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ÖgeMicrowave imaging of breast cancer with contrast agents(Fen Bilimleri Enstitüsü, 2020)The prevalence of breast cancer is seen in both sexes worldwide, however, it is the second type of cancer, which is diagnosed especially in women and results with a fatal outcome among cancer types. Breast cancer like other types of cancer manifests itself by changing breast cells for various reasons, firstly spreading to the surrounding cells and then to the tissues in the body. The spread of other cells under the influence of changing breast cells causes malignant tumors to appear. Hence, it is great of importance to control or stop the spread of from breast cells to other cells in the body. For this purpose, early-stage diagnosis studies for breast cancer have been a working area in which researchers showed great interest. In other words, early diagnosis techniques play a key role in determining the course of the disease. In this point, mammography technologies containing ionizing x-ray radiation are most frequently used for the early-stage breast cancer detection. The usage of x-ray radiation can damage to the body tissues. These technologies, which contain x-rays that can ionize tissues, bring along various limitations during measurement due to the prevalence of the disease especially in elderly people. The disadvantages of mammography techniques exhibit the need for new and alternative imaging methods. In recent years, many researchers have worked on breast imaging systems to handle the particular disadvantages of mammography technologies. In this context, the non-ionizing microwave imaging (MWI) method, reducing the risk of patient health, is seen as a more reliable and alternative imaging technology for the early-stage breast cancer detection. The breast imaging with MWI methods is based on the reconstruction of the dielectric permittivity profile of cancerous and healthy breast tissues. After completed the breast reconstruction, the cancerous tissues are diagnosed due to illustrate a higher dielectric permittivity profile than other healthy tissues in the breast. The purpose of these methods is to diagnose and display any cases other than normal that occur in the body. The most important feature of MWI methods is to carry a low-level risk of harm to the patient because of using non-ionizing electromagnetic waves. In addition to this, it can be used in ambulances and many other emergency points for early diagnosis. Notwithstanding all these advantages, the clinical acceptance of MWI methods for breast cancer has not been realized yet. Hence, MWI methods need to improve with new approaches to receive clinical acceptance. The process of imaging or reconstructing the electromagnetic properties of tissues is based on the inverse scattering theory, and there are several quantitative and qualitative imaging methods developed in this area. Benefit from these methods, the positions, shapes, and electromagnetic properties of diseased tissues in the body are obtained in two or three dimensions. The qualitative imaging methods generally give information about the positions and shapes of diseased tissues. The scattered fields from tumors are higher than other normal tissues allows the qualitative imaging methods to be easily applied. While the quantitative imaging methods provide information about the geometric shapes and positions of tumors, as well as numerical information about the dielectric properties. The most widely used the quantitative imaging methods available in the literature are contrast source inversion (CSI) and Newton-based methods. Within the scope of this thesis, all studies have been realized mainly on the solution methods of the inverse scattering problems. In other words, the alternative methods based on enhanced contrast agents are recommended for scatterer objects with different relative dielectric permittivity and magnetic permeability constants, especially for the microwave imaging methods used in breast cancer. The main purpose of these studies is to eliminate the resolution limitation of microwave imaging methods by using dielectric and magnetic contrast agents and to enable the proposed method to be used more efficiently for the diagnose of early-stage breast cancer. All studies can be categorized under three main headings: i) the reconstruction of dielectric permittivity profile based on quasi-Newton method ii) the enhanced-dielectric contrast agents based on the quasi-Newton method for microwave imaging, ii) the enhanced-magnetic contrast agents based on factorization method for microwave imaging. are higher than other normal tissues allows the qualitative imaging methods to be easily applied. While the quantitative imaging methods provide information about the geometric shapes and positions of tumors, as well as numerical information about the dielectric properties. The most widely used the quantitative imaging methods available in the literature are contrast source inversion (CSI) and Newton-based methods. Within the scope of this thesis, all studies have been realized mainly on the solution methods of the inverse scattering problems. In other words, the alternative methods based on enhanced contrast agents are recommended for scatterer objects with different relative dielectric permittivity and magnetic permeability constants, especially for the microwave imaging methods used in breast cancer. The main purpose of these studies is to eliminate the resolution limitation of microwave imaging methods by using dielectric and magnetic contrast agents and to enable the proposed method to be used more efficiently for the diagnose of early-stage breast cancer. All studies can be categorized under three main headings: i) the reconstruction of dielectric