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Description
Convolutional Neural Network (CNN) has achieved state-of-the-art performance in numerous applications like computer vision, natural language processing, robotics etc. The advancement of High-Performance Computing systems equipped with dedicated hardware accelerators has also paved the way towards the success of compute intensive CNNs. Graphics Processing Units (GPUs), with massive processing capability, have been of general interest for the acceleration of CNNs. Recently, Field Programmable Gate Arrays (FPGAs) have been promising in CNN acceleration since they offer high performance while also being re-configurable to support the evolution of CNNs. This work focuses on a design methodology to accelerate CNNs on FPGA with low inference latency and high-throughput which are crucial for scenarios like self-driving cars, video surveillance etc. It also includes optimizations which reduce the resource utilization by a large margin with a small degradation in performance thus making the design suitable for low-end FPGA devices as well.
FPGA accelerators often suffer due to the limited main memory bandwidth. Also, highly parallel designs with large resource utilization often end up achieving low operating frequency due to poor routing. This work employs data fetch and buffer mechanisms, designed specifically for the memory access pattern of CNNs, that overlap computation with memory access. This work proposes a novel arrangement of the systolic processing element array to achieve high frequency and consume less resources than the existing works. Also, support has been extended to more complicated CNNs to do video processing. On Intel Arria 10 GX1150, the design operates at a frequency as high as 258MHz and performs single inference of VGG-16 and C3D in 23.5ms and 45.6ms respectively. For VGG-16 and C3D the design offers a throughput of 66.1 and 23.98 inferences/s respectively. This design can outperform other FPGA 2D CNN accelerators by up to 9.7 times and 3D CNN accelerators by up to 2.7 times.
FPGA accelerators often suffer due to the limited main memory bandwidth. Also, highly parallel designs with large resource utilization often end up achieving low operating frequency due to poor routing. This work employs data fetch and buffer mechanisms, designed specifically for the memory access pattern of CNNs, that overlap computation with memory access. This work proposes a novel arrangement of the systolic processing element array to achieve high frequency and consume less resources than the existing works. Also, support has been extended to more complicated CNNs to do video processing. On Intel Arria 10 GX1150, the design operates at a frequency as high as 258MHz and performs single inference of VGG-16 and C3D in 23.5ms and 45.6ms respectively. For VGG-16 and C3D the design offers a throughput of 66.1 and 23.98 inferences/s respectively. This design can outperform other FPGA 2D CNN accelerators by up to 9.7 times and 3D CNN accelerators by up to 2.7 times.
ContributorsRavi, Pravin Kumar (Author) / Zhao, Ming (Thesis advisor) / Li, Baoxin (Committee member) / Ren, Fengbo (Committee member) / Arizona State University (Publisher)
Created2020
Description
The use of reactive security mechanisms in enterprise networks can, at times, provide an asymmetric advantage to the attacker. Similarly, the use of a proactive security mechanism like Moving Target Defense (MTD), if performed without analyzing the effects of security countermeasures, can lead to security policy and service level agreement violations. In this thesis, I explore the research questions 1) how to model attacker-defender interactions for multi-stage attacks? 2) how to efficiently deploy proactive (MTD) security countermeasures in a software-defined environment for single and multi-stage attacks? 3) how to verify the effects of security and management policies on the network and take corrective actions?
I propose a Software-defined Situation-aware Cloud Security framework, that, 1) analyzes the attacker-defender interactions using an Software-defined Networking (SDN) based scalable attack graph. This research investigates Advanced Persistent Threat (APT) attacks using a scalable attack graph. The framework utilizes a parallel graph partitioning algorithm to generate an attack graph quickly and efficiently. 2) models single-stage and multi-stage attacks (APTs) using the game-theoretic model and provides SDN-based MTD countermeasures. I propose a Markov Game for modeling multi-stage attacks. 3) introduces a multi-stage policy conflict checking framework at the SDN network's application plane. I present INTPOL, a new intent-driven security policy enforcement solution. INTPOL provides a unified language and INTPOL grammar that abstracts the network administrator from the underlying network controller's lexical rules. INTPOL develops a bounded formal model for network service compliance checking, which significantly reduces the number of countermeasures that needs to be deployed. Once the application-layer policy conflicts are resolved, I utilize an Object-Oriented Policy Conflict checking (OOPC) framework that identifies and resolves rule-order dependencies and conflicts between security policies.
