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The performance of the Acoustic Emission (AE) technique was investigated to establish its reliability in detecting and locating fatigue crack damage as well as distinguishing between different AE sources in potential SHM applications. Experiments were conducted to monitor the AE signals generated during fatigue crack growth in coupon 2014 T6 aluminium. The influence of stress ratio, stress range, sample geometry and whether or not the load spectrum was of constant or variable amplitude were all investigated. Timing filters were incorporated to eliminate extraneous AE signals produced from sources other than the fatigue crack. AE signals detected were correlated with values of applied cyclic load throughout the tests. Measurements of Time difference of arrival were taken for assessment of errors in location estimates obtained using time of flight algorithms with a 1D location setup. It was found that there was significant variability in AE Hit rates in otherwise identical samples and test conditions. However common trends characteristic of all samples could be observed. At the onset of crack growth high AE Hit rates were observed for the first few millimetres after which they rapidly declined to minimal values for an extended period of crack growth. Another peak and then decline in AE Hit rates was observed for subsequent crack growth before yet another increase as the sample approached final failure. The changes in AE signals with applied cyclic load provided great insights into the different AE processes occurring during crack growth. AE signals were seen to occur in the lower two-thirds of the maximum load in the first few millimetres of crack growth before occurring at progressively smaller values as the crack length increased. These emissions could be associated with crack closure. A separate set of AE signals were observed close to the maximum cyclic stress throughout the entire crack growth process. At the failure crack length AE signals were generated across the entire loading range. Novel metrics were developed to statistically characterise variability of AE generation with crack growth and at particular crack lengths across different samples. A novel approach for fatigue crack length estimation was developed based on monitoring applied loads to the sample corresponding with generated AE signals which extends the functionality of the AE technique in an area which was previously deficient. It is however limited by its sensitivity to changes in sample geometry. Experiments were also performed to validate the performance of the AE technique in detecting and locating fatigue crack in a representative wing-box structure. An acousto-ultrasonic method was used to calibrate the AE wave velocity in the structure which was used to successfully locate the 'hidden' fatigue crack. A novel observation was made in the series of tests conducted where the complex propagation paths in the structure could be exploited to perform wide area sensing coverage in certain regions using sensors mounted on different components of the structure. This also extends current knowledge on the capability of the AE technique.

The real-time applications and the IoT promote the need for a newer idle spectrum to support the required high traffic. This pushes toward the emergence of the millimeter-wave (mmWave) and the sub-Terahertz (sub-THz) bands in wireless communication. Albeit these higher frequency bands offer wide spectrum help improving the spectral efficiency, it comes with the challenge of alleviating the severe attenuation. MmWave transceivers use large antenna arrays to form high-directional beams and overcome severe attenuation. A large array size leads to a costly beam alignment process if no prior information about beam directions is available. Beam alignment has two phases: beam discovery, and beam tracking. Beam discovery is finding the beam direction by consuming several pilot symbols to find the optimum direction. Moreover, beam tracking is a common approach to keep the discovered beams tightly coupled without frequent beam discovery to eliminate the overhead associated with realignment. Both phases become more difficult as the beams get narrower since slight mismatches lead to significant degradation in SNR as the beam coherence times are short. As a result, beams may lose alignment before they can be readjusted periodically with the aid of pilot signals. In this thesis, we introduce two complementary proposals. The first proposal is for the issue of beam tracking, and the second proposal is for the issue of beam discovery. In the first part of the thesis, we propose a model where the receiver adjusts beam direction continuously over each physical-layer sample according to a carefully calculated estimate of the continuous variation of the beams. Our approach contrasts the classical methods, which fix the beams in the same direction between pilots. In our approach, the change of direction is configured using the estimate of variation rate via two different methods; a Continuous-Discrete Kalman filter and an MMSE of a first-order approximation of the variation. Our method incurs no additional overhead in pilots, yet, the performance of beam tracking and the resulting effective SNR are improved significantly. In addition, we propose a low-complexity discrete beam tracking algorithm that only requires a single pilot symbol with an angle of arrival (AoA) that lies in the main lobe of the beamforming. In typical situations, we achieve up to 4 dB enhancement in SNR and can reach 7 dB for large array sizes. This gain is associated with our algorithm's average Mean Squared Error (MSE) reduction by up to 99.5%. Furthermore, we introduce an analytical method of choosing the Pilot Period to sustain reliable tracking for a desirable Link Reestablishment Time based on two definitions: (1) beam coherence and (2) outage probability. The numerical results reveal that the Continuous-Discrete algorithms reduce the pilot overhead by 60% and up to 87% while supporting 95% of the maximum achievable rate. In the second part of the thesis, we present a novel beam direction discovery approach that utilizes beam pattern amplitudes and combines it with the phase information available for a quick estimate of the beam direction. The proposed approach surpasses the traditional approaches, which exhaustively search all possible directions or follow hierarchical beam-sweeping architecture by dividing the angular space into sectors. Our approach minimizes the signaling overhead by estimating the beam direction in a single measurement in most instances instead of a number of measurements proportional to the array size as in traditional beam discovery approaches. Numerical results reveal a significant reduction of the pilot overhead by 42% and up to 62% compared to the state-of-the-art.

