Matching Items (11)
Description
Learning and transfer were investigated for a categorical structure in which relevant stimulus information could be mapped without loss from one modality to another. The category space was composed of three non-overlapping, linearly-separable categories. Each stimulus was composed of a sequence of on-off events that varied in duration and number of sub-events (complexity). Categories were learned visually, haptically, or auditorily, and transferred to the same or an alternate modality. The transfer set contained old, new, and prototype stimuli, and subjects made both classification and recognition judgments. The results showed an early learning advantage in the visual modality, with transfer performance varying among the conditions in both classification and recognition. In general, classification accuracy was highest for the category prototype, with false recognition of the category prototype higher in the cross-modality conditions. The results are discussed in terms of current theories in modality transfer, and shed preliminary light on categorical transfer of temporal stimuli.
ContributorsFerguson, Ryan (Author) / Homa, Donald (Thesis advisor) / Goldinger, Stephen (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2011
Description
Despite the various driver assistance systems and electronics, the threat to life of driver, passengers and other people on the road still persists. With the growth in technology, the use of in-vehicle devices with a plethora of buttons and features is increasing resulting in increased distraction. Recently, speech recognition has emerged as an alternative to distraction and has the potential to be beneficial. However, considering the fact that automotive environment is dynamic and noisy in nature, distraction may not arise from the manual interaction, but due to the cognitive load. Hence, speech recognition certainly cannot be a reliable mode of communication.
The thesis is focused on proposing a simultaneous multimodal approach for designing interface between driver and vehicle with a goal to enable the driver to be more attentive to the driving tasks and spend less time fiddling with distractive tasks. By analyzing the human-human multimodal interaction techniques, new modes have been identified and experimented, especially suitable for the automotive context. The identified modes are touch, speech, graphics, voice-tip and text-tip. The multiple modes are intended to work collectively to make the interaction more intuitive and natural. In order to obtain a minimalist user-centered design for the center stack, various design principles such as 80/20 rule, contour bias, affordance, flexibility-usability trade-off etc. have been implemented on the prototypes. The prototype was developed using the Dragon software development kit on android platform for speech recognition.
In the present study, the driver behavior was investigated in an experiment conducted on the DriveSafety driving simulator DS-600s. Twelve volunteers drove the simulator under two conditions: (1) accessing the center stack applications using touch only and (2) accessing the applications using speech with offered text-tip. The duration for which user looked away from the road (eyes-off-road) was measured manually for each scenario. Comparison of results proved that eyes-off-road time is less for the second scenario. The minimalist design with 8-10 icons per screen proved to be effective as all the readings were within the driver distraction recommendations (eyes-off-road time < 2sec per screen) defined by NHTSA.
The thesis is focused on proposing a simultaneous multimodal approach for designing interface between driver and vehicle with a goal to enable the driver to be more attentive to the driving tasks and spend less time fiddling with distractive tasks. By analyzing the human-human multimodal interaction techniques, new modes have been identified and experimented, especially suitable for the automotive context. The identified modes are touch, speech, graphics, voice-tip and text-tip. The multiple modes are intended to work collectively to make the interaction more intuitive and natural. In order to obtain a minimalist user-centered design for the center stack, various design principles such as 80/20 rule, contour bias, affordance, flexibility-usability trade-off etc. have been implemented on the prototypes. The prototype was developed using the Dragon software development kit on android platform for speech recognition.
In the present study, the driver behavior was investigated in an experiment conducted on the DriveSafety driving simulator DS-600s. Twelve volunteers drove the simulator under two conditions: (1) accessing the center stack applications using touch only and (2) accessing the applications using speech with offered text-tip. The duration for which user looked away from the road (eyes-off-road) was measured manually for each scenario. Comparison of results proved that eyes-off-road time is less for the second scenario. The minimalist design with 8-10 icons per screen proved to be effective as all the readings were within the driver distraction recommendations (eyes-off-road time < 2sec per screen) defined by NHTSA.
ContributorsMittal, Richa (Author) / Gaffar, Ashraf (Thesis advisor) / Femiani, John (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2015
Description
Deep learning architectures have been widely explored in computer vision and have
depicted commendable performance in a variety of applications. A fundamental challenge
in training deep networks is the requirement of large amounts of labeled training
data. While gathering large quantities of unlabeled data is cheap and easy, annotating
the data is an expensive process in terms of time, labor and human expertise.
