**Note:** This preprint has been accepted for publication in [ IEEE Transactions on Circuits and Systems--I: Regular Papers ]. The published version is available on IEEE Xplore: [DOI link](  https://ieeexplore.ieee.org/document/10607897 ). The paper presents a design for a high-speed data acquisition (DAQ) system for electrical impedance tomography (EIT). The proposed solution involves using a high-speed analog-to-digital converter (ADC) to digitize the analog signals corresponding to multiple pairs of electrodes within the same cycle in a time-multiplexed manner. The extracted samples are fed to an artificial neural network (ANN) to estimate the peak amplitudes of the signals for every channel, which are then used for image reconstruction. Various ANN models with customized loss functions were assessed, and the optimal model selection approach using the grid search technique is presented. Unlike other multi-frequency based techniques, the suggested approach does not require a multi-frequency current source, thus simplifying the data acquisition system by not requiring high-quality narrow-band pass-band filters for different frequencies. The proposed approach allows EIT systems to operate at a very high throughput that can exceed 2,800 frames per second for a 50 kHz excitation signal using 32 or more electrodes. Extensive experimental testing showed that peak estimation accuracy can be achieved with more than 98%, even for signals with 40 dB SNRs. The suggested approach has thus promising potential for EIT applications requiring high SNR and fast data acquisition.

In electrical impedance tomography (EIT) systems, various current sources were designed to deliver constant electrical current towards the electrodes, irrespective of the conductivity of the conductive medium under test. This, however, is not possible to achieve in practice since the water conductivity ranges from 5 S/m for sea water down-to 5.5 x10-6 and 5 x 10-3 S/m for pure and drinking water respectively which are substantially low values [1]. Thus, even in case of high water-cut fluid, unless the water is very salty, the usage of such current sources may not be appropriate and their substitution with an electrical capacitance tomography system or a dielectric measurement sensor may be required. Indeed, even if the conductivity of the medium is known and high, the gross conductivity formed between a given pair of electrodes depend heavily on the phase’s distribution pattern between them and can be excessively low, causing high electric current fluctuations. One of the contributions of this paper is to experimentally assess the effect of the electrical current fluctuations on the accuracy of the EIT image reconstruction using three different cutting-edge and most widely current sources designs. To the authors’ best knowledge, this study is the first of its kind as all other prior works assume that the electrical current is constant in the formulation of the EIT forward and inverse problems. The paper also suggests a new cost-effective measurement circuit design that overcomes the fluctuations. It continuously measures the electrical current consumed during every single excitation cycle using a very high-speed analog-to-digital (ADC) converter, interfaced with Cyclone V field program gate array (FPGA), to compensate for the associate voltage readings accordingly during the image reconstruction procedure. The assessment of the system was conducted experimentally, and the results were compared with those provided by the three current sources. The associated results show the higher accuracy of the suggested design, when using Gauss Newton (GN) method, in terms of mean-squared error, which was decreased by 75%, and the image correlation coefficient as well.

This paper suggests a high resolution, high frame rate strain mapping sensor that uses three layers to measure the position as well as the value of the strain applied at different locations of the sensor. The top layer which should be highly conductive can be connected to either a current source or a voltage source, depending on the value of conductivity separating it from the ground during the switching sequence. The middle layer which consists of a strain-compressible material, such as piezoresistive foam, exhibits a variable electric resistance the value of which decreases with the increase of the applied strain. In this paper, the sensor sensitivity ranges from 50 to 500 kPa, however it can accommodate any other type of piezoresistive material provided that an adequate calibration is done. The lower layer consists of segmented highly electrically conductive tracks, the pattern of which allows to detect both the location, and the strain intensity at the points of contacts. The sensor is designed to also compensate for eventual changes of the electric resistance function of the temperature. Additionally, it has the advantage to mitigate eventual crosstalks that may occur between adjacent electrodes, since it keeps grounding utmost two electrode, forcing the electric current to flow into only two points. To our best knowledge, these simultaneous attributes have not been reported by a single system. This yields the advantage of using the sensor for a wide range of applications, including rehabilitation and human-computer interaction. A series of experiments and FEM simulations reveal that the sensor is highly accurate and can provide both the location and intensity of multiple contacts with an accuracy of 97.5 % at a frame rate of 20 frames/s when using 29 electrodes.

A high-resolution two-dimensional (2D) electrical impedance tomography (EIT) system requires a larger number of electrodes and a finer mesh than its traditional counterpart. This increases the required number of measurements and, in turn, the amount of computation for the image reconstruction. Given the inverse and ill-posed nature of the EIT systems, they require a high signal-to-noise ratio (SNR) acquisition system as well as a high-precision hardware accelerator platform. In this paper, we present a field programmable gate array (FPGA)-based acquisition system with a tunable single-frequency current source that can reach an acquisition speed of more than 500 and 2400 frames per second (fps) for an excitation signal frequency of 500 kHz using 32 and 16 electrodes, respectively. The data processing and reconstruction are carried out using the most recent embedded Graphical Processing Unit (GPU, Nvidia Jetson Orin) by utilizing multiple Cuda cores to perform parallel high-speed 2D image reconstruction. Five different algorithms, namely linear back projection (LBP), Tikhonov regularization (TK), one-step Gauss Newton (GN), Landweber (LW), and iterative Tikhonov (ITK), were used for investigation. A gain in speed-up of at least 4 times was observed over the traditional implementations on recent general-purpose computers (PCs). Extensive experiments indicate that the proposed system can yield a throughput of more than 2500 fps for a 16-electrode system with around 8192 mesh elements. This paves the way for EIT systems to be potentially used in high-speed imaging applications as well as in 3D EIT applications which involve even larger amount of mesh elements.