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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.


Subject
Date Range
2011 2019


Visual applications – those that use camera frames as part of the application – provide a rich, context-aware experience. The continued development of mixed and augmented reality (MR/AR) computing environments furthers the richness of this experience by providing applications a continuous vision experience, where visual information continuously provides context for applications and the real world is augmented by the virtual. To understand user privacy concerns in continuous vision computing environments, this work studies three MR/AR applications (augmented markers, augmented faces, and text capture) to show that in a modern mobile system, the typical user is exposed to potential mass collection …

Contributors
Jensen, Jk, LiKamWa, Robert, Doupè, Adam, et al.
Created Date
2019

Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install Global Positioning System(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale …

Contributors
Fu, Zishan, Sarwat, Mohamed, Pedrielli, Giulia, et al.
Created Date
2019

Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods …

Contributors
Catchings, Michael Thomas, Braden, Brittany B, Greger, Bradley, et al.
Created Date
2019

Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving. One of the expectations from fully-automated vehicles is never to cause …

Contributors
Tuncali, Cumhur Erkan, Fainekos, Georgios, Ben Amor, Heni, et al.
Created Date
2019

Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach. In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore and predict students’ …

Contributors
Tian, Wenbo, Hsiao, Ihan, Bazzi, Rida, et al.
Created Date
2019

The Internet of Things ecosystem has spawned a wide variety of embedded real-time systems that complicate the identification and resolution of bugs in software. The methods of concurrent checkpoint provide a means to monitor the application state with the ability to replay the execution on like hardware and software, without holding off and delaying the execution of application threads. In this thesis, it is accomplished by monitoring physical memory of the application using a soft-dirty page tracker and measuring the various types of overhead when employing concurrent checkpointing. The solution presented is an advancement of the Checkpoint and Replay In …

Contributors
Prinke, Michael L, Lee, Yann-Hang, Shrivastava, Aviral, et al.
Created Date
2018

The rapid improvement in computation capability has made deep convolutional neural networks (CNNs) a great success in recent years on many computer vision tasks with significantly improved accuracy. During the inference phase, many applications demand low latency processing of one image with strict power consumption requirement, which reduces the efficiency of GPU and other general-purpose platform, bringing opportunities for specific acceleration hardware, e.g. FPGA, by customizing the digital circuit specific for the deep learning algorithm inference. However, deploying CNNs on portable and embedded systems is still challenging due to large data volume, intensive computation, varying algorithm structures, and frequent memory …

Contributors
Ma, Yufei, Vrudhula, Sarma, Seo, Jae-sun, et al.
Created Date
2018

Advances in semiconductor technology have brought computer-based systems intovirtually all aspects of human life. This unprecedented integration of semiconductor based systems in our lives has significantly increased the domain and the number of safety-critical applications – application with unacceptable consequences of failure. Software-level error resilience schemes are attractive because they can provide commercial-off-the-shelf microprocessors with adaptive and scalable reliability. Among all software-level error resilience solutions, in-application instruction replication based approaches have been widely used and are deemed to be the most effective. However, existing instruction-based replication schemes only protect some part of computations i.e. arithmetic and logical instructions and leave …

Contributors
Didehban, Moslem, Shrivastava, Aviral, Wu, Carole-Jean, et al.
Created Date
2018

Hardware implementation of deep neural networks is earning significant importance nowadays. Deep neural networks are mathematical models that use learning algorithms inspired by the brain. Numerous deep learning algorithms such as multi-layer perceptrons (MLP) have demonstrated human-level recognition accuracy in image and speech classification tasks. Multiple layers of processing elements called neurons with several connections between them called synapses are used to build these networks. Hence, it involves operations that exhibit a high level of parallelism making it computationally and memory intensive. Constrained by computing resources and memory, most of the applications require a neural network which utilizes less energy. …

Contributors
Kolala Venkataramanaiah, Shreyas, Seo, Jae-sun, chakrabarti, Chaitali, et al.
Created Date
2018

Internet of Things (IoT) is emerging as part of the infrastructures for advancing a large variety of applications involving connections of many intelligent devices, leading to smart communities. Due to the severe limitation of the computing resources of IoT devices, it is common to offload tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, this offloading method suffers from both high latency and network congestion in the IoT infrastructures. Recently edge computing has emerged to reduce the negative impacts of tasks offloading to …

