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Electromagnetic methods have been widely used in the measurement of the water content of the soil. These methods utilise the permittivity as electrical properties of the soil, to determine the moisture content of the soil. Since the measurements are carried out indirectly, a calibration between permittivity and the water content of the soil is needed. Generally, the calibration method generated by using an empirical and mixing model. This study presents a new method of calibration by using a normalisation approach to calibrate the value of the permittivity of the water content of the soil. Secondary data was used to compare new calibration with other methods from previous studies. This calibration provides satisfactory results when compared to other methods.

Magnetic Induction Tomography (MIT) is a tomography method that utilizes magnetic field induced by coils excited with an electric current. A conductive object placed within the Region of Interest (ROI) will cause a perturbation in the magnetic field and cause a voltage difference measured in the sensor. These voltage data will be processed by implementing the Linear Back-Projection (LBP) algorithm to reconstruct the conductivity distribution image with a resolution of $32\text{x}32$ pixels. Simulation will be used as a method to obtain the sensor's characteristic in the form of a sensitivity matrix. Voltage measurements when a conductive object is placed within the ROI can also be acquired through simulation done with the COMSOL® Multiphysics® 4.4 software. The obtained data will be processed using the MATLAB R2013a software. Based on the results of the MIT sensor simulation, it is retrieved that although the LBP algorithm is a fast and simple method to implement in the MIT system, it can sense a conductive object placed in the ROI though it is still a very rough estimate to map conductivity distribution.

The research proposes a design of 12-channel rotary sensor for electrical capacitance volume tomography (ECVT) toward improvement in image reconstruction result. Sensor was assembled by attaching motors at upper section to rotate in radial; also at side to move up and down. Effects of rotating and translating the sensor has been analyzed in comparison to conventional sensory system. Correlation coefficient (CC) method and linear back-projection (LBP) were used to observe the performance on rotation-translation. Experiments show a difference between rotation with CC of 0.76 and conventional with CC of 0.64. In other words, image quality is improved by 17%.

This paper presents method to implement Linear Back Projection (LBP) algorithm in Field Programmable Gate Arrays (FPGA). Top-down approach has been adopted for the design of the hardware of LBP algorithm. The FPGA used is Xilinx Spartan 3A and the language used to design the hardware is VHSIC Hardware Description Language (VHDL). The final design is able to reconstruct a 32×32 pixel image from 8-electrode Electrical Capacitance Tomography (ECT) with speed of 23809 slice images per second and the image is shown on LCD. It could be further extended to form quasi 3D image with 32 slices at rate 744 frame-per-second.

In Electrical Capacitance Volume Tomography, the internal permittivity distribution of a region of interest has a nonlinear relationship with the measured capacitance. Most image reconstruction algorithms neglects the nonlinear characteristic and use instead a linearized sensitivity approach to solve the non-linear problem, affecting the accuracy of the reconstructed image. In this study, we used feed-forward neural network to solve the non-linear forward problem to replace the linearized sensitivity matrix. The reconstruction process uses an iterative linear back projection technique. Comparison results showed considerable improvement on the image reconstruction of the proposed technique.