Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Electrical Capacitance Volume Tomography (ECVT) for industrial and medical applications-An Overview

Tomography is a non-invasive, non-intrusive imaging technique allowing the visualization of phase dynamics in industrial and biological processes. This article reviews progress in Electrical Capacitance Volume Tomography (ECVT). ECVT is a direct 3D visualizing technique, unlike three-dimensional imaging, which is based on stacking 2D images to obtain an interpolated 3D image. ECVT has recently matured for real time, non-invasive 3-D monitoring of processes involving materials with strong contrast in dielectric permittivity. In this article, ECVT sensor design, optimization and performance of various sensors seen in literature are summarized. Qualitative Analysis of ECVT image reconstruction techniques has also been presented

Advances in EIT reconstruction through Simulated Annealing

EIT reconstruction can be solved as an optimization problem through Simulated Annealing (SA), but often at a high computational cost. This paper presents new techniques of EIT reconstruction through SA, including partial evaluation of the objective function, alternate objective functions and multi-objective optimization. Reconstructions of experimental impedance data using the techniques exposed were successfully performed

1D TiO2 nanostructures probed by 2D transmission electron microscopy

Hybrid solar cells based on nanoparticulate TiO2, dye and poly(3-hexylthiophene) are a common benchmark in the field of solid-state dye-sensitized solar cells. One-dimensionally nanostructured titanium dioxide is expected to enhance power-conversion efficiency (PCE) due to a high surface area combined with a direct path for electrons from the active interface to the back electrode. However, current devices do not meet those expectations and cannot surpass their mesoporous counterparts. This work approaches the problem by detailed investigation of diverse nanostructures on a nanoscale by advanced transmission electron microscopy (TEM). Anodized TiO2 nanotubes are analyzed concerning their crystallinity. An unexpectedly large grain size is found, and its implication is shown by corresponding solar cell characteristics which feature an above-average fill factor. Quasi-single crystalline rutile nanowires are grown hydrothermally, and a peculiar defect structure consisting of free internal surfaces is revealed. A growth model based on Coulombic repulsion and steric hindrance is developed to explain the resulting V-shaped defect cascade. The influence of the defects on solar cell performance is investigated and interpreted by a combination of TEM, electronic device characterization and photoluminescence spectroscopy, including lifetime measurements. A specific annealing treatment is proposed to counter the defects, suppressing several loss mechanisms and resulting in an improvement of PCEs by 35 %. Simultaneously, a process is developed to streamline electron-tomographic reconstruction of complex nanoparticles. Its suitability is demonstrated by the reconstruction of a gold nanostar and a number of iron-based particles distributed on few-layered graphene

Experimental study on a feasibility of using electromagnetic wave cylindrical cavity sensor to monitor the percentage of water fraction in a two phase system

This study proposed a microwave sensor system to monitor single and two phase flow systems. The microwave sensing technology in this study utilises the resonant frequencies that occur in a cylindrical cavity and monitor the changes in the permittivity of the measured phases to differentiate between the volume fractions of air, water and oil. The sensor system used two port configuration S21 (acted as transmitter and receiver) to detect the fluids inside the pipe. In principle, the strong polarity of water molecules results in higher permittivity in comparison to other materials. A tiny change of water fraction will cause a significant frequency shift. Electromagnetic waves in the range of 5 GHz to 5.7 GHz have been used to analyse a two phase air-water and oil-water stratified flow in a pipeline. The results demonstrated the potential of a microwave sensing technique to be used for the two phase systems monitoring. A significant shift in the frequency and change in the amplitude clearly shows the percentage fraction change of water in the pipe. The temperature study of water also demonstrated the independence of microwave analysis technique to the temperature change. This is accounted to overlapping modes negating the affect. Statistical analysis of the amplitude data for two phase systems shows a linear relationship of the change in water percentage to the amplitude. The electromagnetic wave cavity sensor successfully detected the change in the water fraction inside the pipe between 0-100%. The results show that the technique can be developed further to reduce the anomalies in the existing microwave sensor

Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many linear inverse problems, nonlinear problems tend to impair the performance of the method. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets -- the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems