Amplitude demodulation for electrical capacitance tomography based on singular value decomposition

Hao, Zhenhua; Cui, Ziqiang; Wang, Huaxiang

doi:10.1063/1.5038629

Cited by 10 publications

(1 citation statement)

References 30 publications

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“…The computation of the singular value decomposition (SVD) of a non-square matrix [1][2][3] plays a key role in a number of applications (see, for instance, [4][5][6][7][8][9][10][11][12][13]); among them, it is worth citing applications in automatic control [14], digital circuit design [15], time-series prediction [16], and image processing [17,18]. Efforts have been devoted to achieving the computation of the SVD of matrices in the artificial neural networks community [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices

et al. 2020

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The present paper deals with neural algorithms to learn the singular value decomposition (SVD) of data matrices. The neural algorithms utilized in the present research endeavor were developed by Helmke and Moore (HM) and appear under the form of two continuous-time differential equations over the special orthogonal group of matrices. The purpose of the present paper is to develop and compare different numerical schemes, under the form of two alternating learning rules, to learn the singular value decomposition of large matrices on the basis of the HM learning paradigm. The numerical schemes developed here are both first-order (Euler-like) and second-order (Runge-like). Moreover, a reduced Euler scheme is presented that consists of a single learning rule for one of the factors involved in the SVD. Numerical experiments performed to estimate the optical-flow (which is a component of modern IoT technologies) in real-world video sequences illustrate the features of the novel learning schemes.Electronics 2020, 9, 334 2 of 21 (Runge-like). Moreover, a reduced Euler scheme will be presented that consists of a single learning equation for one of the factors involved in the SVD.The developed methods to learn an SVD of a data matrix are applied to optical-flow estimation. Optical flow estimation is a well-known image-processing operation that allows estimating the motion of portions of an image over a video sequence and has found widespread applications (see, for instance, [22][23][24][25][26][27][28][29]). Optical flow is closely related to motion estimation [30]. Optical flow refers to the change of structured light in an image and captures such change through a velocity vector field. Most of the optical-flow estimation algorithms used in video encoding belong to either the class of block matching algorithms (BMAs) or to the class of pixel recursive algorithms (PRAs) [31].Optical flow estimation algorithms are essential components in a number of complex Internet of Things (IoT) technologies, as testified by several existing applications, such as intelligent fall detection [32,33], mobile robotics [34], intelligent flight monitoring [35], mobile object tracing [36], automated surveillance [37], smart healthcare [38], solar energy forecasting [39], and in IoT-based analytics in urban space, shops, and retail stores to inform policy makers, shop owners, and the general public about how they interact with the physical space [40]. See also the interesting discussion in [41].The majority of the current optical-flow estimation methods rely on a block-matching algorithm. The BMA methods are based on the concept of template-matching: it is supposed that a single block in a time-frame has moved solidly to another location in the next time-frame, so the image-block is regarded as a template to be looked for in the subsequent frame. The BMA methods try to evaluate the motion of a block by reducing the number of search locations in the search range and/or by reducing the number of computations at each search location. These algorithm...

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Section: Introductionmentioning

confidence: 99%

First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices

et al. 2020

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Fluidization behavior monitoring of phosphogypsum in cyclone separator using electrical capacitance tomography

Hao

Wang

Sun

et al. 2019

Review of Scientific Instruments

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The fluidized behavior and the concentration distribution of phosphogypsum in the cyclone separator are studied and analyzed using twin-plane electrical capacitance tomography. Particle concentration is relatively low in the central region of the cyclone separator, and the concentration near the side wall of the cyclone separator is relatively high. With the increase in wind velocity, the radial particle concentration decreases, and the separation efficiency increases first and then decreases. Finally, regression analysis is used to fit the radial concentration distribution data of phosphogypsum. The analysis results are helpful to improve the cyclone structure and separation efficiency and to optimize the size of the cyclone separator.

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Survey of signal clamping for digital phase sensitive detector

Cui

Wang

2020

Review of Scientific Instruments

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Electrical capacitance tomography (ECT) is a multiphase flow detection technology, which has the advantages of nonradioactive, noninvasive, and on-line visualization measurements. In ECT measurements, the digital phase sensitive demodulation (PSD) plays an important role in guaranteeing the accuracy of small capacitance measurements. Generally, the clamping circuit is implemented to protect the A/D converter. In the measuring circuit, a programmable gain amplifier is usually employed to expand the dynamic range of PSD. However, the measured signal may exceed the measurement range from time to time, which induces the signal clamping effect. In this case, the digital PSD will present large errors in the capacitance measurements. In consideration of the frequency spectrum characteristic of the clamped signal, two digital PSD methods have been proposed. The proposed methods have been implemented in a field programmable gate array based measurement system. The experimental results show that the proposed multisine PSD method could expand the dynamic range by a factor of 5, at a relatively low error, i.e., ≤2%.

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Amplitude demodulation for electrical capacitance tomography based on singular value decomposition

Cited by 10 publications

References 30 publications

First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices

First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices

Fluidization behavior monitoring of phosphogypsum in cyclone separator using electrical capacitance tomography

Survey of signal clamping for digital phase sensitive detector

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