Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
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
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
3 results
Search Results
Item MODELING AND OPTIMIZATION TECHNIQUES FOR EFFICIENT IMPLEMENTATION OF PARALLEL EMBEDDED SYSTEMS(2010) GU, RUIRUI; Bhattacharyya, Shuvra S; Levine, William S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Embedded systems are becoming more and more important. The products containing embedded systems span from day-to-day household and consumer products, such as digital TVs, mobile phones, and automobiles, to industrial devices and equipment, including, for example, robots, aviation equipment, and high end military and scientific devices such as aircraft. Previously, because embedded systems were highly limited in computational capability, memory size, and power consumption, much research was dedicated to making the best use of limited system resources. In these works, system performance issues, such as execution time, were traded off with system resources, and resources were carefully scheduled and utilized. With more available computational capability in embedded system devices, and more complicated requirements demanding more intensive computation, the most critical design concerns are changing in some important application domains. In such application areas, researchers are paying more and more attention to improving system execution time, which is also the core topic of our work. Execution time is especially critical to real time systems, in the sense that it is related not only to system performance, but also to system correctness and reliability. Multi-core devices, which incorporate two or more processors on the same integrated circuits, are becoming increasingly relevant to the design and implementation of embedded systems. In multi-core platforms, carefully managing communication and synchronization among different cores is important to achieve efficient implementations. Two or more processing cores sharing the same system bus and memory bandwidth limit the achievable performance improvements. The ability of multi-core processors to increase application performance depends on the use of multiple concurrent tasks within applications. Therefore, if code is written in a form that facilitates decomposition into concurrent tasks, the multi-core technologies can be exploited more effectively. Dataflow-based languages are suitable for such decomposition into concurrent tasks, particularly in the broad domain of digital signal processing (DSP) applications. Dataflow representations of DSP software have been explored actively since the 1980s. Such representations have proved to be useful in identifying bottlenecks in DSP algorithms, improving the efficiency of the computations, and designing appropriate hardware for implementing the algorithms. Dataflow descriptions have been used in a wide range of DSP application areas, such as multimedia processing, and wireless communications. Among various forms of dataflow modeling, synchronous dataflow (SDF) is geared towards static scheduling of computational modules, which improves system performance and predictability. However, many DSP applications do not fully conform to the restrictions of SDF modeling. More general dataflow models, such as CAL, have been developed to describe dynamically-structured DSP applications. Such generalized models can express dynamically changing functionality, but lose the powerful static scheduling capabilities provided by SDF. This thesis explores modeling and optimization techniques for efficient implementation of parallel embedded systems. We propose a dataflow based framework, which covers modeling, analysis and optimization and bridges between user-friendly design and efficient implementation. The framework is applied to two kinds of applications: control systems and video processing systems. Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. An important limitation on the application of MPC is the difficulty in completing the necessary computations within the sampling interval. Recent trends in computing hardware towards greatly increased parallelism offer a solution to this problem. Our work describes modeling and analysis tools to facilitate implementing MPC algorithms on parallel computers, thereby greatly reducing the time needed to complete the calculations. The use of these tools is illustrated by an application to the critical components of an important class of MPC problems, including the Newton-KKT algorithm, the active set method and linear system solvers. This thesis also presents an in-depth case study of dataflow-based analysis and exploitation of parallelism in the design and implementation of an MPEG RVC (reconfigurable video coding) decoder. Because dataflow models are effective in exposing concurrency and other important forms of high level application structure, dataflow techniques are promising for implementing complex DSP applications on multi-core systems, and other kinds of parallel processing platforms. Targeting video processing systems, we use the CAL language as a concrete framework for representing and demonstrating dataflow design techniques. Furthermore, we also analyze our application of the DIF package (TDP), which helps to automatically process regions that are extracted from the original network, and exhibit properties similar to synchronous dataflow (SDF) models. Detection of SDF-like regions is an important step for applying static scheduling techniques within a dynamic dataflow framework. Furthermore, segmenting a system into SDF-like regions also allows us to explore cross-actor concurrency that results from dynamic dependencies among different regions. Using SDF-like region detection as a preprocessing step to software synthesis generally provides an