scholarly journals Analysis of Application of Distributed Multi-Node, Multi-GPU Heterogeneous System for Acceleration of Image Reconstruction in Electrical Capacitance Tomography

2015 ◽  
Vol 20 (3) ◽  
pp. 5-14 ◽  
Author(s):  
Michał Majchrowicz ◽  
Paweł Kapusta ◽  
Lidia Jackowska-Strumiłło ◽  
Dominik Sankowski
Keyword(s):  
Image Reconstruction ◽  
Electrical Capacitance Tomography ◽  
Specific Nature ◽  
The Past ◽  
High Quality Image ◽  
Quality Image ◽  
Distributed Platform ◽  
Matrix Transposition ◽  
Capacitance Tomography ◽  
Reconstruction Time

Abstract 3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems.

Analysis of application of distributed multi-node, multi-GPU heterogeneous system for acceleration of image reconstruction in Electrical Capacitance Tomography

2013 ◽  
Vol 18 (2-3) ◽  
pp. 109-117
Author(s):  
Michał Majchrowicz ◽  
Paweł Kapusta ◽  
Lidia Jackowska-Strumiłło
Keyword(s):  
Image Reconstruction ◽  
Electrical Capacitance Tomography ◽  
Specific Nature ◽  
The Past ◽  
High Quality Image ◽  
Quality Image ◽  
Distributed Platform ◽  
Matrix Transposition ◽  
Capacitance Tomography ◽  
Reconstruction Time

Abstract 3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems.

scholarly journals Optimization of Distributed Multi-Node, Multi-GPU, Heterogeneous System for 3D Image Reconstruction in Electrical Capacitance Tomography

2016 ◽  
Vol 21 (3) ◽  
pp. 81-90 ◽  
Author(s):  
Michał Majchrowicz ◽  
Paweł Kapusta ◽  
Lidia Jackowska-Strumiłło ◽  
Dominik Sankowski
Keyword(s):  
Image Reconstruction ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Process ◽  
Non Invasive ◽  
3D Image Reconstruction ◽  
High Quality Image ◽  
Tomography Reconstruction ◽  
Capacitance Tomography ◽  
Reconstruction Time

Abstract Electrical Capacitance Tomography is a non-invasive imaging technique, which allows visualization of the industrial processes interior and can be applied to many branches of the industry. Image reconstruction process, especially in case of 3D images, is a very time consuming task (when using classic processors and algorithms), which in turn leads to an unacceptable waiting time and currently limits the use of 3D Electrical Capacitance Tomography. Reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem must be able to transform capacitance data into images in a fraction of a second. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms, time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for further optimizations of previously developed algorithms.

scholarly journals Adaptive Selection of Truncation Radius in Calderon’s Method for Direct Image Reconstruction in Electrical Capacitance Tomography

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2014 ◽  
Author(s):  
Shijie Sun ◽  
Lijun Xu ◽  
Zhang Cao ◽  
Jiangtao Sun ◽  
Wenbin Tian
Keyword(s):  
Image Reconstruction ◽  
Direct Image ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Phase Information ◽  
Adaptive Selection ◽  
The Past ◽  
Scattering Transform ◽  
Capacitance Tomography ◽  
Selection Of

Calderon’s method has been successfully used for the direct image reconstruction in electrical capacitance tomography. In the method, the truncation radius adopted in numerical integral greatly influences the reconstruction results. In the past, the truncation radius is selected as a constant empirically according to the permittivity distribution pattern and noise level. In this paper, the influence of the truncation radius in Calderon’s method on the reconstruction results was first analyzed by numerical simulation. Then, a strategy for adaptive selection of the truncation radius was proposed. The amplitude information of the elements in the scattering transform matrix computed from the Dirichlet-to-Neumann (DN) map was used to determine the range for the truncation radius selection, and the phase information was further used to select a proper truncation radius value within this selection range. Finally, experiments were carried out to verify the strategy. Experimental results showed that small relative image error and good visual effect could be obtained by using the truncation radius selected by the proposed strategy.

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

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