Vital signs monitoring using pseudo‐random noise coded Doppler radar with Delta–Sigma modulation

Abouzaid, Salah; Ahmad, Wael A.; Eibert, Thomas F.; Ng, Herman Jalli

doi:10.1049/iet-rsn.2020.0170

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Frequency-modulated continuous wave (FMCW) radars have been utilized in a range of applications, including high accuracy distance measurements [1], [2], [3], vital signs monitoring [4], [5], [6], and hand gesture recognition [7], [8]. They have also been shown in the literature to be effective tools for material characterization, as they offer a low-cost, compact, and accurate solution for characterizing dielectric materials [9], [10], [11], [12].…”

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confidence: 99%

Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique

Abouzaid

Jaeschke²,

Kueppers³

et al. 2023

IEEE Trans. Microwave Theory Techn.

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This article proposes a low-cost and practical alternative to vector network analyzers (VNAs) for characterizing dielectric materials using a calibrated frequency-modulated continuous wave (FMCW) radar measurement setup and a machine learning (ML) model. The calibrated FMCW radar measurement setup has the ability to accurately measure the S-parameters of dielectric materials. In addition, an ML model is developed to extract material parameters such as thickness, dielectric constant, and loss tangent with high accuracy. K -means clustering was additionally applied to significantly reduce the complexity of the neural network (NN). Additionally, a state-of-the-art openset recognition (OSR) technique was adopted to simultaneously classify known classes and reject unknown classes. The developed model uses a modified version of the class anchor clustering (CAC) distance-based loss, which outperforms the conventional cross-entropy loss. The proposed model was evaluated on several dielectric materials and compared to reference measurements using a VNA and curve fitting. The results indicate that the proposed model is accurate and robust, and that the calibrated radar sensor provides a practical and cost-effective alternative to VNAs in characterizing dielectric materials, as long as the material parameters are within the defined limits.

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confidence: 99%

Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique

Abouzaid

Jaeschke²,

Kueppers³

et al. 2023

IEEE Trans. Microwave Theory Techn.

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Scalable and Fully-Integrated Multi-Band Radar Sensor Platform in SiGe BiCMOS Technology

2021

2021 International Conference on Microwave and Millimeter Wave Technology (ICMMT)

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How Will Radar Be Integrated Into Daily Life?: mm-Wave Radar Architectures for Modern Daily Life Applications

Ahmad¹,

2022

IEEE Microwave

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Vital signs monitoring using pseudo‐random noise coded Doppler radar with Delta–Sigma modulation

Cited by 6 publications

References 28 publications

Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique

Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique

Scalable and Fully-Integrated Multi-Band Radar Sensor Platform in SiGe BiCMOS Technology

How Will Radar Be Integrated Into Daily Life?: mm-Wave Radar Architectures for Modern Daily Life Applications

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