Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Optimality, Synthesis and a Continuum Model for Collective Motion(2019) Halder, Udit; Krishnaprasad, Perinkulam S.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It is of importance to study biological collectives and apply the wisdom so accrued to modern day engineering problems. In this dissertation we attempt to gain insight into collective behavior where the main contribution is twofold. First, a `bottom-up' approach is employed to study individual level control law synthesis and emergence thereby of collective behavior. Three different problems, involving single and multiple agents, are studied by both analytical and experimental means. These problems arise from either a practical viewpoint or from attempts at describing biologically plausible feedback mechanisms. One result obtained in this context for a double agent scenario is that under a particular constant bearing pursuit strategy, the problem exhibits certain features common with the Kepler two body problem. Laboratory demonstrations of the solutions to these problems are presented. It is to be noted that these types of individual level control problems can help understand and construct building blocks for group level behaviors. The second approach is `top-down' in nature. It treats a collective as a whole and asks if its movement minimizes some kind of energy functional. A key goal of this work is to develop wave equations and their solutions for a natural class of optimal control problems with which one can analyze information transfer in flocks. Controllability arguments in infinite dimensional spaces give strong support to construct solutions for such optimal control problems. Since the optimal control problems are infinite dimensional in the state space and one cannot simply expect Pontryagin's Maximum Principle (PMP) to apply in such a setting, the work has required care and attention to functional analytic considerations. In this work, it is shown that under a certain assumption on finite co-dimensionality of a reachable set, PMP remains valid. This assumption is then shown to hold true for the case of a specific ensemble of agents, each with state space as the Heisenberg group H(3). Moreover, analysis of optimal controls demonstrates the existence of traveling wave solutions in that setting. Synchronization results are obtained in a high coupling limit where deviation from neighbors is too costly for every agent. The combination of approaches based on PMP and calculus of variations have been fruitful in developing a solid new understanding of wave phenomena in collectives. We provide partial results along these lines for the case of a continuum of planar agents (SE(2) case). Finally, a different top-down and data-driven approach to analyze collective behavior is also put forward in this thesis. It is known that the total kinetic energy of a flock can be divided into several modes attributed to rigid-body translations, rotations, volume changes, etc. Flight recordings of multiple events of European starling flocks yield time-signals of these different energy modes. This approach then seeks an explanation of kinetic energy mode distributions (viewed as flock-scale decisions) by appealing to techniques from evolutionary game theory and optimal control theory. We propose the notion of cognitive cost that calculates a suitably defined action functional and measures the cost to an event, resulting from temporal variations of energy mode distributions.Item FISSION-FUSION DYNAMICS IN MAMMALS: LINKING ANIMAL MOVEMENT TO GROUP BEHAVIOR(2016) Alvarez, Silvia J.; Fagan, William F; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mammals living in groups show temporal variation in spatial cohesion and membership of groups, a behavior known as fission-fusion dynamics (FFD). Changes in cohesion depend on the movement behavior of individuals, which is influenced by their social environment, among other factors. I aimed to answer two main questions: 1) how do cognitive abilities and environmental factors explain the variation in social systems of mammals with FFD? and 2) how are FFD related to movement behavior? To answer the first question, I built a dataset on social traits of mammals with FFD from published references and used multivariate analysis to uncover the patterns of variation in social systems. Variation resulted mostly from differences in group and subgroup sizes, and differences in social traits evidenced the presence of discrete categories of social organization that might represent distinct strategies of FFD. To assess the effects of brain size and resource availability on social traits, I used generalized estimating equations as a phylogenetic comparative method. Brain size affected most social traits in marine mammals, supporting predictions of the social brain hypothesis. Resource availability was poorly correlated with social traits in all terrestrial mammals, but it had different effects for herbivores and carnivores, suggesting that environmental constraints acting on FFD differ between trophic levels. To answer the second question, I assessed the predictive power of several movement metrics characterizing tracks of orangutans on FFD, using generalized boosted regressions. Tortuosity, speed, and the number of behaviors were strong predictors of group presence and size, while temporal changes in movement behavior were correlated with changes in cohesion. These findings highlight the potential use of individual movement data to predict aspects of FFD. Lastly, I used an agent-based model to explore the influence of different levels of specificity in recognition on grouping behaviors. Model results suggest that basic social behavioral rules influence FFD, and that more complex group dynamics, such as hierarchical group structures, only emerge in scenarios with high levels of recognition specificity. Overall, the model suggests that recognition abilities, which likely correlate with cognitive skills, may play an important role in the evolution of social systems.Item Motion Reconstruction of Animal Groups: From Schooling Fish to Swarming Mosquitoes(2012) Butail, Sachit; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The long-term goal of this research is to provide kinematic data for the design and validation of spatial models of collective behavior in animal groups. The specific research objective of this dissertation is to apply methods from nonlinear estimation and computer vision to construct multi-target tracking systems that process multi-view calibrated video to reconstruct the three-dimensional movement of animals in a group. We adapt the tracking systems for the study of two animal species: Danio aequipinnatus, a common species of schooling fish, and Anopheles gambiae, the most important vector of malaria in sub-Saharan Africa. Together these tracking systems span variability in target size on image, density, and movement. For tracking fish, we automatically initialize, predict, and reconstruct shape trajectories of multiple fish through occlusions. For mosquitoes, which appear appear as faded streaks on in-field footage, we provide methods to extract velocity information from the streaks, adaptively seek missing measurements, and resolve occlusions within a multi-hypothesis framework. In each case the research has yielded an unprecedented volume of trajectory data for subsequent analysis. We present kinematic data of fast-start response in fish schools and first-ever trajectories of wild mosquito swarming and mating events. The broader impact of this work is to advance the understanding of animal groups for the design of bio-inspired robotic systems, where, similar to the animal groups we study, the collective is able to perform tasks far beyond the capabilities of a single inexpensive robot.