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An Introduction To The Event Related Potential Technique

This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Introduction to the Finite Element Method

Computational and Mathematical Methods in Medicine

Wearable sensors have recently seen a large increase in both research and commercialization. However, success in wearable sensors has been a mix of both progress and setbacks. Most of commercial progress has been in smart adaptation of existing mechanical, electrical and optical methods of measuring the body. This adaptation has involved innovations in how to miniaturize sensing technologies, how to make them conformal and flexible, and in the development of companion software that increases the value of the measured data. However, chemical sensing modalities have experienced greater challenges in commercial adoption, especially for non-invasive chemical sensors. There have also been significant challenges in making significant fundamental improvements to existing mechanical, electrical, and optical sensing modalities, especially in improving their specificity of detection. Many of these challenges can be understood by appreciating the body's surface (skin) as more of an information barrier than as an information source. With a deeper understanding of the fundamental challenges faced for wearable sensors and of the state-of-the-art for wearable sensor technology, the roadmap becomes clearer for creating the next generation of innovations and breakthroughs.

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Wearable sensors: modalities, challenges, and prospects

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The Essential Physics Of Medical Imaging

Medical imaging techniques have evolved to expand our ability to visualize new contrast information of electrical, optical, and mechanical properties of tissues in the human body using noninvasive measurement methods. In particular, electrical tissue property imaging techniques have received considerable attention for the last few decades since electrical properties of biological tissues and organs change with their physiological functions and pathological states. We can express the electrical tissue properties as the frequency-dependent admittivity, which can be measured in a macroscopic scale by assessing the relation between the timeharmonic electric field and current density. The main issue is to reconstruct spectroscopic admittivity images from 10 Hz to 1 MHz, for example, with reasonably high spatial and temporal resolutions. It requires a solution of a nonlinear inverse problem involving Maxwell’s equations. To solve the inverse problem with practical significance, we need deep knowledge on its mathematical formulation of underlying physical phenomena, implementation of image reconstruction algorithms, and practical limitations associated with the measurement sensitivity, specificity, noise, and data acquisition time. This paper discusses a number of issues in electrical tissue property imaging modalities and their future directions.

Spectroscopic admittivity imaging of biological tissues

Studies on the different coil geometries and core materials for magnetic induction spectroscopy (MIS) system development

Estimation of parameters for an induction machine is essential in performance analysis and control scheme design in industrial applications. In this paper, elephant herding optimization (EHO) technique-based parameter estimation technique for an induction motor is studied, and the optimum parameters are obtained using a least mean square technique (LMST). The input impedance is studied at different slip samples and a steady-state model of the squirrel-cage induction machine is developed by incorporating the nonlinear core-loss resistance. The real machine parameters are obtained from the practical experimentation on a squirrel cage induction machine. Real machine parameters are fed to the optimization algorithm as the initial values. By considering the nonlinear core-loss parameter in the equivalent circuit model, the proposed method suggests a more accurate parameter estimation technique. The effectiveness of the proposed EHO-based induction machine parameters optimization techniques is validated by the experimental and simulation results.

Elephant Herding Optimization (EHO) Based Parameters Estimation of Induction Machine Considering the Nonlinear Core-Loss Model

Automatic multifrequency signal generation is essential for a number of practical biomedical applications such as electrical impedance spectroscopy (EIS) and multifrequency electrical impedance tomography (mfEIT). In EIS or mfEIT, the voltage signal is used to apply an electrical signal to the object under study either converting it into a current signal or even applied as a voltage signal itself. The frequency of the signal generated by the function generator or oscillator circuit depends on the values of the electronic components used or the components connected to the function generator IC used. In this paper, a LabVIEW-based resistor–capacitor bank controller (RCBC) has been developed for automatic multifrequency signal generation with a voltage-controlled oscillator (VCO) circuit. The multifrequency signal generation of the VCO is controlled by a resistor–capacitor bank (RCB) connected with a multiplexer board. The RCB is developed with high precision passive components which are switched through the multiplexer board controlled by a LabVIEW-based graphical user interface (GUI). The RCBC is tested for a voltage controlled oscillator (VCO) circuit and found efficiently sweeping the VCO output frequency suitable for EIS and mfEIT applications.

A LabVIEW-Based Resistor–Capacitor Bank Controller for Automatic Signal Generation with a Multifrequency Voltage-Controlled Oscillator (VCO)

Acoustoelectric (AE) imaging is a new technique for mapping current densities in the heart and brain at a high resolution determined by the size of the ultrasound (US) focus. Because the amplitude of the AE interaction signal in biological tissue is weak (on order of 1 μV), detection of small currents is challenging with poor signal-to-noise ratio (SNR). Because optimal detection depends on minimizing background noise, the design and performance of the recording system, especially amplifiers and filters, is crucial for efficient and sensitive AE imaging. The amplitude and bandwidth of the AE signal depends on a variety of factors, including the US bandwidth and beam pattern, distribution of current densities, properties of the recording electrodes, and amount of averaging/sampling. The primary goal of this work was to optimize the design and performance of the acoustoelectric differential amplifier, including signal processing circuits, to minimize noise and maximize sensitivity for detection of the AE signal. Variable gain, band-pass filter (BPF) cutoffs, and bandwidth are critical design parameters for optimizing the AE imaging platform for mapping weak electric currents in tissue.

Tushar Kanti Bera

Papers

Spectroscopic admittivity imaging of biological tissues

Studies on the different coil geometries and core materials for magnetic induction spectroscopy (MIS) system development

Elephant Herding Optimization (EHO) Based Parameters Estimation of Induction Machine Considering the Nonlinear Core-Loss Model

A LabVIEW-Based Resistor–Capacitor Bank Controller for Automatic Signal Generation with a Multifrequency Voltage-Controlled Oscillator (VCO)

Design considerations and performance of a variable gain, variable bandwidth signal processing circuit for acoustoelectric imaging