Conductivity Distribution Research Articles

High frequency conductivity decomposition by solving physically constraint underdetermined inverse problem in human brain

The developed magnetic resonance electrical properties tomography (MREPT) can visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data from magnetic resonance imaging (MRI). The recovered high-frequency conductivity (HFC) value is highly complex and heterogeneous in a macroscopic imaging voxel. Using high and low b-value diffusion weighted imaging (DWI) data, the multi-compartment spherical mean technique (MC-SMT) characterizes the water molecule movement within and between intra- and extra-neurite compartments by analyzing the microstructures and underlying architectural organization of brain tissues. The proposed method decomposes the recovered HFC into the conductivity values in the intra- and extra-neurite compartments via the recovered intra-neurite volume fraction (IVF) and the diffusion patterns using DWI data. As a form of decomposition of intra- and extra-neurite compartments, the problem to determine the intra- and extra-neurite conductivity values from the HFC is still an underdetermined inverse problem. To solve the underdetermined problem, we use the compartmentalized IVF as a criterion to decompose the electrical properties because the ion-concentration and mobility have different characteristics in the intra- and extra-neurite compartments. The proposed method determines a representative apparent intra- and extra-neurite conductivity values by changing the underdetermined equation for a voxel into an over-determined minimization problem over a local window consisting of surrounding voxels. To suppress the noise amplification and estimate a feasible conductivity, we define a diffusion pattern distance to weight the over-determined system in the local window. To quantify the proposed method, we conducted a simulation experiment. The simulation experiments show the relationships between the noise reduction and the spatial resolution depending on the designed local window sizes and diffusion pattern distance. Human brain experiments (five young healthy volunteers and a patient with brain tumor) were conducted to evaluate and validate the reliability of the proposed method. To quantitatively compare the results with previously developed methods, we analyzed the errors for reconstructed extra-neurite conductivity using existing methods and indirectly verified the feasibility of the proposed method.

Three‐Dimensional Simulation of Equatorial Spread F: Effects of Field‐Aligned Plasma Flow and Ionospheric Conductivity

AbstractRecently a Three‐dimensional EquatoriaL spread F (TELF) model using high‐resolving power advection schemes have been developed at the University of Science and Technology of China. With the inclusion of the field‐aligned plasma flow and realistic ionospheric conductivity configuration, the TELF model reproduced typical features of the observed equatorial spread F (ESF) phenomena, including plasma depletions along flux tubes, geomagnetic conjugate irregularities, and ESF morphology as seen in ground optical and satellite observations. Through control simulations, the effects of field‐aligned plasma flow and ionospheric conductivity on ESF are investigated. It is found that field‐aligned plasma flow could inhibit the growth of bubbles by modulating the ionospheric conductivity distribution along field lines, and the plasma bubbles from the TELF model behave more physically in the topside F‐region than those from the 2D model due to the realistic ionospheric conductivity specification. Additionally, the simulations demonstrate that the amplitude and altitude of the initial perturbations can also affect the growth rate of ESF by modulating the polarization electric fields.

Metallogenic Prediction of Magnetite in the Pandian Area at the Northwest Margin of Luxi Uplift, China: Constraints of Wide-Field Electromagnetic Data

The Pandian deposit is a newly discovered contact metasomatic skarn magnetite deposit found in the Cainozoic super-thick overburden on the northwest margin of Luxi Uplift (LXU). Presently, the horizontal scale of the deposit delineated by the potential field (gravity and magnetic method) has shown giant potential for ore deposits, and mapping the ore-controlling structures in the vertical scale becomes a primary task for metallogenic prediction. In our study, the wide-field electromagnetic method (WFEM), with a strong anti-noise ability in recording electromagnetic signals on the surface at multiple frequencies, is applied to characterize the deep conductivity distribution of the Pandian area. Based on the inversion results from two parallel WFEM profiles, which consist of 105 sites and previous geological and geophysical results, the 2D geoelectric models are established. The low-resistivity regions (with a typical range of 25~32 Ω·m) in the electrical models are proven to be ore bodies of Pandian deposit, which are developed along the contact zone between Yanshanian intrusive rocks and Paleozoic Ordovician strata. The scattered bodies (typically >32 Ω·m) in Ordovician limestone strata are probably caused by intrusive diorite pluton closely related to magnetite mineralization. Due to contact metasomatism, bedded limestone near magnetite was metamorphosed into marble and accompanied by low-resistivity skarn alteration, with resistivity much different from its high-resistivity protolith. The inverted geoelectrical models visually reflect the spatial distribution features of intrusive rocks and lithologic alteration/fracture zones.

