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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.


Contributor
Date Range
2010 2019


There is a growing interest in the creation of three-dimensional (3D) images and videos due to the growing demand for 3D visual media in commercial markets. A possible solution to produce 3D media files is to convert existing 2D images and videos to 3D. The 2D to 3D conversion methods that estimate the depth map from 2D scenes for 3D reconstruction present an efficient approach to save on the cost of the coding, transmission and storage of 3D visual media in practical applications. Various 2D to 3D conversion methods based on depth maps have been developed using existing image and …

Contributors
Li, Jinjin, Karam, Lina J, Chakrabarti, Chaitali, et al.
Created Date
2010

Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve …

Contributors
Malin, Anna, Papandreou-Suppappola, Antonia, Bliss, Daniel, et al.
Created Date
2013

Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring …

Contributors
Huff, Daniel William, Papandreou-Suppappola, Antonia, Kovvali, Narayan, et al.
Created Date
2013

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

Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design …

Contributors
Xu, Zihan, Cao, Yu, Chakrabarti, Chaitali, et al.
Created Date
2017

Deep learning (DL) has proved itself be one of the most important developements till date with far reaching impacts in numerous fields like robotics, computer vision, surveillance, speech processing, machine translation, finance, etc. They are now widely used for countless applications because of their ability to generalize real world data, robustness to noise in previously unseen data and high inference accuracy. With the ability to learn useful features from raw sensor data, deep learning algorithms have out-performed tradinal AI algorithms and pushed the boundaries of what can be achieved with AI. In this work, we demonstrate the power of deep …

Contributors
Mohanty, Abinash, Cao, Yu, Seo, Jae-sun, et al.
Created Date
2018

With the end of Dennard scaling and Moore's law, architects have moved towards heterogeneous designs consisting of specialized cores to achieve higher performance and energy efficiency for a target application domain. Applications of linear algebra are ubiquitous in the field of scientific computing, machine learning, statistics, etc. with matrix computations being fundamental to these linear algebra based solutions. Design of multiple dense (or sparse) matrix computation routines on the same platform is quite challenging. Added to the complexity is the fact that dense and sparse matrix computations have large differences in their storage and access patterns and are difficult to …

Contributors
Animesh, Saurabh, Chakrabarti, Chaitali, Brunhaver, John, et al.
Created Date
2018

Process variations have become increasingly important for scaled technologies starting at 45nm. The increased variations are primarily due to random dopant fluctuations, line-edge roughness and oxide thickness fluctuation. These variations greatly impact all aspects of circuit performance and pose a grand challenge to future robust IC design. To improve robustness, efficient methodology is required that considers effect of variations in the design flow. Analyzing timing variability of complex circuits with HSPICE simulations is very time consuming. This thesis proposes an analytical model to predict variability in CMOS circuits that is quick and accurate. There are several analytical models to estimate …

Contributors
Gummalla, Samatha, Chakrabarti, Chaitali, Cao, Yu, et al.
Created Date
2011

Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. While deep neural network (DNN) implementation of these systems have very good performance, they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost of keyword detection and speech recognition networks (or DNNs) are presented. The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the accuracy. Experiments conducted on the …

Contributors
Arunachalam, Sairam, Chakrabarti, Chaitali, Seo, Jae-sun, et al.
Created Date
2016

Negative bias temperature instability (NBTI) is a leading aging mechanism in modern digital and analog circuits. Recent NBTI data exhibits an excessive amount of randomness and fast recovery, which are difficult to be handled by conventional power-law model (tn). Such discrepancies further pose the challenge on long-term reliability prediction under statistical variations and Dynamic Voltage Scaling (DVS) in real circuit operation. To overcome these barriers, the modeling effort in this work (1) practically explains the aging statistics due to randomness in number of traps with log(t) model, accurately predicting the mean and variance shift; (2) proposes cycle-to-cycle model (from the …

Contributors
Velamala, Jyothi Bhaskarr Amarnadh, Cao, Yu, Clark, Lawrence, et al.
Created Date
2012

In recent years, we have observed the prevalence of stream applications in many embedded domains. Stream programs distinguish themselves from traditional sequential programming languages through well defined independent actors, explicit data communication, and stable code/data access patterns. In order to achieve high performance and low power, scratch pad memory (SPM) has been introduced in today's embedded multicore processors. Current design frameworks for developing stream applications on SPM enhanced embedded architectures typically do not include a compiler that can perform automatic partitioning, mapping and scheduling under limited on-chip SPM capacities and memory access delays. Consequently, many designs are implemented manually, which …

