THE OHIO STATE UNIVERSITY GRADUATE SCHOOL NAME: Flake, Layne R. QUARTER/YEAR: SP 95 DEPARTMENT: Electrical Engineering DEGREE: M. S. ADVISER'S NAME: Krishnamurthy, Ashok K. and Ahalt, Stanley C. TITLE OF THESIS: Hidden Markov Models for Multi-aperture SAR Target Detection This thesis proposes a new multi-aperture synthetic aperture radar (MASAR) automatic target detection (ATD) algorithm that uses hidden Markov models (HMMs) to exploit the anisotropic nature of radar returns from man-made objects. Specific HMM structures are developed to represent target and clutter pixels based on the way their radar returns vary at different aspect angles. The HMM ATD algorithm is subjected to a preliminary evaluation using simulated MASAR imagery. The HMM ATD algorithm displays better detection accuracy than the best alternative ATD method while requiring at least two orders of magnitude less calculations. Adviser's Signature Department of Electrical Engineering Acknowledgments I thank my adviser...

In this study we evaluate different HMM topologies in terms of recognition of handwritten numeral strings by considering the framework of the Level Building Algorithm (LBA). By including an end-state in a left-to-right HMM structure we observe a significant improvement in the string recognition performance since it provides a better definition of the segmentation cuts by the LBA. In addition, this end-state allows us the use of a two-step training mechanism with the objective of integrating handwriting-specific knowledge into the numeral models to obtain a more accurate representation of numeral strings. The contextual information regarding the interaction between adjacent numerals in strings (spaces, overlapping and touching) is modeled in a pause model built into the numeral HMMs. This has shown to be a promising approach even though it is really dependent on the training database.

In this paper we introduce a constrained Level Building Algorithm (LBA) in order to reduce the search space of a Large Vocabulary Handwritten Word Recognition (LVHWR) system. A time and a length constraint are introduced to limit the number of frames and the number of levels of the LBA respectively. A regression model that fits the response variables, namely, accuracy and speed, to a non--linear function of the constraints is proposed and a statistical experimental design technique is employed to analyse the effects of the two constraints on the responses. Experimental results prove that the inclusion of these constraints improve the recognition speed of the LVHWR system without changing the recognition rate significantly.

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Hindi is very complex language with large number of phonemes and being used with various ascents in different regions in India. In this manuscript, speaker dependent and independent isolated Hindi word recognizers using the Hidden Markov Model (HMM) is implemented, under noisy environment. For this study, a set of 10 Hindi names has been chosen as a test set for which the training and testing is performed. The scheme instigated here implements the Mel Frequency Cepstral Coefficients (MFCC) in order to compute the acoustic features of the speech signal. Then, K-means algorithm is used for the codebook generation by performing clustering over the obtained feature space. Baum Welch algorithm is used for re-estimating the parameters, and finally for deciding the recognized Hindi word whose model likelihood is highest, Viterbi algorithm has been implemented; for the given HMM. This work resulted in successful recognition with 98.6 % recognition rate for speaker dependent recognition, for total of 10 speakers (6 male, 4 female) and 97.5 % for speaker independent isolated word recognizer for 10 speakers (male).