Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

This thesis addresses the possibility of using a delay-on-ground concept in which flights with less than 1 hour flying time (often referred to as pop-up flights) absorb their arrival sequencing delay at the departure gate by being issued their Arrival Manager (AMAN)-scheduled time as a Required Time of Arrival (RTA) that is inserted into the Flight Management System (FMS). Due to their short duration these flights are currently often inserted into the AMAN sequence shortly before Terminal Manoeuvring Area (TMA) entry and thereby often need to absorb their arrival sequencing delay in the inefficient manner of airborne holding or vectoring close to the arrival airport. The literature review examines current operational procedures of AMANs and Departure Managers (DMANs), the current FMS RTA function and live trials in which the delay-on-ground concept was tested in real operations. A case study airport in Europe that has potential to benefit from the concept is identified. The performance of the delay-on-ground concept for the case study airport is then assessed by performing 180 fast-time Monte Carlo simulation runs. For each run the arrival flow to the case study airport and the departure flows from two medium-sized airports from which the pop-up flights originate are simulated. Each run represents an operational day and variations in departure/arrivals time is put into the timetables to simulate the variation in actual departure/arrival times resulting from operational factors normally encountered in day-to-day operations. An algorithm is written in Matlab to simulate an AMAN-DMAN cooperation in which pop-up flights are locked to the required departure times to meet their RTAs. It is shown that a significant reduction in airborne delay time and fuel consumption can be achieved at the case study airport by using the concept. It is also shown that it is possible to ensure that the pop-up flights depart at the required times to meet their RTAs without negatively affecting the departure sequences.

Ο κύριος σκοπός της παρούσας εργασίας είναι να συγκεντρωθούν συγκεκριμένες θεωρήσεις που χρησιμοποιούν τον φορμαλισμό των συνεπών ιστοριών σε βασικά προβλήματα της κβαντικής θεωρίας και κβαντικής κοσμολογίας. Ο φορμαλισμός αυτός είναι πολλά υποσχόμενος για τον τομέα της κανονικής κβαντικής βαρύτητας. Ο λόγος που θα κάνουμε αυτή την ανασκόπηση είναι για να δώσουμε μία ενοποιημένη εικόνα στα ζητήματα αυτά και να μπορέσουμε να τα συγκρίνουμε. Συγκεκριμένα, το πρώτο μέρος αφορά δύο διαφορετικές προσεγγίσεις για το πρόβλημα του χρόνου άφιξης στην κβαντομηχανική, εκ των οποίων και οι δύο χρησιμοποιούν φορμαλισμό ιστοριών. Η πρώτη έγινε από τους Halliwell και Yearsly (2009) και η δεύτερη από τους Anastopoulo και Saviddou (2012). Από την σύγκριση αυτών καταλήγουμε στο συμπέρασμα ότι και οι δύο δίνουν μία αδρομερή μορφή της εξίσωσης του Kijowski. Το δεύτερο μέρος αφορά την κβαντική κοσμολογία. Σε αυτό αρχικά παρουσιάζεται μία προσέγγιση με συνεπείς ιστορίες για την πυκνότητα πιθανότητας στην κβαντική κοσμολογία η οποία έγινε από τον Halliwell (2009). Στην συνέχεια παρουσιάζεται μία προσέγγιση με ιστορίες για μοντέλα μίνι-υπερχώρου από τους Anastopoulo and Savidou (2005). Σε αυτή κατασκευάζονται μοντέλα μίνι-υπερχώρου με όρους προβολικών τελεστών ιστοριών (HPO). Η σπουδαιότητα αυτού του φορμαλισμού έγκειται στο γεγονός ότι η γενική σχετικότητα σε αυτή την μορφή ικανοποιεί και τους χωροχρονικούς διαφορομορφισμούς και την άλγεβρα Dirac, με αποτέλεσμα να είναι εύκολα κβαντίσιμη. / The major purpose of this study is to consecrate specific approaches to some problems of quantum theory and quantum cosmology, in terms of decoherence histories formalism which is a very promising formalism for the canonical quantum gravity theories. The reason is to give a unified picture to these issues in order to be possible to compare them. Specifically, the first part contains two different approaches to the time of arrival in quantum mechanics, both of these use a histories formalism. The first is from Halliwell and Yearsly (2009) and the second from Anastopoulos and Saviddou (2012). By comparing them we deduce that both of them first gives a coarse-grain form of the Kijowski' s probability distribution. The second part concerns quantum cosmology. In this, we presented a decoherent histories approach to quantum cosmological probabilities, in which was used a complex potential, from Halliwell (2009). After that we present a histories approach to minisuperspace models by Anastopoulos and Savidou (2005). In this, minisuperspace models is written in terms of histories projector operator (HPO) formalism. The spectacular of this is that in that form general relativity satisfies both spacetime diffeomorfisms and Dirac algebra, which is very important because it is easier to be quantized.

