Electrical & Computer Engineering Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2765

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    Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications
    (2017) Kazemipour, Abbas; Wu, Min; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i.d and dynamic compressed sensing by considering such constraints. In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution. Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second.
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    INTEGRATED INPUT MODELING AND MEMORY MANAGEMENT FOR IMAGE PROCESSING APPLICATIONS
    (2005-12-07) Haim, Fiorella; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Image processing applications often demand powerful calculations and real-time performance with low power and energy consumption. Programmable hardware provides inherent parallelism and flexibility making it a good implementation choice for this application domain. In this work we introduce a new modeling technique combining Cyclo-Static Dataflow (CSDF) base model semantics and Homogeneous Parameterized Dataflow (HPDF) meta-modeling framework, which exposes more levels of parallelism than previous models and can be used to reduce buffer sizes. We model two different applications and show how we can achieve efficient scheduling and memory organization, which is crucial for this application domain, since large amounts of data are processed, and storing intermediate results usually requires the use of off-chip resources, causing slower data access and higher power consumption. We also designed a reusable wishbone compliant memory controller module that can be used to access the Xilinx Multimedia Board's memory chips using single accesses or burst mode.