While the interest in the Martian moons increases, the low-thrust propulsion technology is expected to enable novel mission scenarios but is associated with unique trajectory design challenges. Accordingly, the current investigation introduces a multi-phase low-thrust design framework. The trajectory of a potential spacecraft that departs from the Earth vicinity to reach both of the Martian moons, is divided into four phases. To describe the motion of the spacecraft under the influence of gravitational bodies, the two-body problem (2BP) and the Circular-Restricted Three Body Problem (CR3BP) are employed as lower-fidelity models, from which the results are validated in a higher-fidelity ephemeris model. For the computation and optimization of low-thrust trajectories, direct collocation algorithm is introduced. Utilizing the dynamical models and the numerical scheme, the low-thrust trajectory design challenge associated each phase is located and tackled separately. For the heliocentric leg, multiple optimal control problems are formulated between the planets in heliocentric space over different departure and arrival epochs. A contour plot is then generated to illustrate the trade-off between the propellant consumption and the time of flight. For the tour of the Martian moons, the science orbits for both moons are defined. Then, a new algorithm that interfaces the Q-law guidance scheme and direct collocation algorithm is introduced to generate low-thrust transfer trajectories between the science orbits. Finally, an end-to-end trajectory is produced by merging the piece-wise solutions from each phase. The validity of the introduced multi-phase formulation is confirmed by converging the trajectories in a higher-fidelity ephemeris model.<br>

Autonomous ships are one way to increase safety at sea and to decrease environmental impact of marine traveling and shipping. For this application, a good representation of the environment and a physical model of the ship are vital components. By optimizing the trajectory of the ship, a good trade-off between the time duration and energy consumption can be found. In this thesis, a three degree of freedom model that describes the dynamics of a small-scale surface ship is estimated. By using optimal control theory and a grey-box model, the parameters are estimated by defining an optimal control problem (OCP). The optimal solution is found by transcribing the problem into a nonlinear program and solving it using an interior point algorithm. The identification method is tested and validated using simulated data as well as using data from real world experiments. The performance of the estimated models is validated using cross validation. In a second track of this thesis, a trajectory is created in two steps. The first is path planning to find a shortest geometric path between two points. In the second step, the path is converted to a trajectory and is optimized to become dynamically feasible. For this purpose, a roadmap is generated from a modified version of the generalized Voronoi diagram. To find an initial path in the roadmap, the A-star algorithm is utilized and to connect start and goal position to the map a few different methods are examined. An initial trajectory is created by mapping a straight-line trajectory to the initial path, thus connecting time, position and velocity. The final trajectory is found by solving a discrete OCP initialized with the initial trajectory. The OCP contains spatial constraints that ensures that the vessel does not collide with static obstacles. The suggested estimation method resulted in models that could be used for trajectory planning to generate a dynamically feasible trajectory for both simulated and real data. The trajectory generated by the trajectory planner resulted in a collision-free trajectory, satisfying the dynamics of the estimated model, such that the trade-off between time duration and energy consumption is well balanced. Future work consists of implementation of a controller to see if the planned trajectory can be followed by the small-scale ship.

Recently, the availability of high quality and high resolution spatio-temporal data has increased for many sports. This enabled deep analysis of player behaviour and game strategy. This thesis investigates the assumption that game strategy is latent information in tracking data from soccer games and the possibility of modelling player behaviour with deep imitation learning. A possible application would be to perform counterfactual analysis, and switch an observed player in a real sequence, with a simulated player to asses alternative scenarios. An imitation learning application is implemented using recurrent neural networks. It is shown that the application is able to learn individual player behaviour and perform rollouts on previously unseen sequences.

My research proposal intends to investigate deployable structures by studying nomadic systems, to be able to create lightweight structures. This is done by studying the local nomadic knowledge, combined with unconventional and experimental techniques from pneumatic, mobile, and tensile structural innovation, enabling a small or no footprint in the fragile context.   My point of departure is the conception of buildings that move while distributing culture fast and democratic, taking into account layers of social, cultural, and environmental necessities. Through this research, I aim to find organizational, conceptual tools and models for regenerating cultural environment, to create an autonomous building system, aware of the complexities of the global pandemic and the crisis of cultural institutions.   Informed by local, vernacular techniques, structural and environmental, secrets are revealed and translated into new flexible components and cultural and educational programs, while pointing at the importance of local techniques and material choices.    Based on theories and techniques borrowed from the fields of nomadic, pneumatic, and deployable systems, this research resulted in a system of mobile structures that deploy culture and democratically empower communities, anchored in its immediate cultural and social context, and responding to new, wide expressions of cultural exchange.

