UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

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 given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2010 - 20122

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    Estimation of Mixed Distributions on Vehicular Traffic Measurements using the Bluetooth Technology
    (2012) Zoto, Jorgos; La, Richard; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this work we build on the idea of using Bluetooth® sensors as a new intelligent transportation system application of estimating travel time along a section of a highway. Given the existence of High Occupancy Vehicle (HOV) lanes and Express lanes in the U.S highway network, a mixed population estimation problem naturally arises. This estimation problem is attacked from three dierent perspectives: (i) in light of the Expectation Maximization (EM) algorithm, (ii) using Maximum Likelihood Estimation (MLE) techniques and nally (iii) applying a cluster-separation approach to our mixed dataset. The robust performance of the rst approach leads to an EM-inspired MLE technique, a hybrid of (i) and (ii) which combines the good estimation accuracy of EM based algorithms and the lower complexity of MLE techniques. The limitations and performance of all four approaches are tested on actual vehicular data on different highway segments in two dierent U.S states. The superiority of the hybrid approach is shown along with it's limitations.
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    Maximum Likelihood Pitch Estimation Using Sinusoidal Modeling
    (2010) Mahadevan, Vijay; Espy-Wilson, Carol Y; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The aim of the work presented in this thesis is to automatically extract the fundamental frequency of a periodic signal from noisy observations, a task commonly referred to as pitch estimation. An algorithm for optimal pitch estimation using a maximum likelihood formulation is presented. The speech waveform is modeled using sinusoidal basis functions that are harmonically tied together to explicitly capture the periodic structure of voiced speech. The problem of pitch estimation is casted as a model selection problem and the Akaike Information Criterion is used to estimate the pitch. The algorithm is compared with several existing pitch detection algorithms (PDAs) on a reference pitch database. The results indicate the superior performance of the algorithm in comparison with most of the PDAs. The application of parametric modeling in single channel speech segregation and the use of mel-frequency cepstral coefficients for sequential grouping are analyzed in the speech separation challenge database.