Regularization Solver Guided FISTA for Electrical Impedance Tomography.

Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton-Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.

Research on Multi-Physical Field Coupling Simulation of Local Electrochemical Machining

Casing is one of the most important components of an aircraft engine. However, due to its thin wall thickness and difficult-to-cut materials, it is difficult to process with a conventional mechanical method. Counter-rotating electrochemical machining (CRECM) is a special electrochemical machining method, which is very suitable for machining aircraft engine casing parts. However, for the convex structure with large surface height and a complex shape of the casing, is sometimes difficult for CRECM to obtain the desired design accuracy. Local electrochemical machining is proposed under this background, which is used for after-machining of the pre-shaped convex structure by CRECM. In order to predict the local electrochemical machining result accurately and improve the machining precision, this paper establishes a multi-physical field coupling simulation model of the local electrochemical machining considering the influence of gas–liquid two-phase flow and temperature field. The influence of a gas–liquid two-phase flow field and temperature field on the conductivity distribution were simulated and analyzed, the reason for simulation error with pure electric field and the influence of cathode end width L on machining accuracy was analyzed, and it was found that the gas–liquid two-phase flow field played a major role in the simulation results of local electrochemical machining. The experimental results show that there is a significant error between the pure electric field simulation results and the experimental results, and the multi-physical field coupling simulation results are basically consistent with the experimental results. The multi-physical field coupling simulation can predict the results of local electrochemical machining with high accuracy and has important significance for improving the precision of local electrochemical machining.

A linearized integral equation reconstruction method of admittivity distributions using Electrical Impedance Tomography

In this article, a novel reconstruction algorithm that approximates smooth complex admittivity distributions for Electrical Impedance Tomography is presented. This non-iterative algorithm is based on a linearized integral equation approach which has been extended to simultaneously reconstruct the conductivity and permittivity distributions of two-dimensional domains from boundary measurements of high-frequency alternating input currents and induced potentials. Reconstructions from noisy simulated data are obtained from single-time, time-difference and multiple-times data.

Thermal Contact Conductance Estimation on Irregular Interfaces Using the Reciprocity Functional Method

The Reciprocity Functional method, associated to the Classic Integral Transform Technique, has been successfully applied, obtaining analytical solutions for the inverse heat transfer problem that seeks to estimate the thermal contact conductance (TCC) distribution on the interface of a body composed of two materials. Yet, the theoretical development upon which this approach is based is not limited to the need of this interface to have a regular format. This work proposes to extend the method, thus obtaining an analytical development for the estimation of the TCC distribution on interfaces which are not necessarily regular. Several test problems were solved using the techniques described in this work, leading to very good results, with low CPU time usage by the computational implementation. The stability of the method was also verified, via a Monte Carlo analysis considering uncertainties in geometrical and thermophysical properties. The method presented good estimates even when noises were present in the measured temperatures, thermal conductivities, and geometrical dimensions.

Numerical Study on Thermal Stress of High Temperature Proton Exchange Membrane Fuel Cells during Start-Up Process

High-temperature proton-exchange membrane fuel cells (HT-PEMFCs) with phosphoric-doped polybenzimidazole (PBI) membranes have a higher operating temperature compared to the PEMFCs operating below 373.15 K. The fuel cell is first heated from room temperature to the minimum operating temperature to avoid the generation of liquid water. The existence of liquid water can result in the loss of phosphoric acid and then affect the cell performance. In this study, the start-up process of HT-PEMFCs is numerically studied by establishing a three-dimensional non-isothermal mathematical model. Preheated gas is supplied into gas flow channels to heat the fuel cell, and then voltage load is applied to accelerate the start-up process. Effects of voltage (0.9 V, 0.7 V and 0.5 V) and flow arrangement (co-flow and counter flow) on temperature, current density, proton conductivity and stress distributions of fuel cells are examined. It is found that the maximum stress is increased when a lower voltage is adopted, and the counter-flow arrangement provides a more uniform stress distribution than that of co-flow arrangement.

Simultaneous density and thermal conductivity depth profile reconstructions from noised thermal-wave amplitude and phase data using a combined integral-equation and imperialist competitive algorithm method

An efficient new thermal-wave inverse-problem approach based on an integral-equation boundary-value method coupled with an imperialist competitive algorithm was developed. The methodology was successfully applied to simultaneously reconstruct density and thermal conductivity depth profiles in a sintered powder metallurgy sample from an industrial automotive manufacturer with a surface layer of higher density than the bulk. The density and thermal conductivity depth profiles were validated independently using the manufacturer's data and in-house temperature and porosity measurements. The present non-destructive inverse problem approach represents a generalized formalism to thermal-wave reconstruction of dual depth profiles using frequency scan data measured from the interrogated surface. From a fundamental viewpoint, the method adds significant insights into the relationship between thermal conductivity and density distributions in inhomogeneous solids.

Ion-Implanted Epoxy With Graded Surface Conductivity: An Approach to Improve Surface Flashover Voltage

The electric field distribution along the insulator surface plays a significant role in improving surface flashover characteristics of the insulation in an HVdc power system. Functionally graded material with conductivity distribution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> -FGM) is a promising approach that enables the electric field to be more uniform, which leads to hinder the surface flashover. This article proposes a novel method for graded conductivity materials using surface modification via argon (Ar <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$ </tex-math></inline-formula> ) ion implantation. Epoxy samples with homogeneous and graded surface conductivity are prepared considering different ion implantations to verify the effectiveness of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> -FGM method. Flashover tests of the virgin, homogeneously ion-implanted, and graded ion-implanted samples are conducted in air and vacuum at negative dc voltage. Moreover, finite-element method (FEM)-based simulations are carried out using COMSOL Multiphysics software to analyze the electric field relaxation before and after modification. The experimental findings indicate that the flashover characteristics of homogenously implanted samples are improved with the rise in implanted layer conductivity. In particular, the flashover voltage of samples with graded conductivity layers is improved by 27.71% in air and 28.90% in vacuum compared to the virgin samples. The experimental findings and theoretical analysis prove that ion-implantation-based graded conductivity material is a promising solution for the insulation of HVdc applications.

A Hydrogel-Based Electronic Skin for Touch Detection Using Electrical Impedance Tomography.

Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor.

Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

SUMMARYMany geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a prioriinformation is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally constrained smooth inversion as well as to more standard (i.e., not structurally constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach.

All‐in‐One Compression and Encryption Engine Based on Flexible Polyimide Memristor

It is anticipated that the rapid development of the Internet of Things (IoT) will improve the quality of human life. Nonetheless, large amounts of data need to be replicated, stored, processed, and shared, posing formidable challenges to communication bandwidth and information security. Herein, it is reported that polyimide (PI) threshold‐switching memristors exhibit Gaussian conductance and randomly set voltage distribution with nonideal properties to create a compression and encryption engine with a single chip. The Gaussian conductance distribution is used to achieve compressed sensing (CS) to integrate encryption into compression, and the spontaneous formation of the one‐time‐sampling measurement matrix satisfies absolute security. Moreover, the bitstreams generated by randomly distributed set voltages are used to diffuse the ciphertext from CS to improve security. The engine is shown to be secure even if the eavesdropper knows both the plaintext and the corresponding ciphertext. It has compression performance advantages that take both efficiency and security into account. In addition, due to the superior high temperature and mechanical properties of PI, the engine can continue to function normally in harsh environments. Herein, an excellent solution is offered for ensuring the efficiency and security of IoT.

Integration of Geophysical Survey Data for the Identification of New Archaeological Remains in the Subsoil of the Akrai Greek Site (Sicily, Italy)

For more than a decade now, geophysical prospecting has been considered an integral part of archaeological research; the ability to quickly investigate large areas and locate objects buried in the ground without directly interacting with it is a key feature that makes such surveys essential for identifying and locating, with good accuracy, buried archaeological remains. In this study, two extensive magnetic and electromagnetic surveys were carried out in two different areas of the Akrai archaeological site, where given the distribution of archaeological remains already found at the site, it was likely that additional buried remains were present. The surveys were performed using a proton precession magnetometer and a multifrequency electromagnetic device with a frequency range of 2.5–250 kHz. By processing the data, the vertical magnetic gradient and electrical conductivity maps were obtained. Furthermore, 3D models of electrical conductivity distribution were reconstructed. On comparing the results, it was notable that many anomalies characterized by low vertical gradient values were identified within areas characterized by low electrical conductivity values. These anomalies detected by both surveys can be associated with good probability with buried archaeological remains made up of limestones. In fact, they exhibit shapes and sizes comparable to those of the wall remains already found at the site.

Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. It is known that image reconstruction of EIT is mathematically an ill-posed inverse problem that leads to poor reconstruction quality. To improve the quality of reconstructed images, a novel image reconstruction method based on modified bird swarm optimization algorithm is proposed in this paper. An image of conductivity distribution acquired by Newton's one-step error reconstruction algorithm performs as initial population. Then a modified bird swarm optimization algorithm is proposed to optimize the initial conductivity distribution. By introducing a dynamic inertia weight parameter, flight interval of the bird swarm varies with the number of iterations. With the proposed method, a strong global search capability in the initial stage and a strong local search capability in the later stage are ensured during the optimization. To verify the effectiveness of the proposed method, simulation work and experimental validation are carried out. Also, reconstructed images are compared with the results obtained by iterative Landweber method and Newton–Raphson method. Meanwhile, the impact of noise on the reconstruction is investigated. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables.

Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.

Clogging detection and productive layers identification along boreholes using Active Distributed Temperature Sensing

Fiber-Optic Active Distributed Temperature Sensing (FO-ADTS) experiments were performed on an Aquifer Thermal Energy Storage system (ATES) site located on the university campus of Bordeaux, France. The experiments consisted in heating the steel core of the FO cable while monitoring the rate of temperature increase during the heating periods. The changes in temperature, that were monitored through time at every depth under various hydraulic conditions and in different boreholes, were used to evaluate both aquifer properties and wells conditions. A first ADTS experiment was conducted under cross borehole configuration using a pumping well and a monitoring well separated by a distance of 8.5 meters. Then, to check the reciprocity of the results, a second experiment was conducted by switching the monitoring and the pumping well. The results obtained through the use of analytical solutions for reproducing and interpreting the data lead to the following conclusions: (i) ADTS can be used to estimate both thermal conductivity and Darcy velocity distribution along boreholes, crucial properties for ATES performance. (ii) The proposed method is a promising tool to detect clogging locations in the boreholes when it occurs. This can be of great practical interest to maintain systems performance, since, once FO cables deployed, experiments could be easily repeated without opening boreholes and stop the system operation.

Magnetic Induction Tomography: Separation of the Ill-Posed and Non-Linear Inverse Problem into a Series of Isolated and Less Demanding Subproblems.

