Modern Electromobility and the Possibility of How to Achieve a Higher Capacity of the Li-ion Batteries

This article is describing the evolution of modern electromobilitywith describing the problematics connected with the shape of cells in battery modules. There are mentioned Li-ion battery's anode materials with their basic parameters and one of the conversion materials, silicon, which looks like promising material for future enhancing anode capacity. Usage of this material brings some new challenges, which prevents use in practice and must be solved. One of these solutions can be by applying external pressure, which can, for example, improve internal conductivity

A Krylov subspace type method for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a well-known imaging technique for detecting the electrical properties of an object in order to detect anomalies, such as conductive or resistive targets. More specifically, EIT has many applications in medical imaging for the detection and location of bodily tumors since it is an affordable and non-invasive method, which aims to recover the internal conductivity of a body using voltage measurements resulting from applying low frequency current at electrodes placed at its surface. Mathematically, the reconstruction of the internal conductivity is a severely ill-posed inverse problem and yields a poor quality image reconstruction. To remedy this difficulty, at least in part, we regularize and solve the nonlinear minimization problem by the aid of a Krylov subspace-type method for the linear sub problem during each iteration. In EIT, a tumor or general anomaly can be modeled as a piecewise constant perturbation of a smooth background, hence, we solve the regularized problem on a subspace of relatively small dimension by the Flexible Golub-Kahan process that provides solutions that have sparse representation. For comparison, we use a well-known modified Gauss-Newton algorithm as a benchmark. Using simulations, we demonstrate the effectiveness of the proposed method. The obtained reconstructions indicate that the Krylov subspace method is better adapted to solve the ill-posed EIT problem and results in higher resolution images and faster convergence compared to reconstructions using the modified Gauss-Newton algorithm.

Automated filtering in the nonlinear Fourier domain of systematic artifacts in 2D electrical impedance tomography

<p style='text-indent:20px;'>For patients undergoing mechanical ventilation due to respiratory failure, 2D electrical impedance tomography (EIT) is emerging as a means to provide functional monitoring of pulmonary processes. In EIT, electrical current is applied to the body, and the internal conductivity distribution is reconstructed based on subsequent voltage measurements. However, EIT images are known to often suffer from large systematic artifacts arising from various limitations and exacerbated by the ill-posedness of the inverse problem. The direct D-bar reconstruction method admits a nonlinear Fourier analysis of the EIT problem, providing the ability to process and filter reconstructions in the nonphysical frequency regime. In this work, a technique is introduced for automated Fourier-domain filtering of known systematic artifacts in 2D D-bar reconstructions. The new method is validated using three numerically simulated static thoracic datasets with induced artifacts, plus two experimental dynamic human ventilation datasets containing systematic artifacts. Application of the method is shown to significantly reduce the appearance of artifacts and improve the shape of the lung regions in all datasets.</p>

Magnetic induction pneumography: a planar coil system for continuous monitoring of lung function via contactless measurements

Continuous monitoring of lung function is of particular interest to the mechanically ventilated patients during critical care. Recent studies have shown that magnetic induction measurements with single coils provide signals which are correlated with the lung dynamics and this idea is extended here by using a 5 by 5 planar coil matrix for data acquisition in order to image the regional thoracic conductivity changes. The coil matrix can easily be mounted onto the patient bed, and thus, the problems faced in methods that use contacting sensors can readily be eliminated and the patient comfort can be improved. In the proposed technique, the data are acquired by successively exciting each coil in order to induce an eddy-current density within the dorsal tissues and measuring the corresponding response magnetic field strength by the remaining coils. The recorded set of data is then used to reconstruct the internal conductivity distribution by means of algorithms that minimize the residual norm between the estimated and measured data. To investigate the feasibility of the technique, the sensitivity maps and the point spread functions at different locations and depths were studied. To simulate a realistic scenario, a chest model was generated by segmenting the tissue boundaries from NMR images. The reconstructions of the ventilation distribution and the development of an edematous lung injury were presented. The imaging artifacts caused by either the incorrect positioning of the patient or the expansion of the chest wall due to breathing were illustrated by simulations.

A novel deep neural network method for electrical impedance tomography

Image reconstruction for Electrical Impedance Tomography (EIT) is a highly nonlinear and ill-posed inverse problem. It requires the design and employment of feasible reconstruction methods capable to guarantee trustworthy image generation. Deep Neural Networks (DNN) have a powerful ability to express complex nonlinear functions. This research paper introduces a novel framework based on DNN aiming to achieve EIT image reconstruction. The proposed DNN model, comprises of the following two layers, namely: The Stacked Autoencoder (SAE) and the Logistic Regression (LR). It is trained using the large lab samples which are obtained by the COMSOL simulation software (a cross platform finite elements analysis solver). The relationship between the voltage measurement and the internal conductivity distribution is determined. The untrained voltage measurement samples are used as input to the trained DNN, and the output is an estimate for image reconstruction of the internal conductivity distribution. The results show that the proposed model can achieve reliable shape and size reconstruction. When white Gaussian noise with a signal-to-noise ratio of 30, 40 and 50 were added to test set, the proposed DNN structure still has good imaging results, which proved the anti-noise capability of the network. Furthermore, the network that was trained using simulation data sets, would be applied for the EIT image reconstruction based on the experimental data that were produced after preprocessing.

Enhanced imaging of piezoresistive nanocomposites through the incorporation of nonlocal conductivity changes in electrical impedance tomography

Electrical impedance tomography is a method of noninvasively imaging the internal conductivity distribution of a domain. Because many materials exhibit piezoresistivity, electrical impedance tomography has considerable potential for application in structural health monitoring. Despite its numerous benefits such as being low cost, providing continuous sensing, and having the ability to be employed in real time, electrical impedance tomography is limited by several important factors such as the ill-posed nature of the inverse problem and the requirement for large electrode arrays to produce quality images. Unfortunately, current methods of mitigating these limitations impose upon the benefits of electrical impedance tomography. Herein, we propose a multi-physics approach of enhancing electrical impedance tomography without sacrificing any of its benefits. This approach is predicated on coupling global conductivity changes with the electrical impedance tomography inversion process thereby adding additional constraints and rendering the problem less ill-posed. Additionally, we leverage physically motivated global conductivity changes in the context of piezoresistive nanocomposites. We demonstrate this proof of concept with numerical simulations and demonstrate that by incorporating multiple conductivity changes, the rank of the sensitivity matrix can be improved and the quality of electrical impedance tomography reconstructions can be enhanced. The proposed method, therefore, has the potential of easing the implementation burden of electrical impedance tomography while concurrently enabling high-quality images to be produced without imposing on the major advantages of electrical impedance tomography.

An asymptotic formula for boundary potential perturbations in a semilinear elliptic equation related to cardiac electrophysiology

In this paper, we provide a representation formula for boundary voltage perturbations caused by internal conductivity inhomogeneities of low volume fraction in a simplified monodomain model describing the electrical activity of the heart. We derive such a result in the case of a nonlinear problem. Our long-term goal is the solution of the inverse problem related to the detection of regions affected by heart ischemic disease, whose position and size are unknown. We model the presence of ischemic regions in the form of small inhomogeneities. This leads to the study of a boundary value problem for a semilinear elliptic equation. We first analyze the well-posedness of the problem establishing some key energy estimates. These allow us to derive rigorously an asymptotic formula of the boundary potential perturbation due to the presence of the inhomogeneities, following an approach similar to the one introduced by Capdeboscq and Vogelius in [A general representation formula for boundary voltage perturbations caused by internal conductivity inhomogeneities of low volume fraction, Math. Model. Numer. Anal. 37 (2003) 159–173] in the case of the linear conductivity equation. Finally, we propose some ideas of the reconstruction procedure that might be used to detect the inhomogeneities.

Newton Method to Recover the Phase Accumulated during MRI Data Acquisition

For an internal conductivity image, magnetic resonance electrical impedance tomography (MREIT) injects an electric current into an object and measures the induced magnetic flux density, which appears in the phase part of the acquired MR image data. To maximize signal intensity, the injected current nonlinear encoding (ICNE) method extends the duration of the current injection until the end of the MR data reading. It disturbs the usual linear encoding of the MRk-space data used in the inverse Fourier transform. In this study, we estimate the magnetic flux density, which is recoverable from nonlinearly encoded MRk-space data by applying a Newton method.