Thus, developing algorithms that minimize the human effort in training deep models
is of immense practical importance. Active learning algorithms automatically identify
salient and exemplar samples from large amounts of unlabeled data and can augment
maximal information to supervised learning models, thereby reducing the human annotation
effort in training machine learning models. The goal of this dissertation is to
fuse ideas from deep learning and active learning and design novel deep active learning
algorithms. The proposed learning methodologies explore diverse label spaces to
solve different computer vision applications. Three major contributions have emerged
from this work; (i) a deep active framework for multi-class image classication, (ii)
a deep active model with and without label correlation for multi-label image classi-
cation and (iii) a deep active paradigm for regression. Extensive empirical studies
on a variety of multi-class, multi-label and regression vision datasets corroborate the
potential of the proposed methods for real-world applications. Additional contributions
include: (i) a multimodal emotion database consisting of recordings of facial
expressions, body gestures, vocal expressions and physiological signals of actors enacting
various emotions, (ii) four multimodal deep belief network models and (iii)
an in-depth analysis of the effect of transfer of multimodal emotion features between
source and target networks on classification accuracy and training time. These related
contributions help comprehend the challenges involved in training deep learning
models and motivate the main goal of this dissertation.
depicted commendable performance in a variety of applications. A fundamental challenge
in training deep networks is the requirement of large amounts of labeled training
data. While gathering large quantities of unlabeled data is cheap and easy, annotating
the data is an expensive process in terms of time, labor and human expertise.
Thus, developing algorithms that minimize the human effort in training deep models
is of immense practical importance. Active learning algorithms automatically identify
salient and exemplar samples from large amounts of unlabeled data and can augment
maximal information to supervised learning models, thereby reducing the human annotation
effort in training machine learning models. The goal of this dissertation is to
fuse ideas from deep learning and active learning and design novel deep active learning
algorithms. The proposed learning methodologies explore diverse label spaces to
solve different computer vision applications. Three major contributions have emerged
from this work; (i) a deep active framework for multi-class image classication, (ii)
a deep active model with and without label correlation for multi-label image classi-
cation and (iii) a deep active paradigm for regression. Extensive empirical studies
on a variety of multi-class, multi-label and regression vision datasets corroborate the
potential of the proposed methods for real-world applications. Additional contributions
include: (i) a multimodal emotion database consisting of recordings of facial
expressions, body gestures, vocal expressions and physiological signals of actors enacting
various emotions, (ii) four multimodal deep belief network models and (iii)
an in-depth analysis of the effect of transfer of multimodal emotion features between
source and target networks on classification accuracy and training time. These related
contributions help comprehend the challenges involved in training deep learning
models and motivate the main goal of this dissertation.
ContributorsRanganathan, Hiranmayi (Author) / Sethuraman, Panchanathan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Li, Baoxin (Committee member) / Chakraborty, Shayok (Committee member) / Arizona State University (Publisher)
Created2018
Description
As the demand for wireless systems increases exponentially, it has become necessary
for different wireless modalities, like radar and communication systems, to share the
available bandwidth. One approach to realize coexistence successfully is for each
system to adopt a transmit waveform with a unique nonlinear time-varying phase
function. At the receiver of the system of interest, the waveform received for process-
ing may still suffer from low signal-to-interference-plus-noise ratio (SINR) due to the
presence of the waveforms that are matched to the other coexisting systems. This
thesis uses a time-frequency based approach to increase the SINR of a system by estimating the unique nonlinear instantaneous frequency (IF) of the waveform matched
to the system. Specifically, the IF is estimated using the synchrosqueezing transform,
a highly localized time-frequency representation that also enables reconstruction of
individual waveform components. As the IF estimate is biased, modified versions of
the transform are investigated to obtain estimators that are both unbiased and also
matched to the unique nonlinear phase function of a given waveform. Simulations
using transmit waveforms of coexisting wireless systems are provided to demonstrate
the performance of the proposed approach using both biased and unbiased IF estimators.
