In this thesis, I present my study on the impact of multi-group network structure on the performance of consensus building strategies, and the preliminary mathematical formulation of the problem on improving the performance of the National Airspace system (NAS) through long-term investment. The first part of the thesis is concerned with a structural approach to the consensus building problem in multi-group distributed sensor networks (DSNs) that can be represented by bipartite graph. Direct inference of the convergence behavior of consensus strategies from multi-group DSN structure is one of the contributions of this thesis. The insights gained from the analysis facilitate the design and development of DSNs that meet specific performance criteria. The other part of the thesis is concerned with long-term planning and development of the NAS at a network level, by formulating the planning problem as a resource allocation problem for a flow network. The network-level model viewpoint on NAS planning and development will give insight to the structure of future NAS and will allow evaluation of various paradigms for the planning problem.

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A biometric method is a more secure way of personal identification than passwords. This thesis examines the iris as a personal identifier with the use of neural networks as the classifier. A comparison of different feature extraction methods that include the Fourier transform, discrete cosine transform, the eigen analysis method, and the wavelet transform, is performed. The robustness of each method, with respect to distortion and noise, is also studied.

Beginning the study with a regional network map with an intent to perform a detailed harmonic study for a certain location, the first question that comes up is how far out in the system should detailed modeling of individual devices (transmission lines, loads, transformers, capacitor banks, etc) be done. The reason why this is extremely important is because system components that will affect the frequency response characteristics in the specific location should not be missed or poorly modeled. Frequency scan is the simplest and most commonly used simulation technique used to characterize the response of a power system network as a function of frequency. Unfortunately, there are two major problems using frequency scan techniques when real harmonic studies are considered: 1) the size of the admittance matrices (this calculation is repeated using discrete frequency steps throughout the range of interest) may be so large that an exact mathematical model of the system is not realistic and 2) the complexity of a rigorous and complete mathematical model of the system does not necessarily explain the extent to which system components affect the frequency response characteristics in a specified location. It is seldom clear how much of the system must be represented in order to get accurate results in a harmonic study. Realistic procedures to identify whether to include a particular element in a detailed model or to lump the element into a simplifying equivalent are yet to be developed in the industry. It is safe to say that practicing engineers are using tools and techniques of questionable validity. Two new computer-oriented methods that use eigen analysis techniques to identify easily and accurately the boundary between system areas to be modeled in detail and those represented by equivalents are proposed in this dissertation. The key here is to recognize that not all elements present in the ?external? system will participate in the resonant harmonic modes and could therefore be lumped into a simplified short-circuit equivalent. Achieving these objectives from either one of the two methods can be economically attractive. In short, the work described in this dissertation is a fundamentally sound alternative for the purposes of network equivalencing and model reduction.

Land mines are a major humanitarian problem with millions of active mines in place around the world. Since these mines can have little metal in them, novel detection techniques are needed. Nuclear Quadrupole Resonance (NQR) is one such technique. Unfortunately, NQR is highly succeptible to radio frequency interference (RFI). A significant contribution of this thesis is the development of a custom, experimental data acquisition system designed and built specifically for capturing RFI at frequencies significant to NQR land mine detection systems. Another major contribution is the development of data analysis techniques for determining the number of reference antennas required to effectively cancel out RFI at frequencies and in environments typical of an NQR land mine detection system.

Cognitive radio is a technology that aims to maximize the current usage of the licensed frequency spectrum. Cognitive radio aims to provide services for license-exempt users by making use of dynamic spectrum access (DSA) and opportunistic spectrum sharing strategies (OSS). Cognitive radios are defined as intelligent wireless devices capable of adapting their communication parameters in order to operate within underutilized bands while avoiding causing interference to licensed users. An underused band of frequencies in a specific location or time is known as a spectrum hole. Therefore, in order to locate spectrum holes, reliable spectrum sensing algorithms are crucial to facilitate the evolution of cognitive radio networks. Since a large and growing body of literature has mainly focused into the conventional time domain (TD) energy detector, throughout this thesis the problem of spectrum sensing is investigated within the context of a frequency domain (FD) approach. The purpose of this study is to investigate detection based on methods of nonparametric power spectrum estimation. The considered methods are the periodogram, Bartlett's method, Welch overlapped segments averaging (WOSA) and the Multitaper estimator (MTE). Another major motivation is that the MTE is strongly recommended for the application of cognitive radios. This study aims to derive the detector performance measures for each case. Another aim is to investigate and highlight the main differences between the TD and the FD approaches. The performance is addressed for independent and identically distributed (i.i.d.) Rayleigh channels and the general Rician and Nakagami fading channels. For each of the investigated detectors, the analytical models are obtained by studying the characteristics of the Hermitian quadratic form representation of the decision statistic and the matrix of the Hermitian form is identified. The results of the study have revealed the high accuracy of the derived mathematical models. Moreover, it is found that the TD detector differs from the FD detector in a number of aspects. One principal and generalized conclusion is that all the investigated FD methods provide a reduced probability of false alarm when compared with the TD detector. Also, for the case of periodogram, the probability of sensing errors is independent of the length of observations, whereas in time domain the probability of false alarm is increased when the sample size increases. The probability of false alarm is further reduced when diversity reception is employed. Furthermore, compared to the periodogram, both Bartlett method and Welch method provide better performance in terms of lower probability of false alarm but an increased probability of detection for a given probability of false alarm. Also, the performance of both Bartlett's method and WOSA is sensitive to the number of segments, whereas WOSA is also sensitive to the overlapping factor. Finally, the performance of the MTE is dependent on the number of employed discrete prolate spheroidal (Slepian) sequences, and the MTE outperforms the periodogram, Bartlett's method and WOSA, as it provides the minimal probability of false alarm.