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 Mechanisms for Trajectory Options Allocation in Collaborative Air Traffic Flow Management(2018) Mohanavelu Umamagesh, Prithiv Raj; Lovell, David J; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Flight delays are primarily due to traffic imbalances caused by the demand for airspace resource exceeding its capacity. The capacity restriction might be due to inclement weather, an overloaded air traffic sector, or an airspace restriction. The Federal Aviation Administration (FAA), the organization responsible for air traffic control and management in the USA, has developed several tools known as Traffic Management Initiatives (TMI) to bring the demand into compliance with the capacity constraints. Collaborative Trajectory Option Program (CTOP) is one such tool that has been developed by the FAA to mitigate the delay experienced by flights. Operating under a Collaborative Decision Making (CDM) environment, CTOP is considered as the next step into the future of air traffic management by the FAA. The advantages of CTOP over the traditional the TMIs are unequivocal. The concerns about the allocation scheme used in the CTOP and treatment of flights from the flight operators/airlines have limited its usage. This research was motivated by the high ground delays that were experienced by flights and how the rerouting decisions were made in the current allocation method used in a CTOP. We have proposed four alternative approaches in this thesis, which incorporated priority of flights by the respective flight operator, aimed at not merely reducing an individual flight operator’s delay but also the total delay incurred to the system. We developed a test case scenario to compare the performances of the four proposed allocation methods against one another and with the present allocation mechanism of CTOP.Item Energy Cooperation in Energy Harvesting Communication Systems(2016) Gurakan, Berk; Ulukus, Sennur; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In energy harvesting communications, users transmit messages using energy harvested from nature. In such systems, transmission policies of the users need to be carefully designed according to the energy arrival profiles. When the energy management policies are optimized, the resulting performance of the system depends only on the energy arrival profiles. In this dissertation, we introduce and analyze the notion of energy cooperation in energy harvesting communications where users can share a portion of their harvested energy with the other users via wireless energy transfer. This energy cooperation enables us to control and optimize the energy arrivals at users to the extent possible. In the classical setting of cooperation, users help each other in the transmission of their data by exploiting the broadcast nature of wireless communications and the resulting overheard information. In contrast to the usual notion of cooperation, which is at the signal level, energy cooperation we introduce here is at the battery energy level. In a multi-user setting, energy may be abundant in one user in which case the loss incurred by transferring it to another user may be less than the gain it yields for the other user. It is this cooperation that we explore in this dissertation for several multi-user scenarios, where energy can be transferred from one user to another through a separate wireless energy transfer unit. We first consider the offline optimal energy management problem for several basic multi-user network structures with energy harvesting transmitters and one-way wireless energy transfer. In energy harvesting transmitters, energy arrivals in time impose energy causality constraints on the transmission policies of the users. In the presence of wireless energy transfer, energy causality constraints take a new form: energy can flow in time from the past to the future for each user, and from one user to the other at each time. This requires a careful joint management of energy flow in two separate dimensions, and different management policies are required depending on how users share the common wireless medium and interact over it. In this context, we analyze several basic multi-user energy harvesting network structures with wireless energy transfer. To capture the main trade-offs and insights that arise due to wireless energy transfer, we focus our attention on simple two- and three-user communication systems, such as the relay channel, multiple access channel and the two-way channel. Next, we focus on the delay minimization problem for networks. We consider a general network topology of energy harvesting and energy cooperating nodes. Each node harvests energy from nature and all nodes may share a portion of their harvested energies with neighboring nodes through energy cooperation. We consider the joint data routing and capacity assignment problem for this setting under fixed data and energy routing topologies. We determine the joint routing of energy and data in a general multi-user scenario with data and energy transfer. Next, we consider the cooperative energy harvesting diamond channel, where the source and two relays harvest energy from nature and the physical layer is modeled as a concatenation of a broadcast and a multiple access channel. Since the broadcast channel is degraded, one of the relays has the message of the other relay. Therefore, the multiple access channel is an extended multiple access channel with common data. We determine the optimum power and rate allocation policies of the users in order to maximize the end-to-end throughput of this system. Finally, we consider the