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.
Displaying 1 - 10 of 14
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- Creators: Spanias, Andreas
Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The field of education has been immensely benefited by major breakthroughs in technology. The arrival of computers and the internet made student-teacher interaction from different parts of the world viable, increasing the reach of the educator to hitherto remote corners of the world. The arrival of mobile phones in the recent past has the potential to provide the next paradigm shift in the way education is conducted. It combines the universal reach and powerful visualization capabilities of the computer with intimacy and portability. Engineering education is a field which can exploit the benefits of mobile devices to enhance learning and spread essential technical know-how to different parts of the world. In this thesis, I present AJDSP, an Android application evolved from JDSP, providing an intuitive and a easy to use environment for signal processing education. AJDSP is a graphical programming laboratory for digital signal processing developed for the Android platform. It is designed to provide utility; both as a supplement to traditional classroom learning and as a tool for self-learning. The architecture of AJDSP is based on the Model-View-Controller paradigm optimized for the Android platform. The extensive set of function modules cover a wide range of basic signal processing areas such as convolution, fast Fourier transform, z transform and filter design. The simple and intuitive user interface inspired from iJDSP is designed to facilitate ease of navigation and to provide the user with an intimate learning environment. Rich visualizations necessary to understand mathematically intensive signal processing algorithms have been incorporated into the software. Interactive demonstrations boosting student understanding of concepts like convolution and the relation between different signal domains have also been developed. A set of detailed assessments to evaluate the application has been conducted for graduate and senior-level undergraduate students.
ContributorsRanganath, Suhas (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has shown many promising applications in the realm of SAR signal processing, specifically because of its close association to the Wigner distribution and ambiguity function. The objective of this work is to improve the application of the FRFT in order to enhance the implementation of the CSA for SAR processing. This will be achieved by processing real phase-history data from the RADARSAT-1 satellite, a multi-mode SAR platform operating in the C-band, providing imagery with resolution between 8 and 100 meters at incidence angles of 10 through 59 degrees. The phase-history data will be processed into imagery using the conventional chirp scaling algorithm. The results will then be compared using a new implementation of the CSA based on the use of the FRFT, combined with traditional SAR focusing techniques, to enhance the algorithm's focusing ability, thereby increasing the peak-to-sidelobe ratio of the focused targets. The FRFT can also be used to provide focusing enhancements at extended ranges.
ContributorsNorthrop, Judith (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Spanias, Andreas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2011
Description
The ease of use of mobile devices and tablets by students has generated a lot of interest in the area of engineering education. By using mobile technologies in signal analysis and applied mathematics, undergraduate-level courses can broaden the scope and effectiveness of technical education in classrooms. The current mobile devices have abundant memory and powerful processors, in addition to providing interactive interfaces. Therefore, these devices can support the implementation of non-trivial signal processing algorithms. Several existing visual programming environments such as Java Digital Signal Processing (J-DSP), are built using the platform-independent infrastructure of Java applets. These enable students to perform signal-processing exercises over the Internet. However, some mobile devices do not support Java applets. Furthermore, mobile simulation environments rely heavily on establishing robust Internet connections with a remote server where the processing is performed. The interactive Java Digital Signal Processing tool (iJDSP) has been developed as graphical mobile app on iOS devices (iPads, iPhones and iPod touches). In contrast to existing mobile applications, iJDSP has the ability to execute simulations directly on the mobile devices, and is a completely stand-alone application. In addition to a substantial set of signal processing algorithms, iJDSP has a highly interactive graphical interface where block diagrams can be constructed using a simple drag-n-drop procedure. Functions such as visualization of the convolution operation, and an interface to wireless sensors have been developed. The convolution module animates the process of the continuous and discrete convolution operations, including time-shift and integration, so that users can observe and learn, intuitively. The current set of DSP functions in the application enables students to perform simulation exercises on continuous and discrete convolution, z-transform, filter design and the Fast Fourier Transform (FFT). The interface to wireless sensors in iJDSP allows users to import data from wireless sensor networks, and use the rich suite of functions in iJDSP for data processing. This allows users to perform operations such as localization, activity detection and data fusion. The exercises and the iJDSP application were evaluated by senior-level students at Arizona State University (ASU), and the results of those assessments are analyzed and reported in this thesis.