Contributors
Song, Yaozhong, Yau, Sik-Sang, Huang, Dijiang, et al.
Created Date
2018

Much evidence has shown that first language (L1) plays an important role in the formation of L2 phonological system during second language (L2) learning process. This combines with the fact that different L1s have distinct phonological patterns to indicate the diverse L2 speech learning outcomes for speakers from different L1 backgrounds. This dissertation hypothesizes that phonological distances between accented speech and speakers' L1 speech are also correlated with perceived accentedness, and the correlations are negative for some phonological properties. Moreover, contrastive phonological distinctions between L1s and L2 will manifest themselves in the accented speech produced by speaker from these L1s. …

Contributors
Tu, Ming, Berisha, Visar, Liss, Julie M, et al.
Created Date
2018

Mobile devices have penetrated into every aspect of modern world. For one thing, they are becoming ubiquitous in daily life. For the other thing, they are storing more and more data, including sensitive data. Therefore, security and privacy of mobile devices are indispensable. This dissertation consists of five parts: two authentication schemes, two attacks, and one countermeasure related to security and privacy of mobile devices. Specifically, in Chapter 1, I give an overview the challenges and existing solutions in these areas. In Chapter 2, a novel authentication scheme is presented, which is based on a user’s tapping or sliding on …

Contributors
Chen, Yimin, Zhang, Yanchao, Zhang, Junshan, et al.
Created Date
2018

General-purpose processors propel the advances and innovations that are the subject of humanity’s many endeavors. Catering to this demand, chip-multiprocessors (CMPs) and general-purpose graphics processing units (GPGPUs) have seen many high-performance innovations in their architectures. With these advances, the memory subsystem has become the performance- and energy-limiting aspect of CMPs and GPGPUs alike. This dissertation identifies and mitigates the key performance and energy-efficiency bottlenecks in the memory subsystem of general-purpose processors via novel, practical, microarchitecture and system-architecture solutions. Addressing the important Last Level Cache (LLC) management problem in CMPs, I observe that LLC management decisions made in isolation, as in …

Contributors
Arunkumar, Akhil, Wu, Carole-Jean, Shrivastava, Aviral, 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

Generating real-world content for VR is challenging in terms of capturing and processing at high resolution and high frame-rates. The content needs to represent a truly immersive experience, where the user can look around in 360-degree view and perceive the depth of the scene. The existing solutions only capture and offload the compute load to the server. But offloading large amounts of raw camera feeds takes longer latencies and poses difficulties for real-time applications. By capturing and computing on the edge, we can closely integrate the systems and optimize for low latency. However, moving the traditional stitching algorithms to battery …

Contributors
Gunnam, Sridhar, LiKamWa, Robert, Turaga, Pavan, et al.
Created Date
2018

Reasoning about the activities of cyber threat actors is critical to defend against cyber attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult to determine who the attacker is, what the desired goals are of the attacker, and how they will carry out their attacks. These three questions essentially entail understanding the attacker’s use of deception, the capabilities available, and the intent of launching the attack. These three issues are highly inter-related. If an adversary can hide their intent, they can better deceive a defender. If an adversary’s capabilities are not well …

Contributors
Nunes, Eric, Shakarian, Paulo, Ahn, Gail-Joon, 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

The Internet of Things (IoT) has become a more pervasive part of everyday life. IoT networks such as wireless sensor networks, depend greatly on the limiting unnecessary power consumption. As such, providing low-power, adaptable software can greatly improve network design. For streaming live video content, Wireless Video Sensor Network Platform compatible Dynamic Adaptive Streaming over HTTP (WVSNP-DASH) aims to revolutionize wireless segmented video streaming by providing a low-power, adaptable framework to compete with modern DASH players such as Moving Picture Experts Group (MPEG-DASH) and Apple’s Hypertext Transfer Protocol (HTTP) Live Streaming (HLS). Each segment is independently playable, and does not …

Contributors
Khan, Zarah, Reisslein, Martin, Seema, Adolph, et al.
Created Date
2018

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

The world’s population is currently 9% visually impaired. Medical sciences do not have a biological fix that can cure this visual impairment. Visually impaired people are currently being assisted with biological fixes or assistive devices. The current assistive devices are limited in size as well as resolution. This thesis presents the development and experimental validation of a control system for a new vibrotactile haptic display that is currently in development. In order to allow the vibrotactile haptic display to be used to represent motion, the control system must be able to change the image displayed at a rate of at …

