This paper presents one-dimensional and two-dimensional microwave inverse computing methods to detect an internal object using measurements based on a signal applied from the surface of the host material. The modelling of our application system has been aimed towards the in vivo detection of a breast tumour, in particular, and to enable the calculation of the tumour size and its distance from the surface of the breast. However, our approach is also applicable for more general foreign object identification. Complex backscattered electromagnetic waves characterise the relations of the internal properties of the host material. Forward and backscattered signals are used to calculate the impedance and reflection coefficients as a function of the applied microwave frequency. In the study of onedimensional modelling, we discuss the approach to identifying a foreign object hidden inside the host material and we present a method for computing the distance to the object from the surface of the host. Subsequently, a cylindrical coordinate system is used for two-dimensional modelling. A method to compute the size of the object (up to one millimetre in radius) is discussed. Computation of unknown electrical and non-electrical parameters using front-end microwave application is challenging but it is feasible.
This paper discusses a microwave application and the subsequent inverse computation method to detect a tumor inside the breast. The signal's incidence and scattering effect at the breast is modeled using a cylindrical coordinate system. Electrical properties of the normal and malignant tissues are the effective candidates for identifying the tumor inside the breast model. The algorithm calculates the tumor's size and its location using the forward and scattered waves. The scattering effect of the cylindrical wave functions at the presence of a cylinder is used to develop the forward equations in our model. In practical application, both forward and backward waves are obtained using reflection coefficient measurements. The measured data are then analyzed using the inverse algorithm to find the unknown object dimensions and location. As the measured data are subjected to inaccuracy due to the complexity of microwave scattering inside the breast, we test the accuracy of our inverse algorithm for a 10% error in the measured data and show that we can obtain a less than 0.5% error in the estimate of tumor distance.
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