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
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item HIGH ACCELERATIONS PRODUCED THROUGH SECONDARY IMPACT AND ITS EFFECT ON RELIABILITY OF PRINTED WIRING ASSEMBLIES(2010) Douglas, Stuart Taylor; Dasgupta, Abhijit; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The focus of this thesis is the investigation of extremely high accelerations through secondary impact and its effect on reliability of printed wiring assemblies. The test equipment consists of a commercially available drop system and a commercially available attachment termed a Dual Mass Shock Amplifier (DMSA), which extends the impact acceleration range to as much as 30,000 Gs by utilizing secondary impact dynamics. Further secondary impacts between the test vehicle and fixture are intentionally generated in simulation and tested experimentally to imitate board 'slap' phenomena in product assemblies, and to generate even further amplification of the acceleration at various locations on the test specimen. In this thesis a detailed description of the test equipment and modeling techniques are provided. Model complexity ranges from simple analytic closed-form rigid-body mechanics to detailed nonlinear dynamic finite element analysis. The effects of different equipment design parameters (table mass, spring stiffness, table clearance) are investigated through parametric modeling. The effects of contact parameters (constraint enforcement algorithms, stiffness, damping) on model accuracy are explored. Test fixtures for high shock accelerations are discussed and used for board level reliability testing of printed wire assemblies containing WLCSP49s and MEMS microphones.Item Properties of a DTN Packet Forwarding Scheme Inspired By Themodynamics(2010) Mathew, Bipin; La, Richard J.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we develop a discrete time model of a recently proposed algorithm, inspired by thermodynamics, for message routing in Disruption Tolerant Networks (DTNs). We model the evolution of the temperature at the nodes as a stochastic switched linear system and show that the temperatures converge in distribution to a unique stationary distribution that is independent of initial conditions. The proof of this result borrows tools from Iterated Random Maps (IRMs) and Queuing theory. Lastly, we simulate the proposed algorithm, using a variety of mobility models, in order to observe the performance of the algorithm under various conditions.Item DISTRIBUTED ESTIMATION OVER NETWORKS WITH COMMUNICATION COSTS(2010) Lipsa, Gabriel-Mihai; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We analyze how distributed or decentralized estimation can be performed over networks, when there is a price to be paid whenever nodes in the network communicate with each other. The work here has application especially in the network control systems. Assume that different nodes in the network can track perfectly or with imperfectly some stochastic processes, while other nodes in the network need to estimate these stochastic processes. The nodes which can observe the stochastic processes can send information directly to the nodes which need to estimate the processes, or information can be sent to intermediate nodes. When each transmission is performed a cost for communication is paid. The goal of the network is to optimize jointly a cost which consists both of a function of the estimation error and a function of the transmission cost. We show here that for some simple topologies the decision to send information over the network is a threshold policy, while the estimators are linear estimators which resemble with the Kalman-filter. For the result dealing with simple topologies we have proved the results using majorization theory. It is also shown here both analytically and numerically that things can immediately become quite complicated. If we take into consideration multidimensional problems or problems with multiple agents and/or transmission noise, the optimal strategies can no longer be found analytically and it can be quite difficult to compute numerically the optimal strategies.Item Activity Representation from Video Using Statistical Models on Shape Manifolds(2010) Abdelkader, Mohamed F.; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Activity recognition from video data is a key computer vision problem with applications in surveillance, elderly care, etc. This problem is associated with modeling a representative shape which contains significant information about the underlying activity. In this dissertation, we represent several approaches for view-invariant activity recognition via modeling shapes on various shape spaces and Riemannian manifolds. The first two parts of this dissertation deal with activity modeling and recognition using tracks of landmark feature points. The motion trajectories of points extracted from objects involved in the activity are used to build deformation shape models for each activity, and these models are used for classification and detection of unusual activities. In the first part of the dissertation, these models are represented by the recovered 3D deformation basis shapes corresponding to the activity using a non-rigid structure from motion formulation. We use a theory for estimating the amount of deformation for these models from the visual data. We study the special case of ground plane activities in detail because of its importance in video surveillance applications. In the second part of the dissertation, we propose to model the activity by learning an affine invariant deformation subspace representation that captures the space of possible body poses associated with the activity. These subspaces can be viewed as points on a Grassmann manifold. We propose several statistical classification models on Grassmann manifold that capture the statistical variations of the shape data while following the intrinsic Riemannian geometry of these manifolds. The last part of this dissertation addresses the problem of recognizing human gestures from silhouette images. We represent a human gesture as a temporal sequence of human poses, each characterized by a contour of the associated human silhouette. The shape of a contour is viewed as a point on the shape space of closed curves and, hence, each gesture is characterized and modeled as a trajectory on this shape space. We utilize the Riemannian geometry of this space to propose a template-based and a graphical-based approaches for modeling these trajectories. The two models are designed in such a way to account for the different invariance requirements in gesture recognition, and also capture the statistical variations associated with the