Contributors
KIM, KENDRA, Sodemann, Angela, Robertson, John, et al.
Created Date
2018

Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph. To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance …

Contributors
Henderson, Christopher, Bansal, Ajay, Lindquist, Timothy, et al.
Created Date
2018

Software-defined radio provides users with a low-cost and flexible platform for implementing and studying advanced communications and remote sensing applications. Two such applications include unmanned aerial system-to-ground communications channel and joint sensing and communication systems. In this work, these applications are studied. In the first part, unmanned aerial system-to-ground communications channel models are derived from empirical data collected from software-defined radio transceivers in residential and mountainous desert environments using a small (< 20 kg) unmanned aerial system during low-altitude flight (< 130 m). The Kullback-Leibler divergence measure was employed to characterize model mismatch from the empirical data. Using this measure …

Contributors
Gutierrez, Richard, Bliss, Daniel W, Papandreou-Suppappola, Antonia, et al.
Created Date
2018

Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish …

Contributors
Mian, Sami, Panchanathan, Sethuraman, Berman, Spring, et al.
Created Date
2018

Diffusion processes in networks can be used to model many real-world processes, such as the propagation of a rumor on social networks and cascading failures on power networks. Analysis of diffusion processes in networks can help us answer important questions such as the role and the importance of each node in the network for spreading the diffusion and how to top or contain a cascading failure in the network. This dissertation consists of three parts. In the first part, we study the problem of locating multiple diffusion sources in networks under the Susceptible-Infected-Recovered (SIR) model. Given a complete snapshot of …

Contributors
Chen, Zhen, Ying, Lei, Tong, Hanghang, et al.
Created Date
2018

Each programming language has a compiler associated with it which helps to identify logical or syntactical errors in the program. These compiler error messages play important part in the form of formative feedback for the programmer. Thus, the error messages should be constructed carefully, considering the affective and cognitive needs of programmers. This is especially true for systems that are used in educational settings, as the messages are typically seen by students who are novice programmers. If the error messages are hard to understand then they might discourage students from understanding or learning the programming language. The primary goal of …

Contributors
Tanpure, Siddhant Bapusaheb, Sohoni, Sohum, Gary, Kevin A, et al.
Created Date
2018

Static CMOS logic has remained the dominant design style of digital systems for more than four decades due to its robustness and near zero standby current. Static CMOS logic circuits consist of a network of combinational logic cells and clocked sequential elements, such as latches and flip-flops that are used for sequencing computations over time. The majority of the digital design techniques to reduce power, area, and leakage over the past four decades have focused almost entirely on optimizing the combinational logic. This work explores alternate architectures for the flip-flops for improving the overall circuit performance, power and area. It …

Contributors
Yang, Jinghua, Vrudhula, Sarma, Barnaby, Hugh, et al.
Created Date
2018

Cyber-Physical Systems (CPS) are being used in many safety-critical applications. Due to the important role in virtually every aspect of human life, it is crucial to make sure that a CPS works properly before its deployment. However, formal verification of CPS is a computationally hard problem. Therefore, lightweight verification methods such as testing and monitoring of the CPS are considered in the industry. The formal representation of the CPS requirements is a challenging task. In addition, checking the system outputs with respect to requirements is a computationally complex problem. In this dissertation, these problems for the verification of CPS are …

Contributors
Dokhanchi, Adel, Fainekos, Georgios, Lee, Yann-Hang, et al.
Created Date
2017

User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven …

Contributors
Gaudette, Benjamin David, Vrudhula, Sarma, Wu, Carole-Jean, et al.
Created Date
2017

Digital architectures for data encryption, processing, clock synthesis, data transfer, etc. are susceptible to radiation induced soft errors due to charge collection in complementary metal oxide semiconductor (CMOS) integrated circuits (ICs). Radiation hardening by design (RHBD) techniques such as double modular redundancy (DMR) and triple modular redundancy (TMR) are used for error detection and correction respectively in such architectures. Multiple node charge collection (MNCC) causes domain crossing errors (DCE) which can render the redundancy ineffectual. This dissertation describes techniques to ensure DCE mitigation with statistical confidence for various designs. Both sequential and combinatorial logic are separated using these custom and …