<p>Many opportunities for frequent transit between the lunar vicinity and the heliocentric region will arise in the near future, including servicing missions to space telescopes and proposed missions to various asteroids and other destinations in the solar system. The overarching goal of this investigation is the development a framework for periodic and transit options in the Earth-Moon-Sun system. Rather than overlapping different dynamical models to capture the dynamics of the cislunar and heliocentric region, this analysis leverages a four-body dynamical model, the Bicircular Restricted Four-Body Problem (BCR4BP), that includes the dynamical structures that exist due to the combined influences of the Earth, the Moon, and the Sun. The BCR4BP is an intermediate step in fidelity between the CR3BP and the higher-fidelity ephemeris model. The results demonstrate that dynamical structures from the Earth-Moon-Sun BCR4BP provide valuable information on the flow between cislunar and heliocentric spaces. </p> <p><br></p> <p>Dynamical structures associated with periodic and bounded motion within the BCR4BP are successfully employed to construct transfers between the 9:2 NRHO and locations of interest in heliocentric space. The framework developed in this analysis is effective for transit between any cislunar orbit and the Sun-Earth libration point regions; a current important use case for this capability involves departures from the NRHOs, orbits that possess complex dynamics and near-stable properties. Leveraging this methodology, one-way trajectories from the lunar vicinity to a destination orbit in heliocentric space are constructed, as well as round-trip trajectories that returns to the NRHO after completion of the objectives in heliocentric space. The challenges of such trajectory design include the phasing of the trajectory with respect to the Earth, the Moon, the Sun, on both the outbound and inbound legs of the trajectory. Applications for this trajectory include servicing missions to a space telescope in heliocentric space, where the initial and final locations of the mission is the Gateway near the Moon. Lastly, the results of this analysis demonstrate that the properties and geometry of the periodic orbits, bounded motion, and transfers that are delivered from the BCR4BP are maintained when the trajectories are transitioned to the higher-fidelity ephemeris model. </p>

Connected aviation, the Internet of Flying Things and related emerging technologies, such as the System-Wide Information Management infrastructure of the FAA NextGen program, present numerous opportunities for the aviation sector. The ubiquity of aeronautical, flight, weather, aerodrome, and maintenance data accelerates the development of smarter software systems to cope with the ever increasing requirements of the industry sector. The increasing amount, frequency and variety of real-time data available to modern air transport and tactical systems, and their crews, creates exciting new challenges and research opportunities. We present an architectural approach toward the vision of increasingly self-separating and self-governed flight operations within the bigger picture of an evolving set of future Autonomous Flight Rules. The challenges in this field of research are manifold and include autonomic airborne trajectory optimization, data sharing, fusion and information derivation, the incorporation of and communication with rational actors—both human and machine—via a connected aviation infrastructure, to facilitate smarter decision making and support while generating economical, environmental and tactical advantages. We developed a concept and prototype implementation of our Smart Autoflight Control System. The concept and implemented system follow the design principle of an Autonomic Element, consisting of an Autonomic Manager and its Managed Element, acting within an Autonomic Context. The Managed Element concept embraces an infrastructure featuring suitable models of manageable environments, airborne agents, planners, applicable operational cost and risk policies, and connections to the System-Wide Information Management cloud as well as to relevant rational actors, such as Air Traffic Control, Command and Control, Operations or Dispatch. The Autonomic Manager concept incorporates the extraction, that is, short-term sensing, of features from operational scenarios and the categorization of these scenarios according to their level of criticality and associated flight phase. The Autonomic Manager component, furthermore, continuously tunes, that is, actuates, manageable items of its Managed Element, such as environments and planners, and triggers competitions to assess their performance under the various extracted and dynamically changing features of their Autonomic Context. The performance reputations of the tuned manageable items are collected in a knowledge base and may serve as a long-term sensor. Both the managed items of the Managed Element as well the managing items of the Autonomic Manager are extendable and may realize very different paradigms, including deterministic, non-deterministic, heuristically guided, and biologically inspired approaches. We assessed the extensibility and maintainability of our Smart Autoflight Control System infrastructure by including manageable environments and planners of the Classical Grid Search, Probabilistic Roadmaps, and Rapidly-Exploring Random Trees families into its core component. Furthermore, we evaluated the viability of a simple heuristic and a more sophisticated Sequential Model-Based Algorithm Configuration Autonomic Manager to adaptively select and tune manageable planners of the supported families based on the extracted features from very simple to highly challenging scenarios. We were able to show that a self-adaptive approach, that heuristically tunes and selects the best performing planner following a performance competition, produces suitable flight trajectories within reasonable deliberation times. Additionally, we discovered options for improving our heuristic Autonomic Manager through a series of evaluation runs of the Sequential Model-Based Algorithm Configuration Autonomic Manager. Our contributions answer how the manageable items, that is, environments and planners, of our Smart Autoflight Control System core component have to be modified in order to embed System-Wide Information Management data that feature both spatial and temporal aspects. We show how operational cost and risk policies help to assess environments differently and plan suitable flight trajectories accordingly. We identify and implement the necessary extensions and capabilities that have to be supported by manageable and managing items, respectively, to enable continuous feature extraction, adaptive tuning, performance competitions, and planner selection in dynamic flight scenarios. / Graduate

