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 EVALUATION OF THE ELECTRICAL CAPACITANCE TOMOGRAPHY SYSTEM FOR MEASUREMENT USING 3D SENSOR

2019 ◽  
Vol 9 (4) ◽  
pp. 52-59
Author(s):  
Jacek Kryszyn ◽  
Damian Wanta ◽  
Waldemar T. Smolik
Keyword(s):  
Image Reconstruction ◽  
High Speed ◽  
Modular System ◽  
Capacitance Measurement ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
3D Image Reconstruction ◽  
Small Uncertainty ◽  
Small Change ◽  
Capacitance Tomography

Further tests of EVT4 data acquisition system for electrical capacitance tomography are presented. The modular system, which can have up to 32 channels with an individual analogue to digital converter, was designed to ensure small uncertainty of capacitance measurement at high speed of imaging. The system’s performance in the context of 3D imaging was experimentally verified. In particular, we show that the measurement of changes in capacitance due to a small change of an electric permittivity distribution for the most distant electrodes in a suitably designed 3D sensor is possible using our system. Cross-plane measurements together with the measurements for the pairs of most distant electrodes are essential for accurate reconstruction of 3D distributions. Due to sensitivity of capacitance measurements obtained in the hardware, the measurements for all electrode pairs can be used in the inverse problem – the system of equations can be extended. Although the numerical condition number of a matrix of such a system is high, image reconstruction is possible from the data obtained in our system. The results of 3D image reconstruction for simple test objects are shown.

scholarly journals ACCELERATION OF IMAGE RECONSTRUCTION PROCESS IN THE ELECTRICAL CAPACITANCE TOMOGRAPHY 3D IN HETEROGENEOUS, MULTI-GPU SYSTEM

2017 ◽  
Vol 7 (1) ◽  
pp. 37-41 ◽  
Author(s):  
Michał Majchrowicz ◽  
Paweł Kapusta ◽  
Lidia Jackowska-Strumiłło ◽  
Dominik Sankowski
Keyword(s):  
Image Reconstruction ◽  
Electrical Capacitance Tomography ◽  
High Time Resolution ◽  
Electrical Capacitance ◽  
Reconstruction Process ◽  
Significant Development ◽  
Innovative Method ◽  
Complicated Process ◽  
High Volatility ◽  
Capacitance Tomography

Electrical capacitance tomography is an innovative method for visualization of industrial processes. One of its main advantages is it’s high time resolution that allows to the usage of ECT in systems with high volatility. In recent years there has been significant development of electrical capacitance tomography 3D, which however, has significantly reduced industrial it’s applications due to the complicated process of image reconstruction. The authors propose the use of multi-node, multi-GPU system to accelerate the process of image reconstruction in ECT 3D.

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 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.

Sign in / Sign up

Export Citation Format

Share Document