ContributorsHu, Shuang (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Kostas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction.
We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems.
In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.
We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems.
In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.
ContributorsSong, Huan (Author) / Spanias, Andreas (Thesis advisor) / Thiagarajan, Jayaraman (Committee member) / Berisha, Visar (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2018
Description
The research on the topology and dynamics of complex networks is one of the most focused area in complex system science. The goals are to structure our understanding of the real-world social, economical, technological, and biological systems in the aspect of networks consisting a large number of interacting units and to develop corresponding detection, prediction, and control strategies. In this highly interdisciplinary field, my research mainly concentrates on universal estimation schemes, physical controllability, as well as mechanisms behind extreme events and cascading failure for complex networked systems.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
ContributorsChen, Yuzhong (Author) / Lai, Ying-Cheng (Thesis advisor) / Spanias, Andreas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2016
Description
For a sensor array, part of its elements may fail to work due to hardware failures. Then the missing data may distort in the beam pattern or decrease the accuracy of direction-of-arrival (DOA) estimation. Therefore, considerable research has been conducted to develop algorithms that can estimate the missing signal information. On the other hand, through those algorithms, array elements can also be selectively turned off while the missed information can be successfully recovered, which will save power consumption and hardware cost.
Conventional approaches focusing on array element failures are mainly based on interpolation or sequential learning algorithm. Both of them rely heavily on some prior knowledge such as the information of the failures or a training dataset without missing data. In addition, since most of the existing approaches are developed for DOA estimation, their recovery target is usually the co-variance matrix but not the signal matrix.
In this thesis, a new signal recovery method based on matrix completion (MC) theory is introduced. It aims to directly refill the absent entries in the signal matrix without any prior knowledge. We proposed a novel overlapping reshaping method to satisfy the applying conditions of MC algorithms. Compared to other existing MC based approaches, our proposed method can provide us higher probability of successful recovery. The thesis describes the principle of the algorithms and analyzes the performance of this method. A few application examples with simulation results are also provided.
Conventional approaches focusing on array element failures are mainly based on interpolation or sequential learning algorithm. Both of them rely heavily on some prior knowledge such as the information of the failures or a training dataset without missing data. In addition, since most of the existing approaches are developed for DOA estimation, their recovery target is usually the co-variance matrix but not the signal matrix.
In this thesis, a new signal recovery method based on matrix completion (MC) theory is introduced. It aims to directly refill the absent entries in the signal matrix without any prior knowledge. We proposed a novel overlapping reshaping method to satisfy the applying conditions of MC algorithms. Compared to other existing MC based approaches, our proposed method can provide us higher probability of successful recovery. The thesis describes the principle of the algorithms and analyzes the performance of this method. A few application examples with simulation results are also provided.
ContributorsFan, Jie (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2016
Description
Dealing with relational data structures is central to a wide-range of applications including social networks, epidemic modeling, molecular chemistry, medicine, energy distribution, and transportation. Machine learning models that can exploit the inherent structural/relational bias in the graph structured data have gained prominence in recent times. A recurring idea that appears in all approaches is to encode the nodes in the graph (or the entire graph) as low-dimensional vectors also known as embeddings, prior to carrying out downstream task-specific learning. It is crucial to eliminate hand-crafted features and instead directly incorporate the structural inductive bias into the deep learning architectures.
In this dissertation, deep learning models that directly operate on graph structured data are proposed for effective representation learning. A literature review on existing graph representation learning is provided in the beginning of the dissertation. The primary focus of dissertation is on building novel graph neural network architectures that are robust against adversarial attacks. The proposed graph neural network models are extended to multiplex graphs (heterogeneous graphs). Finally, a relational neural network model is proposed to operate on a human structural connectome. For every research contribution of this dissertation, several empirical studies are conducted on benchmark datasets. The proposed graph neural network models, approaches, and architectures demonstrate significant performance improvements in comparison to the existing state-of-the-art graph embedding strategies.
ContributorsShanthamallu, Uday Shankar (Author) / Spanias, Andreas (Thesis advisor) / Thiagarajan, Jayaraman J (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2021