intelligent robots and other fields due to their excellent flexibility, biocompatibility and high sensitivity. With the development of single sensor to multi‐channel and multi‐mode sensor network, the sensor data also presents the characteristics of multi‐dimension, complex and massive. Traditional data analysis methods can no longer meet the data analysis requirements of hydrogel flexible sensor networks. The introduction of machine learning (ML) technology optimizes the process of data analysis. With the continuous development of multi‐layer neural network technology and the improvement of computer performance, deep learning (DL) algorithm is increasingly used to achieve higher efficiency and accuracy, provides a powerful tool for data analysis of hydrogel flexible sensor, and accelerates the intelligent process of hydrogel flexible sensor equipment. This paper introduces the classification of hydrogel flexible sensors and the working mechanism and common algorithms of ML, and summarizes the application of ML technology to assist hydrogel flexible sensors in data analysis in the fields of health care and information recognition. This review will provide inspiration and reference for integrating ML technology into the field of hydrogel flexible sensors.

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“…In fast THz imaging, deep learning can significantly increase the signalto-noise ratio. An optical ML algorithm based on the spatial transmission modulation of a THz beam was applied to improve the recognition speed in the recognition of objects with only a single measurement [213]. As well as ML, THz tomographic imaging is also an emerging technology in the non-destructive testing of materials, including polymers.…”

mentioning

confidence: 99%

Review of Bioplastics Characterisation by Terahertz Techniques in the View of Ensuring a Circular Economy

Abina,

Korošec,

Puc

et al. 2023

Photonics

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The increasing scarcity of natural resources, worsening global climate change, environmental degradation, and rising demand for food are forcing the biotechnology and plastics industries to seek and apply circular economy models that would lead to a sustainable transition in the production and use of bioplastics. Circular economy models can improve the economic productivity of bio-based plastics and have a positive impact on the environment by reducing conventional plastic waste and the consumption of petrochemical feedstocks for plastic production. In addition, some agricultural wastes that have the potential to be used as bioplastics can be reused. Terahertz (THz) systems are already used in the plastics and rubber industries for non-destructive testing, detection, imaging, and quality control. Several reports have highlighted the potential applications of THz spectroscopy and imaging in polymer analysis and plastics characterisation. This potential is even greater with chemometric methods and artificial intelligence algorithms. In this review, we focus on applications that support the transformation of the biotechnology sector to the circular economy, particularly via the transition from conventional plastics to bioplastics. In this review, we discuss the potential of THz systems for the characterisation and analysis of bioplastics and biopolymers. The results of previous studies on biopolymers in the THz frequency range are summarised. Furthermore, the potential of using artificial intelligence approaches such as machine learning as advanced analytical methods in THz spectroscopy and imaging, in addition to the conventionally used chemometric methods, is discussed. The results of this review highlight that THz technology can contribute to closed technological circles in important areas of biotechnology and the related plastics and rubber industries.

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Terahertz optical machine learning for object recognition

Cited by 10 publications

References 18 publications

Roadmap of Terahertz Imaging 2021

Roadmap of Terahertz Imaging 2021

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Review of Bioplastics Characterisation by Terahertz Techniques in the View of Ensuring a Circular Economy

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