A microparticle swarm optimizer for the reconstruction of microwave images

A novel optimization technique known as the microparticle swarm optimizer (μPSO) is proposed for high-dimensional microwave image reconstruction. With the proposed μPSO, good optimization performance can be obtained especially for solving high-dimensional optimization problems. In addition, the proposed μPSO requires only a small population size to outperform the standard PSO that uses a larger population size. Our simulation results on the reconstruction of the dielectric properties of normal and malignant breast tissues have shown that the μPSO can perform quite well for this high-dimensional microwave image reconstruction problem. © 2007 IEEE

Techniques for RF localization of wireless capsule endoscopy

© 2016 IEEE. Location estimation of a wireless capsule endoscope at 400 MHz MICS band is implemented here using both RSSI and TOA-based techniques and their performance investigated. To improve the RSSI-based location estimation, a maximum likelihood (ML) estimation method is employed. For the TOA-based localization, FDTD coupled with continuous wavelet transform (CWT) is used to estimate the time of arrival and localization is performed using multilateration. The performances of the proposed localization algorithms are evaluated using a computational heterogeneous biological tissue phantom in the 402MHz-405MHz MICS band. Our investigations reveal that the accuracy obtained by TOA based method is superior to RSSI based estimates. It has been observed that the ML method substantially improves the accuracy of the RSSI-based location estimation

Complex natural resonances for breast tissues with complex morphology

We investigate the Complex Natural Resonances (CNRs) of the malignant breast tissues with complex peripheries. The tissues are modeled as lossy dielectric objects with complex geometries which are expected to have different CNRs compared to those with smooth surfaces. Since the breast tissues are embedded inside inhomogeneous lossy breast medium, it is very important to ensure that a particular extracted CNR corresponds to the target tissue under consideration. Here we discuss techniques that help to ensure that the external mode of CNR of a dielectric target tissue corresponds to its surface geometry. We demonstrate using 3-D FDTD numerical models, that the extracted CNRs of malignant breast tissues vary for different morphologies. It is expected that this knowledge can potentially be useful to differentiate malignant breast tissues from benign and healthy ones. © 2011 IEEE

Nonorthogonal locally one dimensional FDTD method

We present unconditionally stable Non-orthogonal Locally One Dimensional (LOD) finite-difference time-domain (FDTD) method. The non-orthogonal formulation can be useful to extend the LOD-FDTD methods for curved discontinuities so as to obtain more accurate results with reduced computational resources. Formulations for CPML as well as scattered-field formulation are provided. Numerical simulations are used to demonstrate the accuracy and improvement of the proposed method over the conventional and non-orthogonal FDTD algorithms. © 2011 IEEE

Beamspace based time reversal processing for breast cancer detection

Here we propose novel beamspace processing techniques to enhance the existing time reversal imaging techniques and demonstrate their capability for the detection of early stage breast cancer. Performance of the proposed imaging technique for the detection of malignant lesions has been investigated using the FDTD breast model that contains dense breast tissues that have differing composition of fibroglandular tissues. The detection performance of the proposed method is also investigated when contrast in the dielectric properties between malignant, benign and dense fibroglandular breast tissues is very low. It is found that the proposed beamspace processing enhances the capability of time reversal MUSIC (TR MUSIC) and DORT (decomposition of the time reversal operator) algorithms in successfully detecting and localising the malignant lesions even in highly dense breast models. © 2012 IEEE

Time reversal microwave imaging for the localization and classification of early stage breast cancer

We use time reversal microwave imaging to detect and classify benign and malignant breast cancer lesions in numerical phantoms. We employed Time Reversal MUSIC (TR MUSIC) method to detect breast tumors in the presence of adipose and fibroglandular breast tissues. The classification of tumors is made based on the analysis of singular values of the multistatic matrix. We consider two different bands of frequencies 0.9-3 GHz and 3-10 GHz to show that in 0.9-3 GHz frequency band some distinct features for classification for breast tumors can be extracted. © 2011 Engineers Australia