Towards Neural Network Classification of Terahertz Measurements for Determining the Number of Coating Layers

Busboom, Imke; Rohde, N.; Christmann, Simon; Feige, Volker K. S.; Haehnel, Hartmut; Tibken, Bernd

doi:10.1109/irmmw-thz46771.2020.9370440

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The estimation and extraction THz related parameters has also been explored [154] including the model parameters associated with materials from FMCW THz data was proposed that uses deep optimization based on a neural network [155]. In [156] estimation of the number of layers in THz TDS layer thickness measurements were determined using a feed forward neural network and the approximations for material parameter extraction were performed artificial neural networks [157].…”

Section: Parameter Estimation or Extractionmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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Section: Parameter Estimation or Extractionmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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“…For analyzing measurements that are convoluted by additional effects, such as shape irregularity, variations in thickness, attenuations, Fabry-Perot oscillations and the combinations thereof, experimental data-driven approaches using machine learning algorithms have found success in automatic signal recognition without manual and professional intervention. For example, convolutional neural networks (CNNs) [18]- [21] and various shallow classifiers, like support vector machines [22]- [27], k-nearest neighbors [23], [28], [29], random forest [30], and other single-layer neural networks [31]- [34] have been used for image/waveform classification. With the exception of CNN, these common classifiers are usually used in conjunction with feature extraction methods, e.g., principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Terahertz Waveform Selection of a Pharmaceutical Film Coating Process Using Recurrent Network

Williams

May

et al. 2022

IEEE Trans. THz Sci. Technol.

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In-line terahertz pulsed imaging has been utilized to measure the film coating thickness of individual tablets during the coating process in a production-scale pan coater. A criteria-based waveform selection algorithm (WSA) was developed to select terahertz signals reflected from the surface of coating tablets and determine the coating thickness. Since the WSA uses many criteria thresholds to select terahertz waveforms of sufficiently high quality, it could reject some potential candidate tablet waveforms that are close but do not reach the threshold boundary. On the premise of the availability of large datasets, we aim to improve the efficiency of WSA with machine learning. This article presents a recurrent neural network approach to optimize waveform selection. In comparison with the conventional method of WSA, our approach allows more than double the number of waveforms to be selected while maintaining excellent agreement with offline thickness measurements. Moreover, the processing time of waveform selection decreases so that it can be applied for real-time coating monitoring in the pharmaceutical industry, which leads to more advancements in quality control for pharmaceutical film coating.

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Towards Neural Network Classification of Terahertz Measurements for Determining the Number of Coating Layers

Cited by 2 publications

References 6 publications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Optimizing Terahertz Waveform Selection of a Pharmaceutical Film Coating Process Using Recurrent Network

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