It is well known that a finite-size scatterer has a set of natural resonances, which are uniquely determined by the physical properties of the scatterer. This is also the case for a breast tumor which can be regarded as a dielectric scatterer. Since the scatterer is naturally "tuned" at the resonances, it is expected that an increased electromagnetic coupling would take place at the resonance frequencies compared to other frequencies. For a breast tumor, this would mean a higher power absorption, indicating a faster temperature increase resulting in more efficient hyperthermia. In this dissertation, an adaptive microwave concept is demonstrated for breast cancer applications. The general approach is to detect and identify the tumor-specific resonance, determine the electrical location of the tumor, and apply the focused microwave hyperthermia using the identified resonance and the electrical location. The natural resonances vary depending on the tumor size, shape, and breast tissue configuration. Therefore, an adaptive tuning of the microwave source to tumor-specific resonance frequencies could improve the overall efficiency of hyperthermia treatment by allowing for a faster and more effective heating to achieve a desired therapeutic temperature level. Applying the singularity expansion method (SEM), both the resonances and the electrical location can be obtained from the poles and residues, respectively. This SEM-based approach is computationally inexpensive and can easily be implemented as a combination processing into emerging UWB microwave systems. Alternatively, a relatively simple microwave system based on this concept can potentially be used in conjunction with existing mammography. / Ph. D.

Quantum chemical studies on lowcoordinated group 14-16 compounds have been performed. This thesis focuses particularly on silenes influenced by reverse Siδ-=Cδ+ bond polarization. Hypercoordinated carbon compounds are also studied. The geometries from calculations with several common computationally inexpensive methods have been tested against high level CCSD/cc-pVTZ geometries for a series of substituted silenes. Hybrid HF/DFT methods performed best among the inexpensive methods tested for silenes. Heavy alkenes strongly influenced by reverse polarization are found to have less exothermic dimerization energies for both head-to-head and head-to-tail dimerizations, and to have higher activation energies for water addition than naturally polarized heavy alkenes. We also investigated solvated lithium, magnesium and potassium silenolates and found that lithium and magnesium ions coordinate preferably to O, giving their SiC bond some double bond character. Reverse polarized 2-siloxy-, 2-thiosiloxy-, and 2-(N-sila-N-methyl)-silenes could according to calculations be formed thermolytically from the corresponding tetrasilanes as transient species. It was, however, found that silenes highly influenced by π-conjugative reverse polarization have low barriers for the back-reaction, and thus these silenes are more difficult to form as stable species than naturally polarized silenes. It is also found that conjugated 1-siladienes, formed by electrocyclic ring-opening of 1-silacyclobut-2-enes, which are highly influenced by π-conjugative reverse polarization, have higher barriers for electrocyclization back to starting material than naturally polarized 1-siladienes. It is found that CHe54+, CHe64+, CNe54+, and CNe64+ are the closest carbon analogs of SiH5-, SiH62-, SiF5- and SiF62-, respectively. However, due to their exothermic dissociation reaction, these very high-lying local minima will be impossible to reach experimentally.

This thesis presents a novel aspect and polarization invariant electromagnetic target recognition technique in resonance region based on use of MUSIC algorithm for the extraction of natural-resonance related target features. In the suggested method, the feature patterns called &ldquo / MUSIC Spectrum Matrices (MSMs)&rdquo / are constructed for each candidate target at each reference aspect angle using targets&rsquo / scattered data at different late-time intervals. These individual MSMs correspond to maps of targets&rsquo / natural-resonance related power distributions. All these patterns are first used to obtain optimal late-time interval for classifier design and a &ldquo / Fused MUSIC Spectrum Matrix (FMSM)&rdquo / is generated over this interval for each target by superposing MSMs. The resulting FMSMs include more complete information for target resonances and are almost insensitive to aspect and polarization. In case of multiple target recognition, the relative locations of a multi-target group and separation distance between targets are also important factors. Therefore, MSM features are computed for each multi-target group at each &ldquo / reference aspect/topology&rdquo / combination to determine the optimum late-time interval. The FMSM feature of a given multi-target group is obtained by the superposition of all these aspect and topology dependent MSMs. In both single and multiple target recognition cases, the resulting FMSM power patterns are main target features of the designed classifier to be used during real-time decisions. At decision phase, the unknown test target is classified either as one of the candidate targets or as an alien target by comparing correlation coefficients computed between MSM of test signal and FMSM of each candidate target.

The problem studied in this thesis, is the classification of the small-scale aircraft targets by using a natural resonance based electromagnetic feature extraction technique. The aircraft targets are modeled by perfectly conducting, thin wire structures. The electromagnetic back-scattered data used in the classification process, are numerically generated for five aircraft models. A contemporary signal processing tool, the Wigner-Ville distribution is employed in this study in addition to using the principal components analysis technique to extract target features mainly from late-time target responses. The Wigner-Ville distribution (WD) is applied to the electromagnetic back-scattered responses from different aspects. Then, feature vectors are extracted from suitably chosen late-time portions of the WD outputs, which include natural resonance related v information, for every target and aspect to decrease aspect dependency. The database of the classifier is constructed by the feature vectors extracted at only a few reference aspects. Principal components analysis is also used to fuse the feature vectors and/or late-time aircraft responses extracted from reference aspects of a given target into a single characteristic feature vector of that target to further reduce aspect dependency. Consequently, an almost aspect independent classifier is designed for small-scale aircraft targets reaching high correct classification rate.