I propose a Software-defined Situation-aware Cloud Security framework, that, 1) analyzes the attacker-defender interactions using an Software-defined Networking (SDN) based scalable attack graph. This research investigates Advanced Persistent Threat (APT) attacks using a scalable attack graph. The framework utilizes a parallel graph partitioning algorithm to generate an attack graph quickly and efficiently. 2) models single-stage and multi-stage attacks (APTs) using the game-theoretic model and provides SDN-based MTD countermeasures. I propose a Markov Game for modeling multi-stage attacks. 3) introduces a multi-stage policy conflict checking framework at the SDN network's application plane. I present INTPOL, a new intent-driven security policy enforcement solution. INTPOL provides a unified language and INTPOL grammar that abstracts the network administrator from the underlying network controller's lexical rules. INTPOL develops a bounded formal model for network service compliance checking, which significantly reduces the number of countermeasures that needs to be deployed. Once the application-layer policy conflicts are resolved, I utilize an Object-Oriented Policy Conflict checking (OOPC) framework that identifies and resolves rule-order dependencies and conflicts between security policies.
ContributorsChowdhary, Ankur (Author) / Huang, Dijiang (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Doupe, Adam (Committee member) / Bao, Youzhi (Committee member) / Arizona State University (Publisher)
Created2020
Description
Background. Street food stands (SFS) are common ways in which people in Mexico access food, having been a part of the environment and culture of Mexican food for generations. However, no studies have used a validated assessment tool to reliably measure food and beverage availability at a variety of SFS. Nor have the availability, density, variety, and distribution of SFS and street foods and beverages been assessed across neighborhood income levels.Objective: This dissertation’s goal was to decrease gaps in knowledge about the role SFS may play in food availability in the Mexican food environment.
Methods: Survey design and ethnographic field methods were used to develop, test, and validate the Street Food Stand Assessment Tool (SFSAT). Geographic information system and ground-truthing methods were used to identify a sample of street segments across 20 neighborhoods representing low-, middle- and high-income neighborhoods in Mexico City on which to assess the availability, density, variety, and distribution of SFS and the foods and beverages sold at these food venues using the SFSAT.
Results: A sample of 391 SFS were assessed across 791 street segments. Results showed that SFS were found in all neighborhoods. Contrary to the initial hypothesis, most SFS were found in middle-income neighborhoods. While the availability of street foods and beverages was higher in middle-income neighborhoods, the variety was less consistent: fruit/vegetable variety was high in high-income neighborhoods whereas processed snack variety was higher in low-income neighborhoods. SFS were most often distributed near homes, transportation centers, and worksites across the three neighborhood income levels.
Conclusion: This study bridged the gap in knowledge about the availability, density, variety, and distribution of SFS and products sold at these sources of food by using an assessment tool that was developed, tested, and validated specifically for SFS. The findings showed that SFS were found across all neighborhoods. Furthermore, results also suggested that SFS can be a source of healthy food items. Additional studies are needed to understand the relationship between SFS availability, food consumption, and health outcomes in the Mexican population.
Methods: Survey design and ethnographic field methods were used to develop, test, and validate the Street Food Stand Assessment Tool (SFSAT). Geographic information system and ground-truthing methods were used to identify a sample of street segments across 20 neighborhoods representing low-, middle- and high-income neighborhoods in Mexico City on which to assess the availability, density, variety, and distribution of SFS and the foods and beverages sold at these food venues using the SFSAT.
Results: A sample of 391 SFS were assessed across 791 street segments. Results showed that SFS were found in all neighborhoods. Contrary to the initial hypothesis, most SFS were found in middle-income neighborhoods. While the availability of street foods and beverages was higher in middle-income neighborhoods, the variety was less consistent: fruit/vegetable variety was high in high-income neighborhoods whereas processed snack variety was higher in low-income neighborhoods. SFS were most often distributed near homes, transportation centers, and worksites across the three neighborhood income levels.
Conclusion: This study bridged the gap in knowledge about the availability, density, variety, and distribution of SFS and products sold at these sources of food by using an assessment tool that was developed, tested, and validated specifically for SFS. The findings showed that SFS were found across all neighborhoods. Furthermore, results also suggested that SFS can be a source of healthy food items. Additional studies are needed to understand the relationship between SFS availability, food consumption, and health outcomes in the Mexican population.
ContributorsRosales Chavez, Jose Benito (Author) / Jehn, Megan (Thesis advisor) / Bruening, Meg (Thesis advisor) / Lee, Rebecca E (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Arizona State University (Publisher)
Created2020
Description
Background: Stores authorized by the Supplemental Nutrition Program for Women, Infants, and Children (WIC) have been shown to improve the community food environments of lower-income areas by stocking healthy food items in accordance with the program’s food package guidelines. Whether greater access to WIC-authorized stores is associated with improvements in diet among children from WIC and non-WIC households is not well understood. Methods: Secondary analysis of cross-sectional data collected in 2009-2010 and 2014 for the New Jersey Child Health Study (NJCHS). Surveys from 2,211 urban households with 3-18-year-old children. Counts of WIC stores near children’s homes determined through geo-coding of store and household addresses using roadway network distances of 0.5 and 1.0 mile. Children’s consumption was categorized in age-specific deciles of quantities consumed for each food category examined: fruits, vegetables, sugar from sugar-sweetened beverages, total added sugars. Associations between counts of WIC stores and children’s consumption were examined, first for the full sample, then by household WIC participation.