permittivity profile based on quasi-Newton method ii) the enhanced-dielectric contrast agents based on the quasi-Newton method for microwave imaging, ii) the enhanced-magnetic contrast agents based on factorization method for microwave imaging. In the second part of the thesis, all studies on the use of dielectric contrast agents for breast cancer have been conducted. Unlike the first part here, the QN-CSI method by using the dielectric contrast agents is explored in more different and realistic cases. Firstly, a canonical breast model is designed using the HFSS electromagnetic 3-D simulation program. The designed breast model consists of structures had different dielectric permittivity and conductivity values, which are lined up from the outermost muscle, skin, glandular breast tissue, and tumor respectively. Firstly, the breast model is simulated to generate the forward scattering solution for two different cases, which are the presence and absence of dielectric contrast agents. After that, the scattered fields from the breast model are collected from different illumination angles and the field difference is calculated by using these two simulation results. The proposed QN-CSI method realizes the inverse scattering solution with this difference dataset and in this manner, the tumorous tissues placed in the breast model are imaged. Within the scope of this section, the image acquisition skill of the proposed method for different status parameters is examined. In this context, all simulations are operated at f = 2GHz, and two spherical tumors with radius of 1cm are located at (x1, y1,z1) = (3cm,4cm,−2cm) and (x2, y2,z2) = (3cm,4cm,2cm). The dielectric permittivity and conductivity constants of the placed tumors are selected as εr = 43 and σ = 1S/m. Later, the values of these dielectric constants are increased up to 37% under the assumption of using contrast agents at the second simulation. After completed these two simulations, the difference dataset is calculated and the tumors are imaged by the QN-CSI method. The fact notwithstanding that this image is not an anatomical breast model, it contains the expected tumorous tissues. At the end of this section, several simulations according to different status parameters are performed by changing the number of plane waves, the radiuses of tumors, and the conductivities of tumors In this chapter, all numerical results are given by comparison with the CSI method. In the third and last part of the thesis, the magnetic contrast agents are carried out instead of the dielectric contrast agents. Similar to the second part, but here, the changing of magnetic permeability constant (µr) depended on the frequency and the effect of an externally applied magnetic field are taken into account. Namely, magnetic nanoparticles(MNPs) are employed for the magnetic contrast enhancement. The different types of MNPs such as superparamagnetic iron oxide nanoparticles (SPIONs), which are most often preferred, are used to detect abnormality in several medical imaging methods. Since the biocompatible SPIONs are functionalized by the effect of the polarized magnetic field externally applied from the outside. While normal tissue cells do not react to the external magnetic fields, the magnetic nanoparticles nonlinearly behave depending on the value of the applied magnetic field. As a result of this, the changing of magnetic contrast is displayed. In this thesis, UWCEM numerical breast phantom repository, which includes several realistic experimental breast models, is used as a database to investigate the enhanced magnetic contrast agents. The phantom repository consists of numerical breast phantoms produced from anatomically realistic magnetic resonance imaging (MRI) for breast cancer detection and treatment applications. Here, the third phantom in ACR class 3, which contains heterogeneously dense fibroglandular breast phantom is selected and embedded into a 3D electromagnetic simulation program adding two tumors with a diameter of 0.5cm located into the model. The relative dielectric permittivity εr = 4 is chosen for the background medium. In the following step, the simulations are performed for two different cases over the 1.91GHz − 2.04GHz frequency band with N = 18 dipole antennas. After both simulations are completed for the given frequency range, the difference between the multi-static response matrices included reflection coefficients are calculated and the inverse problem solution is realized. In this part of the thesis, the factorization method is chosen to reconstruct the breast model. Eventually, the locations and shapes of the tumors are easily determined by the factorization method based on SPIONs contrast agents.
ÖgeAnalytical models and cross-layer delay optimization for resource allocation of noma downlink systems( 2020)5G is introduced by 3rd Generation Partnership Project (3GPP) to satisfy the stringent delay and reliability requirements of 5G services such as industrial automation, augmented and virtual reality, and intelligent transportation. Non-orthogonal multiple access (NOMA) is one of the promising technologies for low latency services of 5G, where the system capacity can be increased by allowing simultaneous transmission of multiple users at the same radio resource. The resource allocation in NOMA systems including user scheduling and power allocation determine the mapping of users to radio resource blocks and the transmission power levels of users at each resource block, respectively. In this thesis, we first propose a genetic algorithm (GA) based multi-user radio resource allocation scheme for NOMA downlink systems. In our set-up, GA is used to determine the user groups to simultaneously transmit their signals at the same time and frequency resource while the optimal