Contributors
Che, Weijia, Chatha, Karam Singh, Chatha, Karam Singh, et al.
Created Date
2012

We are expecting hundreds of cores per chip in the near future. However, scaling the memory architecture in manycore architectures becomes a major challenge. Cache coherence provides a single image of memory at any time in execution to all the cores, yet coherent cache architectures are believed will not scale to hundreds and thousands of cores. In addition, caches and coherence logic already take 20-50% of the total power consumption of the processor and 30-60% of die area. Therefore, a more scalable architecture is needed for manycore architectures. Software Managed Manycore (SMM) architectures emerge as a solution. They have scalable …

Contributors
Bai, Ke, Shrivastava, Aviral, Chatha, Karamvir, et al.
Created Date
2014

In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the …

Contributors
Kulkarni, Kuldeep Sharad, Turaga, Pavan, Li, Baoxin, et al.
Created Date
2017

Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity. Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for …

Contributors
Gupta, Ujjwal, Ogras, Umit Y., Ozev, Sule, et al.
Created Date
2014

Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging. The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions …

Contributors
Shah, Mohit, Spanias, Andreas, Chakrabarti, Chaitali, et al.
Created Date
2015

Over the past few decades, the silicon complementary-metal-oxide-semiconductor (CMOS) technology has been greatly scaled down to achieve higher performance, density and lower power consumption. As the device dimension is approaching its fundamental physical limit, there is an increasing demand for exploration of emerging devices with distinct operating principles from conventional CMOS. In recent years, many efforts have been devoted in the research of next-generation emerging non-volatile memory (eNVM) technologies, such as resistive random access memory (RRAM) and phase change memory (PCM), to replace conventional digital memories (e.g. SRAM) for implementation of synapses in large-scale neuromorphic computing systems. Essentially being compact …

Contributors
Chen, Pai-Yu, Yu, Shimeng, Cao, Yu, et al.
Created Date
2018

Memory systems are becoming increasingly error-prone, and thus guaranteeing their reliability is a major challenge. In this dissertation, new techniques to improve the reliability of both 2D and 3D dynamic random access memory (DRAM) systems are presented. The proposed schemes have higher reliability than current systems but with lower power, better performance and lower hardware cost. First, a low overhead solution that improves the reliability of commodity DRAM systems with no change in the existing memory architecture is presented. Specifically, five erasure and error correction (E-ECC) schemes are proposed that provide at least Chipkill-Correct protection for x4 (Schemes 1, 2 …

Contributors
Chen, Hsing-Min, Chakrabarti, Chaitali, Mudge, Trevor, et al.
Created Date
2017

Historically, wireless communication devices have been developed to process one specific waveform. In contrast, a modern cellular phone supports multiple waveforms corresponding to LTE, WCDMA(3G) and 2G standards. The selection of the network is controlled by software running on a general purpose processor, not by the user. Now, instead of selecting from a set of complete radios as in software controlled radio, what if the software could select the building blocks based on the user needs. This is the new software-defined flexible radio which would enable users to construct wireless systems that fit their needs, rather than forcing to use …

Contributors
Chagari, Vamsi Reddy, Chakrabarti, Chaitali, Lee, Hyunseok, et al.
Created Date
2016

When one considers the current state of wireless communications, it becomes clear that it is both absolutely amazing and something of a mess. Present communications standards are the result of local optimizations over time that led to a confusing set of suboptimal and fragile wireless standards. Starting from a clean sheet of paper, Bliss Laboratory for Information, Signals, and Systems (BLISS) is considering a fluid set of communications standards co-optimized with flexible but power-efficient computational implementations that will enable the next revolution of wireless communications. The main aim is to enable much higher data rates and much lower data rates …

Contributors
Rupakula, Venkata Sai Karteek, Bliss, Daniel W, Chakrabarti, Chaitali, et al.
Created Date
2017

This thesis work present the simulation of Bluetooth and Wi-Fi radios in real life interference environments. When information is transmitted via communication channels, data may get corrupted due to noise and other channel discrepancies. In order to receive the information safely and correctly, error correction coding schemes are generally employed during the design of communication systems. Usually the simulations of wireless communication systems are done in such a way that they focus on some aspect of communications and neglect the others. The simulators available currently will either do network layer simulations or physical layer level simulations. In many situations, simulations …

Contributors
Nolastname, Ujjwala, Bliss, Daniel W., Chakrabarti, Chaitali, et al.
Created Date
2017