The Implementation of Neural Networks for Phaseless Parametric Inversion

Edwards, Keeley; Krakalovich, Kennedy; Kruk, Ryan; Khoshdel, Vahab; LoVetri, Joe; Gilmore, Colin; Jeffrey, Ian

doi:10.23919/ursigass49373.2020.9232216

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…It is not practical to obtain a large number of labelled experimental measurements from this system, and synthetically generated data must be used. We have previously shown that bulk-parameter networks can be trained on synthetic data and and successfully applied to uncalibrated experimental data [34]. Synthetic data should be chosen to cover a range of expected fibroglandular geometries and permittivities within the fixed adipose region.…”

Section: Labelled Datamentioning

confidence: 99%

“…We have recently had success extracting bulk imaging parameters from phaseless electromagnetic field data in the application which are stored grain monitoring. In grain bin imaging, the parameters consisting of grain height, angle of repose and bulk complexvalued permittivity, are obtained using either an iterative optimization technique [33] or machine learning using either single-frequency data [34] or multi-frequency data [35]. The machine learning approach provides a cost-effective, near real-time, long-term monitoring solution where the cost of a computationally expensive optimization for every measurement is instead transferred to one-time network training.…”

Section: Introductionmentioning

confidence: 99%

“…No experimental data is required for training the machine learning model presented in this work. An additional contribution includes the generalization of the machine-learning work-flow originally developed for grain-bin imaging to microwave breast imaging [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

et al. 2021

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A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.

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Section: Labelled Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

et al. 2021

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“…3. Previously, we reported the results of several neural network architectures [27] using data generated by a finite element method (FEM) forward solver [10]; we found that multi-frequency networks improve performance over single-frequency networks. For each bin / transceiver configuration, measurements from a number of frequencies are used to perform the inversion.…”

Section: Bulk Parameter Estimation Using Neural Networkmentioning

confidence: 99%

“…Recently, we demonstrated a proof-of-concept that grain bin bulk parameter extraction can be successfully performed on uncalibrated, experimental data using a fully connected neural network trained only on synthetic data [27]. The key feature of the approach is the use of a loss function and normalization scheme that allows the model to be trained synthetically and applied experimentally without system calibration.…”

Section: Introductionmentioning

confidence: 99%

Stored Grain Inventory Management Using Neural-Network-Based Parametric Electromagnetic Inversion

et al. 2020

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We present a neural network architecture to determine the volume and complex permittivity of grain stored in metal bins. The neural networks output the grain height, cone angle and complex permittivity of the grain, using the input of experimental field data (S-parameters) from an electromagnetic imaging system consisting of 24 transceivers installed in the bin. Key for practical applications, the neural networks are trained on synthetic data sets but generate the parametric information using experimental data as input, without the use of calibration objects or open-short-load measurements. To accomplish this, we formulate a data normalization scheme that enables the use of a loss function that directly compares measured S-parameters and simulation model fields. The normalization strategy and the ability to train on synthetic data means we do not need to collect experimental training data. We demonstrate the applicability of this synthetically trained neural network to experimental data from two different bin geometries, and discuss the ability of these neural networks to successfully infer parameters that can be used for grain inventory management. Our neural-network-based approach enables rapid inference, providing a more cost-effective long-term solution than existing optimization-based parametric inversion methods.

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A multi-branch deep convolutional fusion architecture for 3D microwave inverse scattering: stored grain application

Khoshdel

Asefi

Ashraf

et al. 2021

Neural Comput & Applic

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The Implementation of Neural Networks for Phaseless Parametric Inversion

Cited by 5 publications

References 6 publications

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

Stored Grain Inventory Management Using Neural-Network-Based Parametric Electromagnetic Inversion

A multi-branch deep convolutional fusion architecture for 3D microwave inverse scattering: stored grain application

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