Results: No significant associations between WIC store counts near children’s homes and consumption were observed in the overall sample at any distance. A small, but significant inverse relationship was seen in total added sugar consumption among children residing in WIC households only, with each additional WIC store within a 0.5 mile roadway network associated with a 0.24-decile lower consumption (p = .047). In age-stratified exploratory analysis, higher vegetable (p = .024) and combined fruits and vegetables (p = .006) consumption were seen in the under 5 age group only.
Conclusions: Living close to more WIC-authorized stores was associated with healthier consumption, but only for a subset of children and only for a few food categories examined. Lack of a consistent pattern of healthier consumption among children suggests that access to WIC stores may have a positive, albeit limited impact on children’s diets.
Results: No significant associations between WIC store counts near children’s homes and consumption were observed in the overall sample at any distance. A small, but significant inverse relationship was seen in total added sugar consumption among children residing in WIC households only, with each additional WIC store within a 0.5 mile roadway network associated with a 0.24-decile lower consumption (p = .047). In age-stratified exploratory analysis, higher vegetable (p = .024) and combined fruits and vegetables (p = .006) consumption were seen in the under 5 age group only.
Conclusions: Living close to more WIC-authorized stores was associated with healthier consumption, but only for a subset of children and only for a few food categories examined. Lack of a consistent pattern of healthier consumption among children suggests that access to WIC stores may have a positive, albeit limited impact on children’s diets.
ContributorsStevens, Clinton (Author) / Ohri-Vachaspati, Punam (Thesis advisor) / Gosliner, Wendi (Committee member) / Martinelli, Sarah (Committee member) / Arizona State University (Publisher)
Created2021
Description
This study was designed to examine the associations between food skills, resilience, and coping during the Covid-19 pandemic. Between April and June of 2020, a sample of 154 students, faculty, and staff from Arizona State University were surveyed. Each respondent was administered a survey containing demographic questions, a food skill questionnaire, and the 14-item Resilience Scale (RS). Results indicate that food skill was correlated with resilience (p<0.001) at an r=0.364 and r2=0.1243 and that resilience was correlated with coping during the Covid-19 pandemic (p<0.001) at an r=0.455. Correlations were also run between resilience score and the separate domains of food skill score: all domains remained significantly associated with resilience score (p<0.001) with a r=0.340 and r2=0.1173 for ‘Food Selection and Planning,’ r=0.312 and r2=0.0958 for ‘Food Preparation,’ and r=0.294 and r2=0.0767 for ‘Food Safety.’ Data seems to be consistent with contemporary research suggesting positive associations between diet quality and physiological resilience and positive associations between resilience and coping during the Covid-19 pandemic.
ContributorsPhares, Savanna Julene (Author) / Johnston, Carol (Thesis advisor) / McCoy, Maureen (Committee member) / Irving, Andrea (Committee member) / Arizona State University (Publisher)
Created2020
Description
First year college students have been identified as a vulnerable population for weight gain and the onset of overweight and obesity. Research regarding the gut microbiome has identified differences in the microbial composition of overweight and obese individuals compared to normal weight individuals. Dietary components like dietary fibers, act as prebiotics, or fermentable substrate, that the gut microbiota use for metabolic functions including the production of short-chain fatty acids. The objective of this longitudinal, observational study was to assess changes in the gut microbiota over time in relation to changes in fiber consumption in healthy college students at a large a southwestern university (n=137). Anthropometric and fecal samples were collected at the beginning and end of the fall and spring semesters between August 2015 and May 2016. Both alpha, within sample, diversity and beta, between sample, diversity of participant gut microbes were assessed longitudinally using non-parametric pairwise (pre-post) comparisons and linear mixed effect (LME) models which also adjusted for covariates and accounted for time as a random effect. Alpha and beta diversity were also explored using LME first difference metrics and LME first distance metrics, respectively, to understand rates of change over time in microbial richness/phylogeny and community structure. Pre-post comparisons of Shannon Diversity and Faith’s PD were not significantly different within participant groups of fiber change (Shannon diversity, p=0.96 and Faith’s PD, p=0.66). Beta diversity pairwise comparisons also did not differ by fiber consumption groups (Unweighted UniFrac p=0.182 and Bray Curtis p=0.657). Similarly, none of the LME models suggested significant associations between dietary fiber consumption and metrics of alpha and beta diversity. Overall, data from this study indicates that small changes in fiber consumption among a free-living population did not have an impact on gut microbial richness, phylogeny or community structure. This may have been due to the low intake (~15 g/d) of fiber. Further study is needed to fully elucidate the role that fiber plays in the diversity and composition of the gut microbiota, especially when delivered from a variety of food sources rather than fiber supplements.