for different wireless modalities, like radar and communication systems, to share the
available bandwidth. One approach to realize coexistence successfully is for each
system to adopt a transmit waveform with a unique nonlinear time-varying phase
function. At the receiver of the system of interest, the waveform received for process-
ing may still suffer from low signal-to-interference-plus-noise ratio (SINR) due to the
presence of the waveforms that are matched to the other coexisting systems. This
thesis uses a time-frequency based approach to increase the SINR of a system by estimating the unique nonlinear instantaneous frequency (IF) of the waveform matched
to the system. Specifically, the IF is estimated using the synchrosqueezing transform,
a highly localized time-frequency representation that also enables reconstruction of
individual waveform components. As the IF estimate is biased, modified versions of
the transform are investigated to obtain estimators that are both unbiased and also
matched to the unique nonlinear phase function of a given waveform. Simulations
using transmit waveforms of coexisting wireless systems are provided to demonstrate
the performance of the proposed approach using both biased and unbiased IF estimators.
ContributorsGattani, Vineet Sunil (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Richmond, Christ (Committee member) / Maurer, Alexander (Committee member) / Arizona State University (Publisher)
Created2018
Description
Unmanned aerial vehicles have received increased attention in the last decade due to their versatility, as well as the availability of inexpensive sensors (e.g. GPS, IMU) for their navigation and control. Multirotor vehicles, specifically quadrotors, have formed a fast growing field in robotics, with the range of applications spanning from surveil- lance and reconnaissance to agriculture and large area mapping. Although in most applications single quadrotors are used, there is an increasing interest in architectures controlling multiple quadrotors executing a collaborative task. This thesis introduces a new concept of control involving more than one quadrotors, according to which two quadrotors can be physically coupled in mid-flight. This concept equips the quadro- tors with new capabilities, e.g. increased payload or pursuit and capturing of other quadrotors. A comprehensive simulation of the approach is built to simulate coupled quadrotors. The dynamics and modeling of the coupled system is presented together with a discussion regarding the coupling mechanism, impact modeling and additional considerations that have been investigated. Simulation results are presented for cases of static coupling as well as enemy quadrotor pursuit and capture, together with an analysis of control methodology and gain tuning. Practical implementations are introduced as results show the feasibility of this design.
ContributorsLarsson, Daniel (Author) / Artemiadis, Panagiotis (Thesis advisor) / Marvi, Hamidreza (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2016
Description
In this article-style dissertation, I explore how students used digital technologies, specifically three social media platforms, as multimodal writing platforms while creating a digital portfolio in a senior English class. These platforms are 1) Weebly pages: a website building platform, 2) Weebly Blogs: a feature of Weebly, and 3) Instagram: a photo/video sharing application. Under a multiliteracies lens, I examine the changing nature of literacies and the educational practices surrounding learning literacies when mediated through social media.
First, I conducted an analysis of how the students in this class designed their portfolios. This is done through an examination of each students’ Weebly homepage as well as an in-depth analysis two focal students across each of the social media platforms as illustrative cases. Findings show the students designed complex multimodal compositions that would have otherwise not been possible with the more formal, rigid forms of writing typical to this classroom. Implications for this study include embracing alternative authoring paths in classrooms beyond traditional forms of text-based writing to allow for students’ interests to be included through their designs.
I also examined how students used each of the platforms and the pedagogical implications for those uses. I found that students used Instagram to write multimodally, which allowed them to express ideas in non-traditional ways that are often not present in classrooms. Students used Weebly pages to publically showcase their writing, which afforded them an opportunity to extend their writing to a larger audience. Students used Weebly Blogs to communicate informally, which allowed them to reflect on connections to the text. I offer implications for how teachers can use social media in the classroom.
Finally, I outline how Ms. Lee and her students oriented to the value of writing in this unit. Findings indicate that Ms. Lee, like many others, privileged print-based forms of writing, even in a more expansive project like the portfolio unit. The students oriented to this value by predominantly making meaning through textual modes throughout their portfolios. Implications extend to teachers expanding their classroom practices beyond the traditional forms of literacy for which they are trained.
First, I conducted an analysis of how the students in this class designed their portfolios. This is done through an examination of each students’ Weebly homepage as well as an in-depth analysis two focal students across each of the social media platforms as illustrative cases. Findings show the students designed complex multimodal compositions that would have otherwise not been possible with the more formal, rigid forms of writing typical to this classroom. Implications for this study include embracing alternative authoring paths in classrooms beyond traditional forms of text-based writing to allow for students’ interests to be included through their designs.
