Effects of Dipole Model for Magnetic Induction on Biomedical Devices

Department of Mechanical EngineeringMagnetic field has been utilized in the biomedical applications for numerous decades, owing to contactless, noninvasive and harmless property, low cost and robustness in operation, and increased safety compared to other radiative fields. The medical applications using magnetic field have been investigated to enhance their performance. Such applications require accurate analysis of the magnetic field for improvement. However, it is difficult to compute the time-varying magnetic field, due to nonlinearity and interactions by various electromagnetic properties. The problems take a lot of computational time for analysis and cause the ill-posed condition. In this dissertation, the magnetic field is analyzed to develop the medical applications, using the extended distributed multi-pole (eDMP) method. The method utilizes the magnetic dipole moments to solve not only nonlinearity but also interactions and improve ill-posed condition. Based on the modeling method, the magnetic field can be controlled. Then the control method contributes to construct the magnetic field which enhance the performance of the applications. The methods are illustrated in two applications with experiments: a navigation sensor for an intubation tube and magnetic induction tomography (MIT). The navigation sensor for a tube is proposed to prevent a potential danger of perforation during tube intubation. A trajectory of the tube is reconstructed, based on the magnetic induction. The eDMP method is used to modeling the system and the optimal design is found, considering the practical usage. A MIT is a medical imaging device mapping conductivity of target objects. It shows inferior performance due to low signal-to-noise ratio and ill-posed condition. The eDMP method is applied to analyze the magnetic field of the MIT system and implement the system to reconstruct an image. The various materials with different conductivities are applied and their properties, such as the shape and conductivity, are characterized. Eventually, it is expected that the MIT system makes image of the biological cell and could be developed as the medical device.clos

A Portable Phase-Domain Magnetic Induction Tomography Transceiver with Phase-Band Auto-Tracking and Frequency-Sweep Capabilities

This paper presents a portable magnetic induction tomography (MIT) transceiver integrated circuit to miniaturize conventional equipment-based MIT systems. The miniaturized MIT function is enabled through single-chip transceiver implementation. The proposed MIT transceiver utilizes a phase-locked loop (PLL) for frequency sweeping and a phase-domain sigma-delta modulator with phase-band auto-tracking for a full-range fine-phase resolution. The designed transceiver is fabricated and verified to achieve the measured signal to noise and distortion ratio (SNDR) of 101.7 dB. Its system-level prototype including in-house magnetic sensor coils is manufactured and functionally verified for four different material types

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Department of Electrical EngineeringA Sensor system is advanced along sensor technologies are developed. The performance improvement of sensor system can be expected by using the internet of things (IoT) communication technology and artificial neural network (ANN) for data processing and computation. Sensors or systems exchanged the data through this wireless connectivity, and various systems and applications are possible to implement by utilizing the advanced technologies. And the collected data is computed using by the ANN and the efficiency of system can be also improved. Gas monitoring system is widely need from the daily life to hazardous workplace. Harmful gas can cause a respiratory disease and some gas include cancer-causing component. Even though it may cause dangerous situation due to explosion. There are various kinds of hazardous gas and its characteristics that effect on human body are different each gas. The optimal design of gas monitoring system is necessary due to each gas has different criteria such as the permissible concentration and exposure time. Therefore, in this thesis, conventional sensor system configuration, operation, and limitation are described and gas monitoring system with wireless connectivity and neural network is proposed to improve the overall efficiency. As I already mentioned above, dangerous concentration and permissible exposure time are different depending on gas types. During the gas monitoring, gas concentration is lower than a permissible level in most of case. Thus, the gas monitoring is enough with low resolution for saving the power consumption in this situation. When detecting the gas, the high-resolution is required for the accurate concentration detecting. If the gas type is varied in the above situation, the amount of calculation increases exponentially. Therefore, in the conventional systems, target specifications are decided by the highest requirement in the whole situation, and it occurs increasing the cost and complexity of readout integrated circuit (ROIC) and system. In order to optimize the specification, the ANN and adaptive ROIC are utilized to compute the complex situation and huge data processing. Thus, gas monitoring system with learning-based algorithm is proposed to improve its efficiency. In order to optimize the operation depending on situation, dual-mode ROIC that monitoring mode and precision mode is implemented. If the present gas concentration is decided to safe, monitoring mode is operated with minimal detecting accuracy for saving the power consumption. The precision mode is switched when the high-resolution or hazardous situation are detected. The additional calibration circuits are necessary for the high-resolution implementation, and it has more power consumption and design complexity. A high-resolution Analog-to-digital converter (ADC) is kind of challenges to design with efficiency way. Therefore, in order to reduce the effective resolution of ADC and power consumption, zooming correlated double sampling (CDS) circuit and prediction successive approximation register (SAR) ADC are proposed for performance optimization into precision mode. A Microelectromechanical systems (MEMS) based gas sensor has high-integration and high sensitivity, but the calibration is needed to improve its low selectivity. Conventionally, principle component analysis (PCA) is used to classify the gas types, but this method has lower accuracy in some case and hard to verify in real-time. Alternatively, ANN is powerful algorithm to accurate sensing through collecting the data and training procedure and it can be verified the gas type and concentration in real-time. ROIC was fabricated in complementary metal-oxide-semiconductor (CMOS) 180-nm process and then the efficiency of the system with adaptive ROIC and ANN algorithm was experimentally verified into gas monitoring system prototype. Also, Bluetooth supports wireless connectivity to PC and mobile and pattern recognition and prediction code for SAR ADC is performed in MATLAB. Real-time gas information is monitored by Android-based application in smartphone. The dual-mode operation, optimization of performance and prediction code are adjusted with microcontroller unit (MCU). Monitoring mode is improved by x2.6 of figure-of-merits (FoM) that compared with previous resistive interface.clos