Electrical bioimpedance entails the measurement of the electrical properties of tissues as a function of frequency. It is thus a spectroscopic technique. It has been applied in a plethora of biomedical applications for diagnostic and monitoring purposes. In this tutorial, the basics of electrical bioimpedance sensor design will be discussed. The electrode/electrolyte interface is thoroughly described, as well as methods for its modelling with equivalent circuits and computational tools. The design optimization and modelling of bipolar and tetrapolar bioimpedance sensors is presented in detail, based on the sensitivity theorem. Analytical and numerical modelling approaches for electric field simulations based on conformal mapping, point electrode approximations and the finite element method (FEM) are also elaborated. Finally, current trends on bioimpedance sensors are discussed followed by an overview of instrumentation methods for bioimpedance measurements, covering aspects of voltage signal excitations, current sources, voltage measurement front-end topologies and methods for computing the electrical impedance.

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Bioimpedance Sensors: A Tutorial

Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multifrequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3% for an extended lift-off range of up to 10 mm.

Evaluation of Coating Thickness Using Lift-Off Insensitivity of Eddy Current Sensor.

Impedance spectroscopy is a powerful measurement method having decisive advantages in several application fields. It allows the measurements of non-accessible quantities, supports high measurement accuracy, and enables simultaneous measurements of more than one quantity. The method provides deep insights in systems and materials, is non-invasive, and is highly efficient, considering measurement time, measurement procedures and possibilities for implementation in embedded systems for in-field measurements. In this contribution, we point out the potential, recent advances and future trends of the method to provide insight into the powerfulness of the method for future applications.

Impedance Spectroscopy: Applications, Advances and Future Trends

Section 2. The X2 - Distribution and the Incomplete Γ- Function: The Pearson Type III Curve; The Poisson Distribution; The Complete Γ- Function and the Factorial; The X2 - Test of Goodness of Fit

The reliable classification of coin sets from different countries and counterfeit coins is an essential means of securing cash circulation. Especially bimetallic coins, such as 2-Euro coins, are often subjected to counterfeit by mixing them with coins of other countries or by imitations. In this article, a real-time embedded sensor system is proposed, based on inductance spectroscopy to characterize and identify bimetallic coins having similar geometric properties and looking similar at a first view. The system is based on inductance spectroscopy varying the penetration depth of the magnetic field in the bimetallic structure of the coin, which generally contains buried layers of other metals. The experimental evaluation shows that the bimetallic coins from different countries can be identified and classified by the use of the support vector machine, a machine learning algorithm. Experimental results showed that the system reached a classification accuracy up to 100% within a response rate of 36.62 ms per five bimetallic coins.

Multifrequency Inductive Sensor System for Classification of Bimetallic Coins

Embedded impedance spectroscopy is gaining importance in the Internet of Things (IoT) era, where a new class of impedance spectroscopy devices is needed to fulfill a suitable frequency range, size, power consumption, and costs. In this paper, we investigate the feasibility of embedded impedance spectroscopy measurement systems with a focus on power-aware systems. We propose a novel embedded architecture based on a low-cost microcontroller and using a reduced number of electronic components. In order to enable the system for measuring higher frequencies up to 10 MHz, we use the undersampling technique on a relatively simple microcontroller with 12-bit in-built analog-to-digital converters and a sampling rate of 1 mega samples per seconds (MSPs). For the impedance calculation in the range of $100~\Omega -1 \text{M}\Omega $ , the Discrete Fourier Transform (DFT) is implemented directly on the same microcontroller. The experimental validation shows a maximal standard deviation of 0.5% in amplitude measurement and a standard deviation of 0.55° in phase measurement, which are sufficient for various applications of wearable solutions. The system design provides an outstanding performance considering low-cost, small foot print, and low-power consumption.

Embedded Wideband Measurement System for Fast Impedance Spectroscopy Using Undersampling

Modern production equipment is based on the results of quality control as well as process parameters. The magnetic anisotropy of materials is closely connected to internal mechanical stress by the Villari effect, and also to hardening effects due to plastic deformations, and could therefore provide an interesting basis for process control. Nevertheless, the analysis of anisotropic properties is extremely sensitive to sensor and workpiece misalignments, such as tilting. In this work, a novel eddy current sensor system is introduced, performing a non-contact measurement of the magnetic anisotropy of a workpiece and realizing a separation and correction of tilting effects. The measurement principle is demonstrated with the example of two samples with different magnetic anisotropy values induced by cold forming. Both samples are analyzed under different tilt angles between the sensor axis and the surface of the workpiece. In this work, digital signal processing is demonstrated on the acquired raw data in order to differentiate the effects of tilt and of anisotropy, with the use of preliminary results as an example of two prepared samples.

https://www.mdpi.com/1424-8220/21/8/2652/pdf

Eddy Current Sensor System for Tilting Independent In-Process Measurement of Magnetic Anisotropy.

A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A test dataset is synthesized using an analytical model and real measured impedance spectra by an eddy current sensor coil on EUR 2 and TRY 1 coins. The dataset has been extended by using a novel method based on interpolation technique to create datasets with different difficulty levels to replicate the scenario with a good imitation of EUR 2 coins and to investigate the limit of the prediction accuracy. It was observed that the compared frameworks have high accuracy performance for a lower level of difficulty in the dataset. As the difficulty in the dataset is raised, there was a drop in the accuracy of CNTK and Keras with TensorFlow depending upon the number of neurons in the hidden layers. It was observed that CNTK has the overall worst accuracy performance with an increase in the difficulty level of the datasets. Therefore, the major comparison was confined to Pytorch and TensorFlow. It was observed for Pytorch and TensorFlow with 32 and 64 neurons in hidden layers that there is a minor drop in the accuracy with an increase in the difficulty level of the dataset and was above 90% until both the coins were 80% closer to each other in terms of electrical and magnetic properties. However, Pytorch with 32 neurons in the hidden layer has a reduction in model size by 70% and 16.3% and predicts the class, 73.6% and 15.6% faster in comparison to TensorFlow and Pytorch with 64 neurons.

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Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins

Rohan Munjal

Papers

Embedded Wideband Measurement System for Fast Impedance Spectroscopy Using Undersampling

Impedance Spectroscopy: Applications, Advances and Future Trends

Multifrequency Inductive Sensor System for Classification of Bimetallic Coins

Eddy Current Sensor System for Tilting Independent In-Process Measurement of Magnetic Anisotropy.

Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins