Application of GPU Parallel Computing for Acceleration of Finite Element Method Based 3D Reconstruction Algorithms in Electrical Capacitance Tomography

Kapusta, Paweł; Majchrowicz, Michał; Sankowski, Dominik; Banasiak, Robert

doi:10.2478/v10248-012-0063-6

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…It works independently for both swirl sides and can be processed within separate threads what additionally can reduce the processing time. In the future, the optimization at higher resolutions, up to 8K (7680 x 4320 pixels), will be implemented based on the massive parallel computations (nVidia CUDA [3]). It would be possible to divide the image matrix to rows for each swirl side and to run all such slices on separate GPU cores.…”

Section: Edge Detectionmentioning

confidence: 99%

The Diagnostic of Two-Phase Separation Process Using Digital Image Segmentation Algorithms

Łukiański

Wajman²

2020

IAPGOS

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The project aimed to develop and implement algorithms to diagnose the phase separation process based on digital images. The image processing techniques and various numerical methods for interpolation and integration were used to identify the process state. The swirl’s volume and diameters at its three different levels can be determined on-line. A consistent diagnostic signal is produced and can be used by the control unit. The program was written in Python using the OpenCV library that allows the analysis of digital images. The article presents the developed procedure that provides reliable results despite the poor quality of the input source video stream. The complete procedure was described with the results’ presentation and discussion at each step.

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Section: Edge Detectionmentioning

confidence: 99%

The Diagnostic of Two-Phase Separation Process Using Digital Image Segmentation Algorithms

Łukiański

Wajman²

2020

IAPGOS

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“…If the image is especially important for the issue of monitoring and visualization, the image reconstruction and processing tasks can be performed on graphical processors. Kapusta et al [70] developed an effective method called a General Purpose computing on Graphics Processing Units that involves a hybrid algorithm for rapid, parallel determination of the solution of forward problem implementing CUDA API and OpenCL libraries by uses of both x86/x64 class and graphics processors.…”

Section: Imaging or Not Imaging?mentioning

confidence: 99%

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Wajman

2019

ITC

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Nowadays, the application of advanced technologies in modern production systems is the main trend of development and technological progress in many industrial sectors. It is due to the still growing trends of energy-saving and production quality enhancement. Wherever in the production process the phase mixture is transported and it is not optimal or not economic, there is a need to develop a system which would be able to prevent any construction disaster, unexpected production line stopping or situation where for reasons of bad flow parameters, the final product is defective. This paper studies various sensors, measurement techniques and computer methods for signal processing and analysis to diagnose and control two-phase flows. Due to the possibility that the invasive measurement disturbs the process and changes it parameters and behavior especially in the location just after the measurement point and simultaneously does not provide any information about these changes it is unreliable for the diagnosis or control. Therefore, the non-invasive techniques commonly used for measurement of flows parameters are described. Depending on the industrial demands many of applications examples for non-invasive two-phase flows measurement and monitoring are given. This description for identifying the flow parameters is divided into features categories of this phenomena as void fraction distribution, velocity profile and flow regime. However, from these methods the high accuracy and short processing time is expected. The continued observation and monitoring of the process abnormalities can provide valuable information about its dynamic state and allow for real-time and automatic monitoring and control. Therefore, the development of advanced process control is one of the most important challenges to keep the flow regime on the given level and for instant and long-term energy saving, quality improvement.

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“…The most efficient line is the last one (***). It includes the matrix multiplication process, which is considered the most efficient parallelised linear algebra transformation [18]. The acceleration of matrix multiplication is huge, up to several hundredfold.…”

Section: Sequential Cpu Implementationmentioning

confidence: 99%

“…As a result, the implementation of graphics processing units (GPU) has gradually gained popularity [15]. The implementation of GPU techniques has been investigated in the area of both hardfield [16] and soft-field tomography [14] to achieve computational gain, including electrical impedance tomography (EIT) [17] and electrical capacitance tomography (ECT) [18]. Compared to the use of GPU in previous MIT research [19,20], where the parallelisation of the finite difference (FD) algorithm was achieved, in this work, the time reduction using GPU is realised differently.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Induction Tomography With High Performance Gpu Implementation

Ma¹,

Banasiak

Soleimani

2016

PIER B

Self Cite

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Magnetic induction tomography (MIT) is a non-invasive medical imaging technique with promising applications such as brain imaging and cryosurgery monitoring. Despite its potential, the realisation of medical MIT application is challenging. The computational complexity of both the forward and inverse problems, and specific MIT hardware design are the major limitations for the development of MIT research in medical imaging. The MIT forward modeling and linear system equations for large scale matrices are computationally expensive. This paper presents the implementation of GPU (graphics processing unit) for both forward and inverse problems in MIT research. For a given MIT mesh geometry composed of 167,488 tetrahedral elements, the GPU accelerated Biot-Savart Law for solving the free space magnetic field and magnetic vector potential is proved to be over 200 times faster compared to the time consumption of a CPU (central processing unit). The linear system equation arising from the forward and inverse problem, can also be accelerated using GPU. Both simulations and experimental results are presented based on a new GPU implementation. Laboratory experimental results are shown for a phantom study representing potential cryosurgery monitoring using an MIT system.

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Application of GPU Parallel Computing for Acceleration of Finite Element Method Based 3D Reconstruction Algorithms in Electrical Capacitance Tomography

Cited by 6 publications

References 5 publications

The Diagnostic of Two-Phase Separation Process Using Digital Image Segmentation Algorithms

The Diagnostic of Two-Phase Separation Process Using Digital Image Segmentation Algorithms

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Magnetic Induction Tomography With High Performance Gpu Implementation

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