contour data.Item Novel Integrated System Architecture for an Autonomous Jumping Micro-Robot(2010) Churaman, Wayne Anthony; Goldsman, Neil; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As the capability and complexity of robotic platforms continue to evolve from the macro to micro-scale, innovation of such systems is driven by the notion that a robot must be able to sense, think, and act [1]. The traditional architecture of a robotic platform consists of a structural layer upon which, actuators, controls, power, and communication modules are integrated for optimal system performance. The structural layer, for many micro-scale platforms, has commonly been implemented using a silicon die, thus leading to robotic platforms referred to as "walking chips" [2]. In this thesis, the first-ever jumping microrobotic platform is demonstrated using a hybrid integration approach to assemble on-board sensing and power directly onto a polymer chassis. The microrobot detects a change in light intensity and ignites 0.21mg of integrated nanoporous energetic silicon, resulting in 246µJ of kinetic energy and a vertical jump height of 8cm.Item Capacity Bounds For Multi-User Channels With Feedback, Relaying and Cooperation(2010) Tandon, Ravi; Ulukus, Sennur; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent developments in communications are driven by the goal of achieving high data rates for wireless communication devices. To achieve this goal, several new phenomena need to be investigated from an information theoretic perspective. In this dissertation, we focus on three of these phenomena: feedback, relaying and cooperation. We study these phenomena for various multi-user channels from an information theoretic point of view. One of the aims of this dissertation is to study the performance limits of simple wireless networks, for various forms of feedback and cooperation. Consider an uplink communication system, where several users wish to transmit independent data to a base-station. If the base-station can send feedback to the users, one can expect to achieve higher data-rates since feedback can enable cooperation among the users. Another way to improve data-rates is to make use of the broadcast nature of the wireless medium, where the users can overhear each other's transmitted signals. This particular phenomenon has garnered much attention lately, where users can help in increasing each other's data-rates by utilizing the overheard information. This overheard information can be interpreted as a generalized form of feedback. To take these several models of feedback and cooperation into account, we study the two-user multiple access channel and the two-user interference channel with generalized feedback. For all these models, we derive new outer bounds on their capacity regions. We specialize these results for noiseless feedback, additive noisy feedback and user-cooperation models and show strict improvements over the previously known bounds. Next, we study state-dependent channels with rate-limited state information to the receiver or to the transmitter. This state-dependent channel models a practical situation of fading, where the fade information is partially available to the receiver or to the transmitter. We derive new bounds on the capacity of such channels and obtain capacity results for a special sub-class of such channels. We study the effect of relaying by considering the parallel relay network, also known as the diamond channel. The parallel relay network considered in this dissertation comprises of a cascade of a general broadcast channel to the relays and an orthogonal multiple access channel from the relays to the receiver. We characterize the capacity of the diamond channel, when the broadcast channel is deterministic. We also study the diamond channel with partially separated relays, and obtain capacity results when the broadcast channel is either semi-deterministic or physically degraded. Our results also demonstrate that feedback to the relays can strictly increase the capacity of the diamond channel. In several sensor network applications, distributed lossless compression of sources is of considerable interest. The presence of adversarial nodes makes it important to design compression schemes which serve the dual purpose of reliable source transmission to legitimate nodes while minimizing the information leakage to the adversarial nodes. Taking this constraint into account, we consider information theoretic secrecy, where our aim is to limit the information leakage to the eavesdropper. For this purpose, we study a secure source coding problem with coded side information from a helper to the legitimate user. We derive the rate-equivocation region for this problem. We show that the helper node serves the dual purpose of reducing the source transmission rate and increasing the uncertainty at the adversarial node. Next, we considered two different secure source coding models and provide the corresponding rate-equivocation regions.Item Sparse and Redundant Representations for Inverse Problems and Recognition(2010) Patel, Vishal M.; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.Item Low Power Adaptive Circuits: An Adaptive Log Domain Filter and A Low Power Temperature Insensitive Oscillator Applied in Smart Dust Radio(2010) Zhai, Yiming; Goldsman, Neil; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on exploring two low power adaptive circuits. One is an adaptive filter at audio frequency for system identification. The other is a temperature insensitive oscillator for low power radio frequency communication. The adaptive filter is presented with integrated learning rules for model reference estimation. The system is a first order low pass filter with two parameters: gain and cut-off frequency. It is implemented using multiple input floating gate transistors to realize online learning of system parameters. Adaptive dynamical system theory is used to derive robust control laws in a system identification task. Simulation results show that convergence is slower using simplified control laws but still occurs within milliseconds. Experimental results confirm that the estimated gain and cut-off frequency track the corresponding parameters of the reference filter. During operation, deterministic errors are introduced by mismatch within the analog circuit implementation. An analysis is presented which attributes the errors to current mirror mismatch. The harmonic distortion of the filter operating in different inversion is analyzed using EKV model numerically. The temperature insensitive oscillator is designed for a low power wireless network. The system is based on a current starved ring oscillator implemented using CMOS