scholarly journals Exploratory Designing a Magnetic Induction Tomography Sensor Coil Circuit for Agarwood

2021 ◽  
Vol 10 (3) ◽  
pp. 163-168
Author(s):  
Nurfarahin Ishak ◽  
Chua King Lee ◽  
Siti Zarina Mohd Muji ◽  
Abdul Azlin Bin Abdul Latip
Keyword(s):  
Magnetic Induction ◽  
Imaging Modality ◽  
Input Voltage ◽  
The Body ◽  
Electromagnetic Properties ◽  
Signal Frequency ◽  
Induced Voltage ◽  
Circuit Input ◽  
Magnetic Induction Tomography ◽  
Voltage Feedback

Magnetic induction tomography (MIT) is an imaging modality focused on tracing the transmission of electrical conductivity within the body. This technique used to image electromagnetic properties of an object by using the eddy current effect. This paper explains the primary analog transceiver circuit of MIT. This is a surrogate design of the analog system in the electronic components for pattern recognition and conditioning. This MIT system operating with a single excitation signal frequency at 10MHz. The input voltage received by the receiver sensor would become the circuit input which contained information. The four stages process in the receiver circuit successfully captured the signal from the transmitter. These subsystems have their functions and can be put into effect in many ways. Therefore, the circuit was used to be reliable at agarwood samples. The approach transceiver circuit were successful and functional for MIT coil sensing. The input voltage feedback depending on the conductivity of the samples. As the dielectric properties of samples are high, the input voltage at the receiver also high. Therefore, 10MHz can use for agriculture while this range of frequency is usually used in biomedical applications. Series – parallel circuit gives a greater induction factor and therefore more induced voltage for the load of the receiver.

scholarly journals A Study of Magnetic Induction Tomography (MIT) for Calcium Oxalate Renal Screening

2021 ◽  
Vol 17 (4) ◽  
pp. 485-494
Author(s):  
Thompson Paulus ◽  
Nur Amira Zulkiflli ◽  
Fatin Aliah Phang Abdullah ◽  
Azli Yahya ◽  
Siti Zarina Abdul Muji ◽  
...  
Keyword(s):  
Calcium Oxalate ◽  
Magnetic Induction ◽  
Imaging Modality ◽  
Stone Formation ◽  
Imaging Techniques ◽  
Electromagnetic Properties ◽  
Sensor Performance ◽  
Magnetic Induction Tomography ◽  
Set Up ◽  
Renal Screening

Nephrolithiasis is a process of stone formation in the kidney by crystallization. The increasing prevalence of nephrolithiasis from time to time had sought an alternative from the conventional imaging techniques that is invasive, radiative, and non-rapid usage. This paper enclosed a design simulation study of Magnetic Induction Tomography (MIT) system using COMSOL Multiphysics for renal imaging. MIT is a soft field tomography and non-contact imaging modality which can project the passive electromagnetic properties (conductivity, permittivity and permeability) under the principle of electromagnetic induction. In this research also, 8 copper trans-receiver coils were employed in the MIT system and fixed by the insulation belt. Meanwhile, geometric set-up of renal organ was set to imitate the transverse section of human renal. In the methodology, sensor performance analyses were done using frequency ranging from 50 kHz to 2 MHz of the MIT system on radii of calcium oxalate in renal. The sensor response and pattern is discussed in this paper.

Simulation Analysis of the Sensitivity in Magnetic Induction Tomography Based on Perturbation Theory

2013 ◽  
Vol 749 ◽  
pp. 371-376
Author(s):  
Yang Xuan ◽  
Xu Wang ◽  
Cheng An Liu ◽  
Dan Yang
Keyword(s):  
Perturbation Theory ◽  
Salt Solution ◽  
Magnetic Induction ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Target Object ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Projection Reconstruction ◽  
Magnetic Induction Tomography

Magnetic induction tomography (MIT) is a noninvasive and contactless imaging modality which aims at the reconstruction of the electrical conductivity in objects from alternating magnetic fields. Filtered back projection reconstruction algorithm is widely used in biomedical imaging field, and tried to use in MIT. Finite element analysis model has been established based on Scharfetter coil-coil model and perturbation theory, then simulated coaxial coil system by ANSYS software, the perturbation aroused by a target object moving on vertical coil axis. The sensitivity of a target object moves in vacuum and a salt solution were calculated respectively, the characteristics of the perturbation sensitivity in a salt solution were analyzed. The conditions of filtered back projection reconstruction algorithm in MIT were discussed.

scholarly journals A Simulation Magnetic Induction Tomography (MIT) for Agarwood using COMSOL Multiphysics