two-user cooperative multiple access channel with energy harvesting users. The users cooperate at the physical layer (data cooperation) by establishing common messages through overheard signals and then cooperatively sending them. For this channel model, we investigate the effect of intermittent data arrivals to the users. We find the optimal offline transmit power and rate allocation policy that maximize the departure region. When the users can further cooperate at the battery level (energy cooperation), we find the jointly optimal offline transmit power and rate allocation policy together with the energy transfer policy that maximize the departure region.Item Allocation Algorithms for Networks with Scarce Resources(2015) Sarpatwar, Kanthi Kiran; Khuller, Samir; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many fundamental algorithmic techniques have roots in applications to computer networks. We consider several problems that crop up in wireless ad hoc networks, sensor networks, P2P networks, and cluster networks. The common challenge here is to deal with certain bottleneck resources that are crucial for performance of the underlying system. Broadly, we deal with the following issues. Data: The primary goal in resource replication problems is to replicate data objects on server nodes with limited storage capacities, so that the latency of client nodes needing these objects is minimized. Previous work in this area is heuristic and without guarantees. We develop tight (or nearly) approximation algorithms for several problems including basic resource replication - where clients need all objects and server can store at most one object, subset resource replication - where clients require different subsets of objects and servers have limited non-uniform capacity, and related variants. Computational resources: To facilitate packing of jobs needing disparate amounts of computational resources in cluster networks, an important auxiliary problem to solve is that of container selection. The idea is to select a limited number of ``containers'' that represent a given pool of jobs while minimizing ``wastage'' of resources. Subsequently, containers representing jobs can be packed instead of jobs themselves. We study this problem in two settings: continuous - where there are no additional restrictions on chosen containers, and discrete - where we must choose containers from a given set. We show that the continuous variant is NP-hard and admits a polynomial time approximation scheme. Contrastingly, the discrete variant is shown to be NP-hard to approximate. Therefore, we seek bi-approximation algorithms for this case. Energy resources: Wireless ad hoc networks contain nodes with limited battery life and it is crucial to design energy efficient algorithms. We obtain tight approximation (up to constant factors) guarantees for partial and budgeted versions of the connected dominating set problem, which is regarded as a good model for a virtual backbone of a wireless ad hoc network. Further, we will discuss approximation algorithms for some problems involving target monitoring in sensor networks and message propagation in radio networks. We will end with a discussion on future work.Item Resource Allocation in Computer Vision(2013) Chen, Daozheng; Jacobs, David W; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We broadly examine resource allocation in several computer vision problems. We consider human resource or computational resource constraints. Human resources, such as human operators monitoring a camera network, provide reliable information, but are typically limited by the huge amount of data to be processed. Computational resources refer to the resources used by machines, such as running time, to execute the programs. It is important to develop algorithms to make effective use of these resources in computer vision applications. We approach human resource constraints with a frame retrieval problem in a camera network. This work addresses the problem of using active inference to direct human attention in searching a camera network for people that match a query image. We find that by representing the camera network using a graphical model, we can more accurately determine which video frames match the query, and improve our ability to direct human attention. We experiment with different methods to determine from which frames to sample expert information from humans, and discover that a method that learns to predict which frame is misclassified gives the best performance. We approach the problem of allocating computational resource in a video processing task. We consider a video processing application in which we combine the outputs from two algorithms so that the budget-limited computationally more expensive algorithm is run in the most useful video frames to maximize processing performance. We model the video frames as a chain graphical model and extend a dynamic programming algorithm to determine on which frames to run the more expensive algorithm. We perform experiments on moving object detection and face detection to demonstrate the effectiveness of our approaches. Finally, we consider an idea for saving computational resources and maintaining program performance. We work on a problem of learning model complexity in latent variable models. Specifically, we learn the latent variable state space complexity in latent support vector machines using group norm regularization. We apply our method to handwritten digit recognition and object detection with deformable part models. Our approach reduces latent variable state size and performs faster inference with similar or better performance.