Multipoles THz Metamaterial Biosensor for Low-Density Biomarker Detection

Entezami, Milad; Farahabadi, Seyed Ali Hosseini; Amarloo, Hadi; Naeini, Safieddin Safavi

doi:10.1109/aps/ursi47566.2021.9703947

Cited by 2 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In PCA, the principle of operation is such that a beam of pulsed laser illuminates a PCA gap composed of thin semiconductor film of high resistance with two contact pads of electrical property. When the bias voltage and laser beam are applied, there is in turn generation of a photocurrent and free carriers are accelerated by the static bias field thereby producing broadband THz frequency to the free space [79], [93]. In OR, NLO centrosymmetric crystals are used to generate THz broadband from 0.1THz to more than 40THz.…”

Section: Overview Of Thz Technologymentioning

confidence: 99%

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

Gezimati,

Singh

2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Overview Of Thz Technologymentioning

confidence: 99%

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

Gezimati,

Singh

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Metamaterial-based sensors offer significantly higher sensitivity compared to traditional sensors [12][13][14][15][16][17]. Similarly, the properties of metamaterial and fractal forms can be combined to obtain multi-service applications [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the characteristics of this biosensor are compared with other resonators existing in the literature [24][25][26][27][28]. Most of this research only covers the simulation aspect without considering the verification of application performance [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], and this is what we present in this study, in a manner that applies across multiple samples.…”

mentioning

confidence: 99%

Design and Fabrication of a Novel Corona-Shaped Metamaterial Biosensor for Cancer Cell Detection

Dadouche,

Mezache,

Tao

et al. 2023

Micromachines

View full text Add to dashboard Cite

The early detection and diagnosis of cancer presents significant challenges in today’s healthcare. So, this research, suggests an original experimental biosensor for cell cancer detection using a corona-shaped metamaterial resonator. This resonator is designed to detect cancer markers with high sensitivity, selectivity, and linearity properties. By exploiting the unique properties of the corona metamaterial structure in the GHz regime, the resonator provides enhanced interaction of electromagnetic waves and improved detection skills. Through careful experimental, simulation, and optimization studies, we accurately demonstrate the resonator’s ability to detect cancer. The proposed detection system is capable of real-time non-invasive cancer detection, allowing for rapid intervention and better patient outcomes. The sensitivity value was confirmed through simulation, estimated at 0.1825 GHz/RIU. The results of two different simulation methods are used: the simulation software CST Studio Suite (version 2017) based on the finite element method (FEM), and the simulation software ADS (version 2019) based on the equivalent circuit method, thereby increasing confidence in the convergence of simulation and measurement results. This work opens new avenues for developing advanced detection technologies in the field of oncology, and paves the way for more effective cancer diagnosis. The experimental study verified that this realized sensor has very small frequency shifts, significantly small electrical dimension and miniaturization, high sensitivity, and good linearity. The suggested configurations showed a capacity for sensing cancer cells in the GHz regime.

show abstract

Multipoles THz Metamaterial Biosensor for Low-Density Biomarker Detection

Cited by 2 publications

References 5 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

Design and Fabrication of a Novel Corona-Shaped Metamaterial Biosensor for Cancer Cell Detection

Contact Info

Product

Resources

About