The heterogeneous, chronic, and proliferating aspect of attention deficit hyperactivity disorder (ADHD) and comorbidities covers heritability, cognitive, emotional, motor, and everyday behavioral domains that place individuals presenting the condition at some considerable disadvantage. Disruption of "typical developmental trajectories" in the manifestation of gene-environment interactive predispositions implies that ADHD children and adolescents may continue to perform at defective levels as adults with regard to academic achievement, occupational enterprises, and interpersonal relationships, despite the promise of pharmacotherapeutic treatments. Physical exercise provides a plethora of beneficial effects against stress, anxiety, depression, negative affect and behavior, poor impulse control, and compulsive behavior concomitant with improved executive functioning, working memory and positive affect, as well as improved conditions for relatives and care-givers. Brain-derived neurotrophic factor, an essential element in normal brain development that promotes health-associated behaviors and quality-of-life, though reduced in ADHD, is increased markedly by the intervention of regular physical exercise. Functional, regional, and biomarker deficits, as well as hypothalamic-pituitary-adrenal disruptions, have been improved through regular and carefully applied exercise programs. In view of the complications involving ADHD with co-morbidities, such as obesity, the influence of regular physical exercise has not been found negligible. Physical exercise bestows a propensity for eventual manifestation of "redifferentiated" developmental trajectories that may equip ADHD adults with a prognosis that is more adaptive functionally, independent of the applications of other therapeutic agents and treatments.

Background For the 1.2 million older adults residing in U.S. nursing homes, little is known about their experience with physical frailty and cognitive impairment, two critical interrelated aging conditions. Methods Minimum Data Set 3.0 was used. Physical frailty was measured by FRAIL-NH and cognitive impairment by Brief Interview for Mental Status and Cognitive Performance Scale. Demographic and clinical characteristics were adjusted accordingly. Aim 1 described the prevalence of physical frailty and cognitive impairment and longitudinally examined the association between two conditions with the non-proportional odds model. Aim 2 used latent class analysis to identify physical frailty subgroups and estimated their association with cognitive impairment using multinomial logistic regression. Aim 3 fitted group-based trajectory models to identify physical frailty trajectories and cognitive impairment trajectories and quantified the association between the two sets of trajectories. Main Results Around 60% of older residents were physically frail and 68% had moderate/severe cognitive impairment, with improvement and worsening observed in both conditions, particularly in the first three months. Older residents with moderate/severe cognitive impairment were consistently and increasingly more likely to be frail. Three physical frailty subgroups were identified at admission. Greater cognitive impairment was associated with higher odds to belong to “severe physical frailty”. Five physical frailty trajectories and three cognitive impairment trajectories were identified over the first six months. One in five older residents were “Consistently Frail” and “Consistently Severe Cognitive Impairment”. Conclusion Findings emphasized the need for care management tailored to the heterogeneous presentations and progression trajectories of physical frailty and cognitive impairment.

Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation.  There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate.   Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements.  In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001.  Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels.  Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels.   We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data.  The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm.  EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery.   We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values.  These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential.  These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters.  Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data.  They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects. / Ph. D.

In this dissertation, we develop a new framework for 3D trajectory planning of Autonomous Underwater Vehicles (AUVs) in realistic ocean scenarios. The work is divided into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. We apply a Non-Linear Programming (NLP) to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. To assess the effectiveness of the extended framework, we initially test the methodology in idealized settings. The scheme is then demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we account for uncertainty in the ocean current field, is described by an ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. We formulate stochastic problems that aim to minimize a risk measure of the travel time or energy consumption, using a flexible methodology that enables the user to explore various objectives, ranging seamlessly from risk-neutral to risk-averse. The capabilities of the approach are demonstrated using steady and transient currents. Advanced visualization tools have been further designed to simulate results.