Contributors
Ramamurthy, Chandarasekaran, Clark, Lawrence T, Allee, David, et al.
Created Date
2017

With the advent of GPGPU, many applications are being accelerated by using CUDA programing paradigm. We are able to achieve around 10x -100x speedups by simply porting the application on to the GPU and running the parallel chunk of code on its multi cored SIMT (Single instruction multiple thread) architecture. But for optimal performance it is necessary to make sure that all the GPU resources are efficiently used, and the latencies in the application are minimized. For this, it is essential to monitor the Hardware usage of the algorithm and thus diagnose the compute and memory bottlenecks in the implementation. …

Contributors
Wadekar, Ameya Rajendra, Sohoni, Sohum, Aukes, Daniel, et al.
Created Date
2017

With the massive multithreading execution feature, graphics processing units (GPUs) have been widely deployed to accelerate general-purpose parallel workloads (GPGPUs). However, using GPUs to accelerate computation does not always gain good performance improvement. This is mainly due to three inefficiencies in modern GPU and system architectures. First, not all parallel threads have a uniform amount of workload to fully utilize GPU’s computation ability, leading to a sub-optimal performance problem, called warp criticality. To mitigate the degree of warp criticality, I propose a Criticality-Aware Warp Acceleration mechanism, called CAWA. CAWA predicts and accelerates the critical warp execution by allocating larger execution …

Contributors
Lee, Shin-Ying, Wu, Carole-Jean, Chakrabarti, Chaitali, et al.
Created Date
2017

Modern-day integrated circuits are very capable, often containing more than a billion transistors. For example, the Intel Ivy Bridge 4C chip has about 1.2 billion transistors on a 160 mm2 die. Designing such complex circuits requires automation. Therefore, these designs are made with the help of computer aided design (CAD) tools. A major part of this custom design flow for application specific integrated circuits (ASIC) is the design of standard cell libraries. Standard cell libraries are a collection of primitives from which the automatic place and route (APR) tools can choose a collection of cells and implement the design that …

Contributors
Vangala, Manoj, Clark, Lawrence T, Brunhaver, John S, et al.
Created Date
2017

Light field imaging is limited in its computational processing demands of high sampling for both spatial and angular dimensions. Single-shot light field cameras sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing incoming rays onto a 2D sensor array. While this resolution can be recovered using compressive sensing, these iterative solutions are slow in processing a light field. We present a deep learning approach using a new, two branch network architecture, consisting jointly of an autoencoder and a 4D CNN, to recover a high resolution 4D light field from a single coded 2D image. This network decreases reconstruction time …

Contributors
Gupta, Mayank, Turaga, Pavan, Yang, Yezhou, et al.
Created Date
2017

Caches pose a serious limitation in scaling many-core architectures since the demand of area and power for maintaining cache coherence increases rapidly with the number of cores. Scratch-Pad Memories (SPMs) provide a cheaper and lower power alternative that can be used to build a more scalable many-core architecture. The trade-off of substituting SPMs for caches is however that the data must be explicitly managed in software. Heap management on SPM poses a major challenge due to the highly dynamic nature of of heap data access. Most existing heap management techniques implement a software caching scheme on SPM, emulating the behavior …

Contributors
Lin, Jinn-Pean, Shrivastava, Aviral, Ren, Fengbo, et al.
Created Date
2017

Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and the central idea is that multiple tenant applications can be developed using compo nents stored in the SaaS infrastructure. Recently, MTA has been extended where a tenant application can have its own sub-tenants as the tenant application acts like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy Architecture ). In STA, each tenant application not only need to develop its own functionalities, but also need to prepare an infrastructure to allow its sub-tenants to develop customized applications. This dissertation formulates eight models for STA, and proposes …

Contributors
Zhong, Peide, Davulcu, Hasan, Sarjoughian, Hessam, et al.
Created Date
2017

Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), …

Contributors
Derakhshandeh, Zahra, Richa, Andrea, Sen, Arunabha, et al.
Created Date
2017

The information era has brought about many technological advancements in the past few decades, and that has led to an exponential increase in the creation of digital images and videos. Constantly, all digital images go through some image processing algorithm for various reasons like compression, transmission, storage, etc. There is data loss during this process which leaves us with a degraded image. Hence, to ensure minimal degradation of images, the requirement for quality assessment has become mandatory. Image Quality Assessment (IQA) has been researched and developed over the last several decades to predict the quality score in a manner that …