transmission power level is assigned to each user to maximize the geometric mean of user throughputs. The simulation results show that the GA based approach is a powerful heuristic to quickly converge to the target solution which balances the trade-off between total system throughput and fairness among users. The most of the resource allocation studies for NOMA systems including our GA based approach assumes full buffer traffic model where the incoming traffic of each user is infinite while the traffic in real life scenarios is generally non-full buffer. As the second contribution, we propose User Demand Based Proportional Fairness (UDB-PF) and Proportional User Satisfaction Fairness (PUSF) algorithms for resource allocation in NOMA downlink systems when traffic demands of the users are rate limited and time-varying. UDB-PF extends the PF based scheduling by allocating optimum power levels towards satisfying the traffic demand constraints of user pair in each resource block. The objective of PUSF is to maximize the network-wide user satisfaction by allocating sufficient frequency and power resources according to traffic demands of the users. In both cases, user groups are selected first to simultaneously transmit their signals at the same frequency resource while the optimal transmission power level is assigned to each user to optimize the underlying objective function. In addition, the GA is employed for user group selection to reduce the computational complexity. When the user traffic rate requirements change rapidly over time, UDB-PF yields better sum-rate (throughput) while PUSF provides better network-wide user satisfaction results compared to the PF based user scheduling. We also observed that the GA based user group selection significantly reduced the computational load while achieving the comparable results of the exhaustive search. The low latency objectives of URLLC services such as industrial control and automation, augmented and virtual reality, tactile Internet and intelligent transportation requires delay analysis which cannot be possible using the rate limited traffic demands. The packet based traffic model with random inter-arrival times and packet sizes have to be utilized. New analytical models using packet based traffic model with random inter-arrival times and packet sizes are of paramount importance to develop high performance resource allocation strategies satisfying the challenging latency requirements of 5G services. As the third contribution, we propose an analytical model to characterize the average queuing delay for NOMA downlink systems by utilizing a discrete time M/G/1 queuing model under a Rayleigh fading channel. The packet arrival process is assumed to be Poisson distributed while the departure process depends on network settings and resource allocation. The average queuing delay results of the analytical model are validated through Monte Carlo simulation experiments. One of the main results is that the ergodic capacity region of NOMA is a superset of OMA indicating that the NOMA can support higher service rate and lower latency using the same resources such as transmission power and bandwidth. Furthermore, the proposed analytical model is applied for the performance evaluation of the 5G NR concept when the NOMA is utilized. The model accurately predicts that the average queuing delay decreases when wider bandwidth and shorter time slot duration are employed in 5G NR. The outage probability becomes an important metric that should be minimized to address the reliability aspect of the URLLC services. We utilize the common outage condition such that the user fails either decoding its own signal or performing SIC for the signals of other users at the receiver when the SINR is lower than a predefined outage threshold. As the fourth contribution, the optimum power allocation for a single resource block that minimizes the system outage probability under Rayleigh fading channel, where a common signal to interference plus noise ratio (SINR) level is utilized as an outage condition, is provided as a closed form expression. The accuracy of the proposed optimum power allocation model is validated by the Monte Carlo simulations. The numerical results show that the outage probability of OMA with the fractional power allocation is lower than NOMA with the optimum power allocation. The results indicate that the trade-off between the outage and spectral efficiency in NOMA should be carefully controlled to meet higher throughput and lower latency objectives of 5G. The last contribution considers the reliability and latency aspects jointly such that the discrete time M/G/1 queuing model of a NOMA downlink system is extended by taking the outage condition into account. The departure process of the queuing model is characterized by obtaining the first and second moment statistics of the service time that depends on the resource allocation strategy and the packet size distribution. The proposed model is utilized to obtain the optimum power allocation that minimizes the maximum of the average queuing delay (MAQD) for a two-user network scenario. The Monte Carlo simulation experiments are performed to numerically validate the model by providing MAQD results for both NOMA and orthogonal multiple access (OMA) schemes. The results demonstrate that the NOMA achieves lower latency for low SINR outage thresholds while its performance is degraded faster than OMA as the SINR outage threshold increases such that OMA outperforms NOMA beyond a certain threshold. Another important result is that the latency performance of NOMA is significantly degraded when the 5G NR frame types having wider bandwidth are utilized. The results provide powerful insights for 5G ultra-reliable low-latency communication (URLLC) services.