The reliability of Nondestructive Evaluation (NDE) is an important input for risk analysis for sustainment of aging infrastructure. Reliability has typically been quantified via probability of detection (POD) studies. There are three problems with POD modeling methodologies provided in the most recent guidance on the subject:1)Current models do not estimate the extremes of a POD curve very well because of the assumption that the POD curve approaches zero as flaw size goes to zero, and the POD curve approaches one as the flaw size goes to infinity.2)The existing 2-parameter logit/probit models can be misused since there is not a set of diagnostics and procedures that can catch every violation of fundamental assumptions.3)Data sets from realistic inspections often violate core assumptions in statistical models such as homoscedasticty and linearity, but statistical inference is still needed for the application.Since one of the important inputs to risk assessment is POD, and it's believed that the output of risk analyses can be sensitive to the tail behavior at large flaw sizes, it is worthwhile to consider better estimation procedures for the extremes of a POD curve. In this dissertation, new POD models that include lower and upper asymptotes are proposed to better model tail behavior. Transformations such as Box-Cox are proposed to mitigate violations of homoscedasticity, and bootstrapping is proposed to provide confidence bound calculations for higher order models. A case study is presented where these improvements to POD analysis are incorporated into a risk analysis. Simulation studies a presented to quantify the improvements of this work.

Three spined, mussel species occur in the United States along the Atlantic slope; James spinymussel (Pleurobema collina), Tar spinymussel (Elliptio steinstansana), and Altamaha spinymussel (E. spinosa). The James spinymussel was listed as endangered in 1988, and was until recently considered to be endemic to the James River basin (Clarke and Neves 1984; USFWS 1990). Biologists with the North Carolina Department of Transportation (NCDOT) discovered spinymussel populations in the Dan and Mayo rivers in NC in 2000 and 2001, respectively. The U.S. Fish & Wildlife Service (USFWS) tentatively identified this species as Pleurobema collina. My project proposed by the Virginia Cooperative Fish and Wildlife Research Unit to the USFWS and the Virginia Transportation Research Council, determined where P. collina resides in VA and what the extent of its range is within the state. An informal preliminary survey design for P. collina was used during the summer of 2002 and simple random sampling was deployed in 2003-2004 surveys to provide a good basis for comparison to gauge the efficiency of the informal sampling design. In 2002, a total of 116 person-hours were spent surveying 39 localities on the Mayo, Dan, and Smith rivers. A total of 96 P. collina was observed in the South Fork of the Mayo River, Patrick and Henry counties, VA. A documented range of 24 rkm was established in the South Fork Mayo River. During the summers of 2003 and 2004, a total of 228 person-hours were spent surveying 38 equal-area river reaches (10, 000 m2) on the mainstems of the Dan, Smith, South Mayo, and Banister rivers. No specimens of P. collina (live or relic shells) were detected. A simple random sampling approach was designed to be easy, relatively quick and cost effective, applicable to most rivers, and to provide actual numbers for comparison. Negative results were only reported after 6 person-hours of searching within each randomly selected, equal-area river reach had been expended. P. collina was declared absent from the VA random sites surveyed in 2003-2004 with a confidence of ~90%. A genetic characterization of four extant populations of P. collina was conducted to assess its taxonomic affinity and to resolve conservation issues related to recovery planning and management actions. The populations were examined for phenotypic variation, and were characterized phylogenetically using DNA sequences. A comprehensive analysis was performed for both separate and combined mitochondrial (357 bp of cytochrome-b, 916 bp of ND-1) and nuclear (502 bp of ITS-1) DNA sequences. Based on comprehensive molecular, morphological, and life history data, populations of P. collina sampled from the Dan River sub-drainage do not warrant separate species designation from P. collina sampled from the James River drainage.