I also examined how students used each of the platforms and the pedagogical implications for those uses. I found that students used Instagram to write multimodally, which allowed them to express ideas in non-traditional ways that are often not present in classrooms. Students used Weebly pages to publically showcase their writing, which afforded them an opportunity to extend their writing to a larger audience. Students used Weebly Blogs to communicate informally, which allowed them to reflect on connections to the text. I offer implications for how teachers can use social media in the classroom.
Finally, I outline how Ms. Lee and her students oriented to the value of writing in this unit. Findings indicate that Ms. Lee, like many others, privileged print-based forms of writing, even in a more expansive project like the portfolio unit. The students oriented to this value by predominantly making meaning through textual modes throughout their portfolios. Implications extend to teachers expanding their classroom practices beyond the traditional forms of literacy for which they are trained.
ContributorsStewart, Olivia Grace (Author) / Anderson, Kate T. (Thesis advisor) / Serafini, Frank (Committee member) / Gee, Elisabeth (Committee member) / Arizona State University (Publisher)
Created2017
Description
In baseball, the difference between a win and loss can come down to a single call, such as when an umpire judges force outs at first base by typically comparing competing auditory and visual inputs of the ball-mitt sound and the foot-on-base sight. Yet, because the speed of sound in air only travels about 1100 feet per second, fans observing from several hundred feet away will receive auditory cues that are delayed a significant portion of a second, and thus conceivably could systematically differ in judgments compared to the nearby umpire. The current research examines two questions. 1. How reliably and with what biases do observers judge the order of visual versus auditory events? 2. Do observers making such order judgments from far away systematically compensate for delays due to the slow speed of sound? It is hypothesized that if any temporal bias occurs it is in the direction consistent with observers not accounting for the sound delay, such that increasing viewing distance will increase the bias to assume the sound occurred later. It was found that nearby observers are relatively accurate at judging if a sound occurred before or after a simple visual event (a flash), but exhibit a systematic bias to favor visual stimuli occurring first (by about 30 msec). In contrast, distant observers did not compensate for the delay of the speed of sound such that they systematically favored the visual cue occurring earlier as a function of viewing distance. When observers judged simple visual stimuli in motion relative to the same sound burst, the distance effect occurred as a function of the visual clarity of the ball arriving. In the baseball setting, using a large screen projection of baserunner, a diminished distance effect occurred due to the additional visual cues. In summary, observers generally do not account for the delay of sound due to distance.
ContributorsKrynen, R. Chandler (Author) / McBeath, Michael (Thesis advisor) / Homa, Donald (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
This classroom-based qualitative study examines a multiliteracies approach to teaching Young Adult Literature graphic novels and memoirs within a five-week book club study unit that took place within a twelfth-grade secondary English language arts classroom in an urban school in the Southwest. It explores the teaching and take up of several multiliteracies approaches including written language, oral language, visual representation, audio representation and spatial representation to support adolescents in reading and responding to this unfamiliar genre of Young Adult Literature. Data collection included a demographic survey, pre and post reading habits surveys, student interviews, student drawing and writing in response to texts, visual analysis, and digital graphic narratives. Findings from this study reveal how a multiliteracies approach to teaching Young Adult Literature graphic novels/memoirs supports student reading by allowing for personal and real-world connections to text. It also showed that summarized visual responses to texts in the form of doodling allowed students to come to a deeper understanding of visual literacy through the words and images of the Young Adult Literature graphic novel/memoir. Other findings showed that through the creation of graphic narratives, students grew to appreciate and understand the complexity of Young Adult Literature graphic novels/memoirs as well as discover a newfound appreciation for the genre. Lastly, through participating in literature circle discussions, students gained new insight and perspective from talking in groups on the interpretation of the words and images from their books. In addition, they were able to clarify confusions, work through problems and advance their understanding of their Young Adult Literature graphic novel/memoirs. These findings support the use of a multiliteracies approach to teaching Young Adult Literature graphic novels within the secondary English language arts classroom and point to the value of expanding access to this genre within the formal English language arts curriculum.
ContributorsO'Loughlin, Heather-Ann (Author) / Early, Jessica (Thesis advisor) / Durand, Sybil (Committee member) / Warriner, Doris (Committee member) / