Advancing microwave imaging algorithms and techniques
| dc.contributor.author | Bayat, Nozhan | |
| dc.contributor.examiningcommittee | Bridges, Gregory (Electrical and Computer Engineering) Lui, Shaun (Mathematics) Nikolova, Natalia (Electrical and Computer Engineering, McMaster University) | en_US |
| dc.contributor.supervisor | Mojabi, Puyan (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2020-01-03T17:39:53Z | |
| dc.date.available | 2020-01-03T17:39:53Z | |
| dc.date.issued | 2019-12-27 | en_US |
| dc.date.submitted | 2019-12-28T21:02:52Z | en |
| dc.date.submitted | 2020-01-02T15:42:35Z | en |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | This dissertation studies and develops novel techniques and algorithms in the area of microwave imaging (MWI). In MWI, the objective is to create a (quantitative) image of the dielectric profile of the object of interest (OI) in a non-destructive fashion. To this end, the OI is interrogated using non-ionizing and relatively low-power microwaves which are generated by some antennas. These incident microwaves will then interact with the OI, and consequently scattered electromagnetic fields will arise which contain information about the OI. These scattered fields will be collected, and then processed to extract their information so as to create the final image. This process is often referred to as the inversion of the scattered field data to reconstruct the OI’s dielectric profile. Currently, MWI faces some challenges that limit its capability to become a widely-accepted imaging tool. Three of these challenges are: (i) lack of fundamental understanding about the relation between themeasured scattered fields and the achievable image accuracy and resolution, (ii) insufficient image accuracy and resolution for some applications, and (iii) difficulty to collect sufficient measured data due to various practical limitations. The main focus of this dissertation is to investigate and develop techniques and algorithms in an attempt to address these three challenges. This dissertation begins by introducing the concept of ‘best’ possible reconstruction from given MWI configurations. This concept is important since if the best possible reconstruction fails to provide the features of interest, the actual blind reconstruction will not be able to provide these features either. To improve the achievable reconstruction, one option is to inject prior information into the algorithm. To this end, a fully automated inversion algorithm is presented that is able to incorporate prior spatial (structural) information about the OI. The proposed algorithm, which is capable of working with both complete and partially-available prior spatial information, is evaluated against synthetic and experimental data sets. A central part of MWI data collection, i.e., transmit and receive patterns of the antennas, is then considered. To this end, the use of focused near-field (NF) beams for illumination of ii the OI is first addressed. Using a NF plate and a Bessel beam launcher simulated in ANSYS HFSS, it will be shown that focused NF beams can suppress the effects of undesired regions under the Born approximation. Moreover, based on the relation between the electromagnetic inverse scattering and inverse source problems, it will be discussed how this focused approach can reduce the number of required measured data points. Then, the simultaneous use of focused transmit and receive patterns is considered to further suppress the sensitivity of the measured data with respect to undesired regions. In particular, using two NF plates (one for focused transmitting and the other for focused receiving), a single measured data point will be made mainly sensitive to a subwavelength cell within the imaging domain under some constraints and assumptions, namely, one-dimensional objects, limited working distance, and a localized approximation. This is different than typical MWI where one measured data point is sensitive to all the subwavelength cells within the imaging domain. | en_US |
| dc.description.note | February 2020 | en_US |
| dc.identifier.citation | Nozhan Bayat and Puyan Mojabi, “A Mathematical Framework to Analyze the Achievable Resolution from Microwave Tomography,” IEEE Transactions on Antenna and Propagation, vol. 64, no. 4, pp. 1484-1489 , 2016 | en_US |
| dc.identifier.citation | Nozhan Bayat and Puyan Mojabi, “On the Use of Focused Incident Near-Field Distributions in Microwave Imaging,” Sensors, vol. 18, no. 9, pp. 1-26, 2018. | en_US |
| dc.identifier.citation | fromNozhan Bayat and PuyanMojabi, “Incorporating Spatial Priors in Microwave Imaging via Multiplicative Regularization,” IEEE Transactions on Antennas and Propagation, pp. 1-12, 2019 (Early Access at the time of thesis submission). | en_US |
| dc.identifier.citation | In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of the University of Manitoba's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34439 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Microwave imaging, Microwave tomography, Imaging algorithms, | en_US |
| dc.title | Advancing microwave imaging algorithms and techniques | en_US |
| dc.type | doctoral thesis | en_US |