Combined feed-forward neural network and iterative linear back projection for Electrical Capacitance Volume Tomography

Saputra, Almushfi; Taruno, Warsito P.; Baidillah, Marlin Ramadhan; Handoko, Dwi

doi:10.1109/apcase.2014.6924480

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The soft-field properties of electrical based tomography and the ill-conditioned measurement matrix are crucial factors that make the inverse problem (ECVT imaging) difficult to solve. The reconstruction algorithms that so far have been implemented in ECVT are ILBP [2], NN-MOIRT [2] and Combined Feed Forward NN and ILBP [3]. ILBP, or the Landweber algorithm, is one of the oldest methods for electrical based tomography, including ECT.…”

Section: B Introductionmentioning

confidence: 99%

Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework

2016

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Electrical capacitance volume tomography is a volumetric tomography technique that utilizes capacitance and fringing to capture behavior or perturbation in the sensing domain. One of the crucial issues in developing ECVT technology is the reconstruction algorithm. In practice, ILBP is most used due to its simplicity. However, it still presents elongation errors for certain dielectric contrasts. The high undersampling measurement of the ECVT imaging system, which is mathematically defined as an undetermined linear system, is one of the most challenging issues. Compressive sensing (CS) is a framework that enables the recovery of a sparse signal or a signal that can be represented as sparse in a certain domain, by having a lower dimension of measurement data compared to the Shanon-Nyquist theorem. Thus, mathematically, this framework is promising for solving an undetermined linear system such as the ECVT imaging system. This paper discusses the possibility of developing an ECVT imaging technique for static objects based on a CS framework. Based on the simulation results, Non-optimized CS does not completely succeed in providing better ECVT imaging quality. However, it does provide more localized imaging compared to ILBP. In addition, by having fewer requirements for the measurement data dimension, the CS framework is promising for reducing the number of required electrodes.

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Section: B Introductionmentioning

confidence: 99%

Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework

2016

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The Critical Study of Mutual Coherence Properties on Compressive Sensing Framework for Sparse Reconstruction Performance: Compression vs Measurement System

Andryani¹,

Pradnyana

Gunawan

2019

J. Phys.: Conf. Ser.

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Compressive Sensing (CS) framework becomes well known since its ability to recover signal only by using less sampling required by Shanon-Nyquist theorem. The lack of required sampling is no longer constraint for having good reconstruction performance. The load is shifted to the reconstruction procedure instead of the sampling acquisition process. As long as the signal can be guaranteed sparse, the CS based method is able to provide high reconstruction accuracy. One of the CS principle is incoherence property, which can be represented by mutual coherence value. It represents the coherence between the sensing matrix and the sparse base dictionary. The theory said the less coherence between those two parameters, the more precise the reconstruction is. In fact, it is not consistently applied. The research presented on this paper find that, the theory is consistent for reconstruction on compression system, while it is not applied on the reconstruction of measurement system. Other properties are found to be more representative on assigning necessary condition for reconstruction performance on measurement system.

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Compressive Sensing Approach with Double Layer Soft Threshold for ECVT Static Imaging

Andryani¹,

Sudiana

Gunawan

2018

2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE)

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Combined feed-forward neural network and iterative linear back projection for Electrical Capacitance Volume Tomography

Cited by 4 publications

References 9 publications

Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework

Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework

The Critical Study of Mutual Coherence Properties on Compressive Sensing Framework for Sparse Reconstruction Performance: Compression vs Measurement System

Compressive Sensing Approach with Double Layer Soft Threshold for ECVT Static Imaging

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