Magnetic induction tomography (MIT) is based on remotely excited eddy currents inside a measurement object. The conductivity distribution shapes the eddies, and their secondary fields are detected and used to reconstruct the conductivities. While the forward problem from given conductivities to detected signals can be unambiguously simulated, the inverse problem from received signals back to searched conductivities is a non-linear ill-posed problem that compromises MIT and results in rather blurry imaging. An MIT inversion is commonly applied over the entire process (i.e., localized conductivities are directly determined from specific signal features), but this involves considerable computation. The present more theoretical work treats the inverse problem as a non-retroactive series of four individual subproblems, each one less difficult by itself. The decoupled tasks yield better insights and control and promote more efficient computation. The overall problem is divided into an ill-posed but linear problem for reconstructing eddy currents from given signals and a nonlinear but benign problem for reconstructing conductivities from given eddies. The separated approach is unsuitable for common and circular MIT designs, as it merely fits the data structure of a recently presented and planar 3D MIT realization for large biomedical phantoms. For this MIT scanner, in discretization, the number of unknown and independent eddy current elements reflects the number of ultimately searched conductivities. For clarity and better representation, representative 2D bodies are used here and measured at the depth of the 3D scanner. The overall difficulty is not substantially smaller or different than for 3D bodies. In summary, the linear problem from signals to eddies dominates the overall MIT performance.

Multi-frequency complex conductivity sparse imaging of plant root zone based on space-frequency correlation

The root zone provides indispensable water and nutrients for plants and monitoring the dynamics of the root zone is highly important. However, due to the opacity and complexity of this zone, in situ characterization of the root zone poses many challenges. A sparse Bayesian learning complex model based on spatial frequency correlation (SF-SBLC) was proposed for fast nondestructive imaging of the growth trends in the root zone of plants, and multi-frequency electrical impedance tomography (mfEIT) was used to reconstruct the complex conductivity distribution of the root zone. To achieve the best sparse coefficients and enhance the quality of the reconstructed images, the SF-SBLC learned and utilized the spatial and frequency correlations of the complex conductivities at different frequencies. The reconstruction algorithm was further optimized using a concave-convex process method and a multi-measure vector model, which decreased its time complexity. When compared to conventional mfEIT reconstruction algorithms, the experimental findings demonstrated that the SF-SBLC could greatly reduce imaging artifacts, generate higher reconstruction performance (in terms of the image correlation coefficient, relative image error, resolution, position error, shape deformation, and ringing), and shorten imaging time. Moreover, the SF-SBLC was able to recreate the root zone's shape, size, and location in both a nutrient solution and a horticulture substrate. Finally, the effective volume of the root zone is defined and calculated based on the 3D EIT image. This technique offers a prospective tool that might be useful for monitoring the root growth trends and expansion rates of potted plants.

Gastric functional monitoring by gastric electrical impedance tomography (gEIT) suit with dual-step fuzzy clustering

Gastric Function has been successfully estimated by gastric electrical impedance tomography (gEIT) Suit with dual-step fuzzy clustering. The gEIT Suit which are made of elastic cloth with dual-planar electrodes and compact data acquisition (DAQ) system measures gastric impedance Z to visualize the gastric conductivity distribution σ. The dual-step fuzzy clustering extracts the clustered gastric conductivity distribution kσ, which accurately estimates the gastric function. The gEIT Suit with dual-step fuzzy clustering are applied to eight healthy persons during liquid meal consumption to estimate the gastric function under gastric accommodation phase of 200, 400 and 600 mL based on the gastric emptying phase. As the results, the gEIT Suit successfully estimate the gastric function. By the measured impedance Z, the subjects have a mean temporal impedance overline{Delta mathbf{Z} }= − 9.27 [Ohm] and p-value of that overline{mathbf{Z} }p(Z) = 0.0013[–]as the t-test result. In the case of gastric conductivity distribution σ, the subjects have a value of spatial mean conductivity distribution ⟨σ⟩ = 0.23[–] and p-value of that ⟨σ⟩ p(σ) = 0.0140[–]. Lastly, in the case gastric volume V, subjects have a gastric volume V = 12.44 [%] and p-value p(V) = 0.0664[–].