In this thesis, the problem of finding optimal searcher paths for multiple searchers is studied. First, a wireless multi-robot test bed consisting of four mobile platforms, a wireless radio link for communication and a central station is introduced. Then, a hierarchical control architecture for multi-vehicle teams and a communication protocol is developed. In the second part of the thesis, this cooperation framework is used to devise search strategies for multiple searchers. We implemented an algorithm to find a searcher path so as to approximate the optimal effort allocation. Next, the co- ordination and coordinated set concepts are defined and the algorithm is extended to multiple searchers. It is observed that the hierarchical control architecture provides a useful framework to design cooperative control laws. Furthermore, we demonstrated that our cooperative search strategy achieves a close approximation to the optimal allocation.

I compared the performance of habitat models created from data of differing reliability. Because the reliability is dependent on the probability of detecting the species, I experimented to estimate detectability for a salamander species. Based on these estimates, I investigated the sensitivity of habitat models to varying detectability. Models were created using a database of amphibian and reptile observations at Fort A.P. Hill, Virginia, USA. Performance was compared among modeling methods, taxa, life histories, and sample sizes. Model performance was poor for all methods and species, except for the carpenter frog (Rana virgatipes). Discriminant function analysis and ecological niche factor analysis (ENFA) predicted presence better than logistic regression and Bayesian logistic regression models. Database collections of observations have limited value as input for modeling because of the lack of absence data. Without knowledge of detectability, it is unknown whether non-detection represents absence. To estimate detectability, I experimented with red-backed salamanders (Plethodon cinereus) using daytime, cover-object searches and nighttime, visual surveys. Salamanders were maintained in enclosures (n = 124) assigned to four treatments, daytime__low density, daytime__high density, nighttime__low density, and nighttime__high density. Multiple observations of each enclosure were made. Detectability was higher using daytime, cover-object searches (64%) than nighttime, visual surveys (20%). Detection was also higher in high-density (49%) versus low-density enclosures (35%). Because of variation in detectability, I tested model sensitivity to the probability of detection. A simulated distribution was created using functions relating habitat suitability to environmental variables from a landscape. Surveys were replicated by randomly selecting locations (n = 50, 100, 200, or 500) and determining whether the species was observed, based on the probability of detection (p = 40%, 60%, 80%, or 100%). Bayesian logistic regression and ENFA models were created for each sample. When detection was 80 __ 100%, Bayesian predictions were more correlated with the known suitability and identified presence more accurately than ENFA. Probability of detection was variable among sampling methods and effort. Models created from presence/absence data were sensitive to the probability of detection in the input data. This stresses the importance of quantifying detectability and using presence-only modeling methods when detectability is low. If planning for sampling as an input for suitability modeling, it is important to choose sampling methods to ensure that detection is 80% or higher.