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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.




Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An …

Contributors
Edla, Shwetha Reddy, Papandreou-Suppappola, Antonia, Chakrabarti, Chaitali, et al.
Created Date
2012

A critical problem for airborne, ship board, and land based radars operating in maritime or littoral environments is the detection, identification and tracking of targets against backscattering caused by the roughness of the sea surface. Statistical models, such as the compound K-distribution (CKD), were shown to accurately describe two separate structures of the sea clutter intensity fluctuations. The first structure is the texture that is associated with long sea waves and exhibits long temporal decorrelation period. The second structure is the speckle that accounts for reflections from multiple scatters and exhibits a short temporal decorrelation period from pulse to pulse. …

Contributors
Northrop, Judith, Papandreou-Suppappola, Antonia, Chakrabarti, Chaitali, et al.
Created Date
2019

Thousands of high-resolution images are generated each day. Segmenting, classifying, and analyzing the contents of these images are the key steps in image understanding. This thesis focuses on image segmentation and classification and its applications in synthetic, texture, natural, biomedical, and industrial images. A robust level-set-based multi-region and texture image segmentation approach is proposed in this thesis to tackle most of the challenges in the existing multi-region segmentation methods, including computational complexity and sensitivity to initialization. Medical image analysis helps in understanding biological processes and disease pathologies. In this thesis, two cell evolution analysis schemes are proposed for cell cluster …

Contributors
Said, Asaad F., Karam, Lina, Chakrabarti, Chaitali, et al.
Created Date
2010

Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images. The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to …

Contributors
Xu, Qian, Karam, Lina J, Chakrabarti, Chaitali, et al.
Created Date
2014

Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the …

Contributors
Varadarajan, Srenivas, Karam, Lina J, Chakrabarti, Chaitali, et al.
Created Date
2014

Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they …

Contributors
Ravichandran, Lakshminarayan, Papandreou-Suppappola, Antonia, Spanias, Andreas S, et al.
Created Date
2011