This study investigates the manifestation of geometric features of turbine blades in signatures of non-optical time of arrival (ToA) probes. The approach enables an evaluation of the various signal characteristics used for defining ToA for a range of airfoil geometries and provides knowledge about additional waveform characteristics. The objective of this research is to increase the accuracy of microwave ToA probes by gaining a better understanding of the microwave signals in five steps. Firstly, ToA definitions used in the past are compared. Considering accuracy, computational effort, and versatility, the constant fraction crossing definition is found to be the most accurate. Secondly, an experimental apparatus capable of measuring airfoil ToA with microwave probes and optical probes as a reference is designed and built. As a third step, a catalog of 16 turbine blade geometries is developed. Fourthly, the signatures of these turbine blades are acquired using both the optical and the microwave probes. Finally, the impact of the geometric effects on the signatures is evaluated. The quality of the microwave results is found to be highly dependent on the polarization of the microwaves. Analysis of the time domain signal shows that decreasing the blade width, increasing the chord angle, or incorporating a blade tip pocket or a varying cross-section leads to a decrease in the amplitude of the peak caused by the blade. Increasing the blade width and incorporating a chord angle leads to an increase in peak width. A frequency domain analysis is conducted on the microwave signals and verified using a synthetic signal. This analysis confirms the findings from the time domain analysis. The time domain analysis of the laser measurements shows that the spatial resolution of the laser is much higher than that of the microwave sensor. Consequently, the signal acquired with the optical setup provides a good means of defining the blade ToA. The knowledge gained in this study about the sensor and its interaction with passing blade tips of varying geometry can be used to enhance the understanding of microwave ToA measurements. This knowledge provides further insight into airfoil and engine health.

abstract: In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered. In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived. In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived. A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

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Yes / Underwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency (RF) broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival (AOA) and the time difference of arrival (TDoA). The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net-zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154m and 93.8275m for four trials, 61.2256m and 92.7956m for eight trials, and 42.6584m and 119.5228m for twelve trials. Comparatively, the distance-based measurements show higher accuracy than the angle-based measurements.

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This work has two objectives: a) to predict people's future locations, and b) to predict when they will be at given locations. Current location-based applications react to the user's current location. The progression from location-awareness to location-prediction can enable the next generation of proactive, context-predicting applications. Existing location-prediction algorithms predict someone's next location. In contrast, this dissertation predicts someone's future locations. Existing algorithms use a sequence of locations and predict the next location in the sequence. This dissertation incorporates temporal information as timestamps in order to predict someone's location at any time in the future. Sequence predictors based on Markov models have been shown to be effective predictors of someone's next location. This dissertation applies a Markov model to two-dimensional, timestamped location information to predict future locations. This dissertation also predicts when someone will be at a given location. These predictions can support presence or understanding co-workers’ routines. Predicting the times that someone is going to be at a given location is a very different and more difficult problem than predicting where someone will be at a given time. A location-prediction application may predict one or two key locations for a given time, while there could be hundreds of correct predictions for times of the day that someone will be in a given location. The approach used in this dissertation, a heuristic model loosely based on Market Basket Analysis, is the first to predict when someone will arrive at any given location. The models are applied to sparse, WiFi mobility data collected on PDAs given to 275 college freshmen. The location-prediction model predicts future locations with 78-91% accuracy. The temporal-prediction model achieves 33-39% accuracy. If a tolerance of plus/minus twenty minutes is allowed, the prediction rates rise to 77%-91%. This dissertation shows the characteristics of the timestamped, location data which lead to the highest number of correct predictions. The best data cover large portions of the day, with less than three locations for any given timestamp. / Ph. D.