ContributorsLolley, Sarah (Author) / Whisner, Corrie (Thesis advisor) / Sears, Dorothy (Committee member) / Shepard, Christina (Committee member) / Arizona State University (Publisher)
Created2020
Description
The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
ContributorsAnand, Harish (Author) / Das, Jnaneshwar (Thesis advisor) / Yang, Yezhou (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2020
Description
Many real-world planning problems can be modeled as Markov Decision Processes (MDPs) which provide a framework for handling uncertainty in outcomes of action executions. A solution to such a planning problem is a policy that handles possible contingencies that could arise during execution. MDP solvers typically construct policies for a problem instance without re-using information from previously solved instances. Research in generalized planning has demonstrated the utility of constructing algorithm-like plans that reuse such information. However, using such techniques in an MDP setting has not been adequately explored.
This thesis presents a novel approach for learning generalized partial policies that can be used to solve problems with different object names and/or object quantities using very few example policies for learning. This approach uses abstraction for state representation, which allows the identification of patterns in solutions such as loops that are agnostic to problem-specific properties. This thesis also presents some theoretical results related to the uniqueness and succinctness of the policies computed using such a representation. The presented algorithm can be used as fast, yet greedy and incomplete method for policy computation while falling back to a complete policy search algorithm when needed. Extensive empirical evaluation on discrete MDP benchmarks shows that this approach generalizes effectively and is often able to solve problems much faster than existing state-of-art discrete MDP solvers. Finally, the practical applicability of this approach is demonstrated by incorporating it in an anytime stochastic task and motion planning framework to successfully construct free-standing tower structures using Keva planks.
This thesis presents a novel approach for learning generalized partial policies that can be used to solve problems with different object names and/or object quantities using very few example policies for learning. This approach uses abstraction for state representation, which allows the identification of patterns in solutions such as loops that are agnostic to problem-specific properties. This thesis also presents some theoretical results related to the uniqueness and succinctness of the policies computed using such a representation. The presented algorithm can be used as fast, yet greedy and incomplete method for policy computation while falling back to a complete policy search algorithm when needed. Extensive empirical evaluation on discrete MDP benchmarks shows that this approach generalizes effectively and is often able to solve problems much faster than existing state-of-art discrete MDP solvers. Finally, the practical applicability of this approach is demonstrated by incorporating it in an anytime stochastic task and motion planning framework to successfully construct free-standing tower structures using Keva planks.
ContributorsKala Vasudevan, Deepak (Author) / Srivastava, Siddharth (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Next-generation sequencing is a powerful tool for detecting genetic variation. How-ever, it is also error-prone, with error rates that are much larger than mutation rates.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
ContributorsOrr, Adam James (Author) / Cartwright, Reed (Thesis advisor) / Wilson, Melissa (Committee member) / Kusumi, Kenro (Committee member) / Taylor, Jesse (Committee member) / Pfeifer, Susanne (Committee member) / Arizona State University (Publisher)
Created2020
Description
Access to real-time situational information including the relative position and motion of surrounding objects is critical for safe and independent travel. Object or obstacle (OO) detection at a distance is primarily a task of the visual system due to the high resolution information the eyes are able to receive from afar. As a sensory organ in particular, the eyes have an unparalleled ability to adjust to varying degrees of light, color, and distance. Therefore, in the case of a non-visual traveler, someone who is blind or low vision, access to visual information is unattainable if it is positioned beyond the reach of the preferred mobility device or outside the path of travel. Although, the area of assistive technology in terms of electronic travel aids (ETA’s) has received considerable attention over the last two decades; surprisingly, the field has seen little work in the area focused on augmenting rather than replacing current non-visual travel techniques, methods, and tools. Consequently, this work describes the design of an intuitive tactile language and series of wearable tactile interfaces (the Haptic Chair, HaptWrap, and HapBack) to deliver real-time spatiotemporal data. The overall intuitiveness of the haptic mappings conveyed through the tactile interfaces are evaluated using a combination of absolute identification accuracy of a series of patterns and subjective feedback through post-experiment surveys. Two types of spatiotemporal representations are considered: static patterns representing object location at a single time instance, and dynamic patterns, added in the HaptWrap, which represent object movement over a time interval. Results support the viability of multi-dimensional haptics applied to the body to yield an intuitive understanding of dynamic interactions occurring around the navigator during travel. Lastly, it is important to point out that the guiding principle of this work centered on providing the navigator with spatial knowledge otherwise unattainable through current mobility techniques, methods, and tools, thus, providing the \emph{navigator} with the information necessary to make informed navigation decisions independently, at a distance.
ContributorsDuarte, Bryan Joiner (Author) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020