transistors instead of LC tank for less chip area and power consumption. The frequency variance with temperature is compensated by the temperature adaptive circuits. Experimental results show that the frequency stability from 5°C to 65°C has been improved 10 times with automatic compensation and at least 1 order less power is consumed than published competitors. This oscillator is applied in a 2.2GHz OOK transmitter and a 2.2GHz phase locked loop based FM receiver. With the increasing needs of compact antenna, possible high data rate and wide unused frequency range of short distance communication, a higher frequency phase locked loop used for BFSK receiver is explored using an LC oscillator for its capability at 20GHz. The success of frequency demodulation is demonstrated in the simulation results that the PLL can lock in 0.5μs with 35MHz lock-in range and 2MHz detection resolution. The model of a phase locked loop used for BFSK receiver is analyzed using Matlab.Item DEVELOPMENT OF A SIMPLIFIED, MASS PRODUCIBLE HYBRIDIZED AMBIENT, LOW FREQUENCY, LOW INTENSITY VIBRATION ENERGY SCAVENGER (HALF-LIVES)(2010) Khbeis, Michael Tawfik; Ghodssi, Reza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Scavenging energy from environmental sources is an active area of research to enable remote sensing and microsystems applications. Furthermore, as energy demands soar, there is a significant need to explore new sources and curb waste. Vibration energy scavenging is one environmental source for remote applications and a candidate for recouping energy wasted by mechanical sources that can be harnessed to monitor and optimize operation of critical infrastructure (e.g. Smart Grid). Current vibration scavengers are limited by volume and ancillary requirements for operation such as control circuitry overhead and battery sources. This dissertation, for the first time, reports a mass producible hybrid energy scavenger system that employs both piezoelectric and electrostatic transduction on a common MEMS device. The piezoelectric component provides an inherent feedback signal and pre-charge source that enables electrostatic scavenging operation while the electrostatic device provides the proof mass that enables low frequency operation. The piezoelectric beam forms the spring of the resonant mass-spring transducer for converting vibration excitation into an AC electrical output. A serially poled, composite shim, piezoelectric bimorph produces the highest output rectified voltage of over 3.3V and power output of 145uW using ¼ g vibration acceleration at 120Hz. Considering solely the volume of the piezoelectric beam and tungsten proof mass, the volume is 0.054cm3, resulting in a power density of 2.68mW/cm3. Incorporation of a simple parallel plate structure that provides the proof mass for low frequency resonant operation in addition to cogeneration via electrostatic energy scavenging provides a 19.82 to 35.29 percent increase in voltage beyond the piezoelectric generated DC rails. This corresponds to approximately 2.1nW additional power from the electrostatic scavenger component and demonstrates the first instance of hybrid energy scavenging using both piezoelectric and synchronous electrostatic transduction. Furthermore, it provides a complete system architecture and development platform for additional enhancements that will enable in excess of 100uW additional power from the electrostatic scavenger.Item Video Processing with Additional Information(2010) Ramachandran, Mahesh; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cameras are frequently deployed along with many additional sensors in aerial and ground-based platforms. Many video datasets have metadata containing measurements from inertial sensors, GPS units, etc. Hence the development of better video processing algorithms using additional information attains special significance. We first describe an intensity-based algorithm for stabilizing low resolution and low quality aerial videos. The primary contribution is the idea of minimizing the discrepancy in the intensity of selected pixels between two images. This is an application of inverse compositional alignment for registering images of low resolution and low quality, for which minimizing the intensity difference over salient pixels with high gradients results in faster and better convergence than when using all the pixels. Secondly, we describe a feature-based method for stabilization of aerial videos and segmentation of small moving objects. We use the coherency of background motion to jointly track features through the sequence. This enables accurate tracking of large numbers of features in the presence of repetitive texture, lack of well conditioned feature windows etc. We incorporate the segmentation problem within the joint feature tracking framework and propose the first combined joint-tracking and segmentation algorithm. The proposed approach enables highly accurate tracking, and segmentation of feature tracks that is used in a MAP-MRF framework for obtaining dense pixelwise labeling of the scene. We demonstrate competitive moving object detection in challenging video sequences of the VIVID dataset containing moving vehicles and humans that are small enough to cause background subtraction approaches to fail. Structure from Motion (SfM) has matured to a stage, where the emphasis is on developing fast, scalable and robust algorithms for large reconstruction problems. The availability of additional sensors such as inertial units and GPS along with video cameras motivate the development of SfM algorithms that leverage these additional measurements. In the third part, we study the benefits of the availability of a specific form of additional information - the vertical direction (gravity) and the height of the camera both of which can be conveniently measured using inertial sensors, and a monocular video sequence for 3D urban modeling. We show that in the presence of this information, the SfM equations can be rewritten in a bilinear form. This allows us to derive a fast, robust, and scalable SfM algorithm for large scale applications. The proposed SfM algorithm is experimentally demonstrated to have favorable properties compared to the sparse bundle adjustment algorithm. We provide experimental evidence indicating that the proposed algorithm converges in many cases to solutions with lower error than state-of-art implementations of bundle adjustment. We also demonstrate that for the case of large reconstruction problems, the proposed algorithm takes lesser time to reach its solution compared to bundle adjustment. We also present SfM results using our algorithm on the Google StreetView research dataset, and several other datasets.