Contributors
Gunavelu Mohan, Aswin, Sohoni, Sohum, Ren, Fengbo, et al.
Created Date
2017

Many real-time vision applications require accurate estimation of optical flow. This problem is quite challenging due to extremely high computation and memory requirements. This thesis focuses on designing low complexity dense optical flow algorithms. First, a new method for optical flow that is based on Semi-Global Matching (SGM), a popular dynamic programming algorithm for stereo vision, is presented. In SGM, the disparity of each pixel is calculated by aggregating local matching costs over the entire image to resolve local ambiguity in texture-less and occluded regions. The proposed method, Neighbor-Guided Semi-Global Matching (NG-fSGM) achieves significantly less complexity compared to SGM, by …

Contributors
Xiang, Jiang, Chakrabarti, Chaitali, Karam, Lina, et al.
Created Date
2017

This work presents a communication paradigm, using a context-aware mixed reality approach, for instructing human workers when collaborating with robots. The main objective of this approach is to utilize the physical work environment as a canvas to communicate task-related instructions and robot intentions in the form of visual cues. A vision-based object tracking algorithm is used to precisely determine the pose and state of physical objects in and around the workspace. A projection mapping technique is used to overlay visual cues on tracked objects and the workspace. Simultaneous tracking and projection onto objects enables the system to provide just-in-time instructions …

Contributors
Kalpagam Ganesan, Ramsundar, Ben Amor, Hani, Yang, Yezhou, et al.
Created Date
2017

Image processing has changed the way we store, view and share images. One important component of sharing images over the networks is image compression. Lossy image compression techniques compromise the quality of images to reduce their size. To ensure that the distortion of images due to image compression is not highly detectable by humans, the perceived quality of an image needs to be maintained over a certain threshold. Determining this threshold is best done using human subjects, but that is impractical in real-world scenarios. As a solution to this issue, image quality assessment (IQA) algorithms are used to automatically compute …

Contributors
Gupta, Ayush, Sohoni, Sohum, Amresh, Ashish, et al.
Created Date
2017

Mobile devices are penetrating everyday life. According to a recent Cisco report [10], the number of mobile connected devices such as smartphones, tablets, laptops, eReaders, and Machine-to-Machine (M2M) modules will hit 11.6 billion by 2021, exceeding the world's projected population at that time (7.8 billion). The rapid development of mobile devices has brought a number of emerging security and privacy issues in mobile computing. This dissertation aims to address a number of challenging security and privacy issues in mobile computing. This dissertation makes fivefold contributions. The first and second parts study the security and privacy issues in Device-to-Device communications. Specifically, …

Contributors
Sun, Jingchao, Zhang, Yanchao, Zhang, Junshan, et al.
Created Date
2017

Cyber-physical systems and hard real-time systems have strict timing constraints that specify deadlines until which tasks must finish their execution. Missing a deadline can cause unexpected outcome or endanger human lives in safety-critical applications, such as automotive or aeronautical systems. It is, therefore, of utmost importance to obtain and optimize a safe upper bound of each task’s execution time or the worst-case execution time (WCET), to guarantee the absence of any missed deadline. Unfortunately, conventional microarchitectural components, such as caches and branch predictors, are only optimized for average-case performance and often make WCET analysis complicated and pessimistic. Caches especially have …

Contributors
Kim, Yooseong, Shrivastava, Aviral, Broman, David, et al.
Created Date
2017

Achieving human level intelligence is a long-term goal for many Artificial Intelligence (AI) researchers. Recent developments in combining deep learning and reinforcement learning helped us to move a step forward in achieving this goal. Reinforcement learning using a delayed reward mechanism is an approach to machine intelligence which studies decision making with control and how a decision making agent can learn to act optimally in an environment-unaware conditions. Q-learning is one of the model-free reinforcement directed learning strategies which uses temporal differences to estimate the performances of state-action pairs called Q values. A simple implementation of Q-learning algorithm can be …

Contributors
Gankidi, Pranay Reddy, Thangavelautham, Jekanthan, Ren, Fengbo, et al.
Created Date
2016

