Estimate the Location of a Breast Tumor using the Received Signal Angle

الموضوعات : Majlesi Journal of Telecommunication Devices

javad nouri pour ¹ (Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mohammad Ali Pourmina ² (Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mohamad Naser Moghaddasi ³ (Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
behbod Ghalamkari ⁴ (Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)

تاريخ الإرسال : 09 الخميس , ربيع الثاني, 1444 تاريخ التأكيد : 21 السبت , جمادى الثانية, 1444 تاريخ الإصدار : 09 الأربعاء , شعبان, 1444

الکلمات المفتاحية: Maximum Probability, breast tumor, Signal angle deviation, AOA, Tumor,

ملخص المقالة :

In this paper, a new method for estimating the tumor's location is presented; the technique is based on the arrangement of sensors and received wave changes. A new method has been developed for locating the return signal from the tumor. The main idea is that the location of the tumor is randomly assumed. And then estimates the location of the tumor. It presents a phase event. Various articles have used a combination of these methods to shape different issues. However, these methods have not been used to estimate tumor location. In this article, two types of wavelengths are used; the transmitter node first sends a signal with a specific frequency, it is assumed that the reflection is almost complete. Therefore, according to the reviews of the tumor signal, we find that the tumor is in the region of intensification, i.e., the dimensions of the tumor are in the order of the transmitted wavelength. Then, by changing the frequency, the signal passes through the tumor. The signal passing through the tumor (according to "Snell's" law) fails; that is, the received signal is received with angular deviation and delay. An estimator is suggested by the maximum likelihood maximum (MLE). The estimators have a nonlinear form. We use the SD reduction slope algorithm to LS linear search algorithm for optimizations.

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