efficient way for mapping tasks to multi-core systems, and improves the system performance of video processing applications on multi-core platforms. Finally the automation from system design to efficient implementation helps our dataflow based modeling and optimization techniques extend into a wide range of embedded applications.Item Collaborative Control of Autonomous Swarms with Resource Constraints(2006-11-24) Xi, Wei; Baras, John; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on the collaborative control of homogeneous UAV swarms. A two-level scheme is proposed by combining the high-level path planning and the lowlevel vehicle motion control. A decentralized artificial potential function (APF) based approach, which mimics the bacteria foraging process, is studied for the high-level path planning. The deterministic potential based approach, however, suffers from the local minima entrapment dilemma, which motivate us to fix the "flaw" that is naturally embedded. An innovative decentralized stochastic approach based on the Markov Random Filed (MRF) theory is proposed; this approach traditionally used in statistical mechanics and in image processing. By modeling the local interactions as Gibbs potentials, the movements of vehicles are then decided by using Gibbs sampler based simulated annealing (SA) algorithm. A two-step sampling scheme is proposed to coordinate vehicle networks: in the first sampling step, a vehicle is picked through a properly designed, configuration-dependent proposal distribution, and in the second sampling step, the vehicle makes a move by using the local characteristics of the Gibbs distribution. Convergence properties are established theoretically and confirmed with simulations. In order to reduce the communication cost and the delay, a fully parallel sampling algorithm is studied and analyzed accordingly. In practice, the stochastic nature of the proposed algorithm might lead to a high traveling cost. To mitigate this problem, a hybrid algorithm is eveloped by combining the Gibbs sampler based method with the deterministic gradient-flow method to gain the advantages of both approaches. The robustness of the Gibbs sampler based algorithm is also studied. The convergence properties are investigated for different types sensor errors including range-error and random-error. Error bounds are derived to guarantee the convergence of the stochastic algorithm. In the low-level motion control module, a model predictive control (MPC) approach is investigated for car-like UAV model. Multiple control objectives, for example, minimizing tracking error, avoiding actuator/state saturation, and minimizing control effort, are easily encoded in the objective function. Two numerical optimization approaches, gradient descendent approach and dynamic programming approach, are studied to strike the balance between computation time and complexity.Item Inferential Model Predictive Control Using Statistical Tools(2005-05-06) Dave, Kedar Himanshu; Mc Avoy, Thomas J; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With an ever increasing emphasis on reducing costs and improving quality control, the application of advanced process control in the bulk chemical and petrochemical industry is steadily rising. Two major areas of development are model-based control strategies and process sensors. This study deals with the application of multivariate statistical techniques for developing soft-sensors in an inferential model predictive control framework. McAvoy (2003) has proposed model predictive statistical process control (MP-SPC), a principal component (PC) score control methodology. MP-SPC was found to be very effective in reducing the variability in the quality variables without using any real-time, on-line quality or disturbance measurements. This work extends McAvoy's formulation to incorporate multiple manipulated variables and demonstrates the controller's performance under different disturbance scenarios and for an additional case study. Moreover, implementation issues critical to the success of the formulations considered such as controller tuning, measurement selection and model identification are also studied. A key feature is the emphasis on confirming the consistency of the cross-correlation between the selected measurements and the quality variable before on-line implementation and that between the scores and the quality variables after on-line implementation. An analysis of the controller's performance in dealing with disturbances of different frequencies, sizes and directions, as well as non-stationarities in the disturbance, reveals the robustness of the approach. The penalty on manipulated variable moves is the most effective tuning parameter. A unique scheme, developed in this study, takes advantage of the information contained in historical databases combined with plant testing to generate collinear PC score models. The proposed measurement selection algorithm ranks measurements that have a consistent cross-correlation with the quality variable according to their cross-correlation coefficient and lead time. Higher ranked variables are chosen as long as they make sufficiently large contributions to the PC score model. Several approaches for identifying dynamic score models are proposed. All approaches put greater emphasis on short term predictions. Two approaches utilize the statistics associated with the PC score models. The Hotelling's statistic and the Q-residual information may be used to remove outliers during pre-processing or may be incorporated as sample weights. The process dynamics and controller performance results presented in this study are simulations based on well-known, industrially benchmarked, test-bed models: the Tennessee Eastman challenge process and the azeotropic distillation tower of the Vinyl Acetate monomer process.