High-level inference tasks in video applications such as recognition, video retrieval, and zero-shot classification have become an active research area in recent years. One fundamental requirement for such applications is to extract high-quality features that maintain high-level information in the videos. Many video feature extraction algorithms have been purposed, such as STIP, HOG3D, and Dense Trajectories. These algorithms are often referred to as “handcrafted” features as they were deliberately designed based on some reasonable considerations. However, these algorithms may fail when dealing with high-level tasks or complex scene videos. Due to the success of using deep convolution neural networks (CNNs) …

Contributors
Hu, Sheng-Hung, Li, Baoxin, Turaga, Pavan, et al.
Created Date
2016

Multi-sensor fusion is a fundamental problem in Robot Perception. For a robot to operate in a real world environment, multiple sensors are often needed. Thus, fusing data from various sensors accurately is vital for robot perception. In the first part of this thesis, the problem of fusing information from a LIDAR, a color camera and a thermal camera to build RGB-Depth-Thermal (RGBDT) maps is investigated. An algorithm that solves a non-linear optimization problem to compute the relative pose between the cameras and the LIDAR is presented. The relative pose estimate is then used to find the color and thermal texture …

Contributors
Krishnan, Aravindhan K., Saripalli, Srikanth, Klesh, Andrew, et al.
Created Date
2016

Coarse-grained Reconfigurable Arrays (CGRAs) are promising accelerators capable of accelerating even non-parallel loops and loops with low trip-counts. One challenge in compiling for CGRAs is to manage both recurring and nonrecurring variables in the register file (RF) of the CGRA. Although prior works have managed recurring variables via rotating RF, they access the nonrecurring variables through either a global RF or from a constant memory. The former does not scale well, and the latter degrades the mapping quality. This work proposes a hardware-software codesign approach in order to manage all the variables in a local nonrotating RF. Hardware provides modulo …

Contributors
Dave, Shail, Shrivastava, Aviral, Ren, Fengbo, et al.
Created Date
2016

Soft errors are considered as a key reliability challenge for sub-nano scale transistors. An ideal solution for such a challenge should ultimately eliminate the effect of soft errors from the microprocessor. While forward recovery techniques achieve fast recovery from errors by simply voting out the wrong values, they incur the overhead of three copies execution. Backward recovery techniques only need two copies of execution, but suffer from check-pointing overhead. In this work I explored the efficiency of integrating check-pointing into the application and the effectiveness of recovery that can be performed upon it. After evaluating the available fine-grained approaches to …

Contributors
Lokam, Sai Ram Dheeraj, Shrivastava, Aviral, Clark, Lawrence T, et al.
Created Date
2016

The availability of a wide range of general purpose as well as accelerator cores on modern smartphones means that a significant number of applications can be executed on a smartphone simultaneously, resulting in an ever increasing demand on the memory subsystem. While the increased computation capability is intended for improving user experience, memory requests from each concurrent application exhibit unique memory access patterns as well as specific timing constraints. If not considered, this could lead to significant memory contention and result in lowered user experience. This work first analyzes the impact of memory degradation caused by the interference at the …

Contributors
SHINGARI, DAVESH, Wu, Carole-Jean, Vrudhula, Sarma, et al.
Created Date
2016

The technological advances in the past few decades have made possible creation and consumption of digital visual content at an explosive rate. Consequently, there is a need for efficient quality monitoring systems to ensure minimal degradation of images and videos during various processing operations like compression, transmission, storage etc. Objective Image Quality Assessment (IQA) algorithms have been developed that predict quality scores which match well with human subjective quality assessment. However, a lot of research still remains to be done before IQA algorithms can be deployed in real world systems. Long runtimes for one frame of image is a major …

Contributors
Yadav, Aman, Sohoni, Sohum, Aukes, Daniel, et al.
Created Date
2016

A lot of strides have been made in enabling technologies to aid individuals with visual impairment live an independent life. The advent of smart devices and participatory web has especially facilitated the possibility of new interactions to aide everyday tasks. Current systems however tend to be complex and require multiple cumbersome devices which invariably come with steep learning curves. Building new cyber-human systems with simple integrated interfaces while keeping in mind the specific requirements of the target users would help alleviate their mundane yet significant daily needs. Navigation is one such significant need that forms an integral part of everyday …

Contributors
Paladugu, Devi Archana, Li, Baoxin, Hedgpeth, Terri, et al.
Created Date
2016