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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


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

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

The goal is to provide accurate measurement of the channel between a ground source and a receiving satellite. The effects of the the ionosphere for ground to space propagation for radio waves in the 3-30 MHz HF band is an unstudied subject. The effects of the ionosphere on radio propagation is a long studied subject, the primary focus has been ground to ground by means of ionospheric reflection and space to ground corrections of ionospheric distortions of GPS. Because of the plasma properties of the ionosphere there is a strong dependence on the frequency of use. GPS L1 1575.42 MHz …

Contributors
Standage-Beier, Wylie S, 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

With the new age Internet of Things (IoT) revolution, there is a need to connect a wide range of devices with varying throughput and performance requirements. In this thesis, a wireless system is proposed which is targeted towards very low power, delay insensitive IoT applications with low throughput requirements. The low cost receivers for such devices will have very low complexity, consume very less power and hence will run for several years. Long Term Evolution (LTE) is a standard developed and administered by 3rd Generation Partnership Project (3GPP) for high speed wireless communications for mobile devices. As a part of …

Contributors
Sharma, Prashant, Bliss, Daniel, Chakrabarti, Chaitali, et al.
Created Date
2017

Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power …

Contributors
Gupta, Ujjwal, Ogras, Umit Y., Chakrabarti, Chaitali, et al.
Created Date
2018

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

Deep neural networks (DNN) have shown tremendous success in various cognitive tasks, such as image classification, speech recognition, etc. However, their usage on resource-constrained edge devices has been limited due to high computation and large memory requirement. To overcome these challenges, recent works have extensively investigated model compression techniques such as element-wise sparsity, structured sparsity and quantization. While most of these works have applied these compression techniques in isolation, there have been very few studies on application of quantization and structured sparsity together on a DNN model. This thesis co-optimizes structured sparsity and quantization constraints on DNN models during training. …

Contributors
Srivastava, Gaurav, Seo, Jae-Sun, Chakrabarti, Chaitali, et al.
Created Date
2018

In recent years, conventional convolutional neural network (CNN) has achieved outstanding performance in image and speech processing applications. Unfortunately, the pooling operation in CNN ignores important spatial information which is an important attribute in many applications. The recently proposed capsule network retains spatial information and improves the capabilities of traditional CNN. It uses capsules to describe features in multiple dimensions and dynamic routing to increase the statistical stability of the network. In this work, we first use capsule network for overlapping digit recognition problem. We evaluate the performance of the network with respect to recognition accuracy, convergence and training time …

Contributors
XIONG, YAN, Chakrabarti, Chaitali, Berisha, Visar, et al.
Created Date
2018

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

Articial Neural Network(ANN) has become a for-bearer in the field of Articial Intel- ligence. The innovations in ANN has led to ground breaking technological advances like self-driving vehicles,medical diagnosis,speech Processing,personal assistants and many more. These were inspired by evolution and working of our brains. Similar to how our brain evolved using a combination of epigenetics and live stimulus,ANN require training to learn patterns.The training usually requires a lot of computation and memory accesses. To realize these systems in real embedded hardware many Energy/Power/Performance issues needs to be solved. The purpose of this research is to focus on methods to study …

Contributors
Chowdary, Hidayatullah, Cao, Yu, Seo, JaeSun, et al.
Created Date
2018

Medical ultrasound imaging is widely used today because of it being non-invasive and cost-effective. Flow estimation helps in accurate diagnosis of vascular diseases and adds an important dimension to medical ultrasound imaging. Traditionally flow estimation is done using Doppler-based methods which only estimate velocity in the beam direction. Thus when blood vessels are close to being orthogonal to the beam direction, there are large errors in the estimation results. In this dissertation, a low cost blood flow estimation method that does not have the angle dependency of Doppler-based methods, is presented. First, a velocity estimator based on speckle tracking and …

Contributors
WEI, SIYUAN, Chakrabarti, Chaitali, Papandreou-Suppappola, Antonia, 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

Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movements. Many researchers advocate pushing processing close to the sensor to substantially reduce data movements. However, continuous near-sensor processing raises the sensor temperature, impairing the fidelity of imaging/vision tasks. The work characterizes the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. The characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, the characterization also identifies opportunities -- unique to the needs …

Contributors
Kodukula, Venkatesh, LiKamWa, Robert, Chakrabarti, Chaitali, et al.
Created Date
2019

Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess …

Contributors
Deb, Ranadeep, Ogras, Umit Y, Shill, Holly, et al.
Created Date
2019

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

The past decade has seen a tremendous surge in running machine learning (ML) functions on mobile devices, from mere novelty applications to now indispensable features for the next generation of devices. While the mobile platform capabilities range widely, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful CPUs with GPUs to accelerate the computation of deep neural networks (DNNs), which are the most common structures to perform ML operations. But traditional von Neumann architectures are not optimized for the high memory bandwidth …

Contributors
Kadetotad, Deepak Vinayak, Seo, Jae-sun, Chakrabarti, Chaitali, et al.
Created Date
2019

In this paper, the Software Defined Radio (SDR) platform is considered for building a pseudo-monostatic, 100MHz Pulse-Doppler radar. The SDR platform has many benefits for experimental communications systems as it offers relatively cheap, parametrically dynamic, off-the-shelf access to the Radiofrequency (RF) spectrum. For this application, the Universal Software Radio Peripheral (USRP) X310 hardware package is utilized with GNURadio for interfacing to the device and Matlab for signal post- processing. Pulse doppler radar processing is used to ascertain the range and velocity of a target considered in simulation and in real, over-the-air (OTA) experiments. The USRP platform offers a scalable and …

Contributors
Gubash, Gerard Robert, Bliss, Daniel W, Richmond, Christ, et al.
Created Date
2019