2021 ◽  
Vol 10 (3) ◽  
pp. 67-71
Author(s):  
Nurfarahin Ishak ◽  
Chua King Lee ◽  
Siti Zarina Mohd Muji
Keyword(s):  
Magnetic Field ◽  
Magnetic Induction ◽  
Electromagnetic Properties ◽  
Induced Current ◽  
Solenoid Coil ◽  
Agriculture Industry ◽  
Eddy Current Effect ◽  
Coil Diameter ◽  
Great Performance ◽  
Magnetic Induction Tomography

Magnetic induction tomography is an imaging technique used to image electromagnetic properties of an object by using the eddy current effect. (MIT) is a non-destructive method that greatly is used in the agriculture industry. This method provided an opportunity to improve the quality of agricultural products. MIT simulation was used for agarwood existence detection. This paper presented for the simulation system contains 7 channel coils receiver and a channel transmitter which is a sensing detector. This experiment aims to demonstrate the reaction of induced current density and magnetic field at 10 MHz frequency. Then, it also determines the optimal solenoid coil to be used for a better outcome for the magnetic induction system. The simulation result shows that coil diameter, coil length, and coil layer have a crucial role in the great performance of the induced current and magnetic field. The more coil turns, the greater the strength of the permanent magnetic field around the solenoid coil. The result of the simulation is important and needs to be considered to verify the effectiveness of the system for developing the magnetic induction circuit design.

scholarly journals Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach

2021 ◽  
Author(s):  
Imamul Muttakin ◽  
Manuchehr Soleimani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Liquid Metal ◽  
Magnetic Induction ◽  
Phase Distribution ◽  
Submerged Entry Nozzle ◽  
Test Accuracy ◽  
Induced Voltage ◽  
Static Test ◽  
Magnetic Induction Tomography

Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or complex computation degrading its real-time capability. Additionally, in some cases, the actual image may not be essential whereas the void fraction or porosity needs to be estimated. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to approximately 86% accuracy on this dataset. CNN classification using multi-dimensional data with more classes produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

scholarly journals Three-dimensional Magnetic Induction Tomography: Improved Performance for the Center Regions inside a Low Conductive and Voluminous Body

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1306
Author(s):  
Martin Klein ◽  
Daniel Erni ◽  
Dirk Rueter
Keyword(s):  
Magnetic Induction ◽  
Three Dimensional ◽  
Central Area ◽  
Electromagnetic Properties ◽  
Interior Region ◽  
Noise Floor ◽  
Sensitivity Map ◽  
Improved Performance ◽  
Magnetic Induction Tomography ◽  
Conductive Volume

Magnetic induction tomography (MIT) is a contactless technique that is used to image the distribution of passive electromagnetic properties inside a voluminous body. However, the central area sensitivity (CAS) of this method is critically weak and blurred for a low conductive volume. This article analyzes this challenging issue, which inhibits even faint imaging of the central interior region of a body, and it suggests a remedy. The problem is expounded via two-dimensional (2D) and three-dimensional (3D) eddy current simulations with different transmitter geometries. On this basis, it is shown that a spatially undulating exciter coil can significantly improve the CAS by >20 dB. Consequently, the central region inside a low conductive voluminous object becomes clearly detectable above the noise floor, a fact which is also confirmed by practical measurements. The improved sensitivity map of the new arrangement is compared with maps of more typical circular MIT geometries. In conclusion, 3D MIT reconstructions are presented, and for the same incidence of noise, their performance is much better with the suggested improvement than that with a circular setup.

scholarly journals Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach

2021 ◽  
Author(s):  
Imamul Muttakin ◽  
Manuchehr Soleimani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Liquid Metal ◽  
Magnetic Induction ◽  
Phase Distribution ◽  
Submerged Entry Nozzle ◽  
Test Accuracy ◽  
Induced Voltage ◽  
Static Test ◽  
Magnetic Induction Tomography

Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or complex computation degrading its real-time capability. Additionally, in some cases, the actual image may not be essential whereas the void fraction or porosity needs to be estimated. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to approximately 86% accuracy on this dataset. CNN classification using multi-dimensional data with more classes produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

Inspection of defects in CFRP by improved magnetic induction tomography

2019 ◽  
Vol 61 (3) ◽  
pp. 255-259
Author(s):  
Lipan Zhang ◽  
Qifeng Meng ◽  
Kai Song ◽  
Ming Gao ◽  
Zhiyuan Cheng
Keyword(s):  
Magnetic Induction ◽  
Magnetic Induction Tomography

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

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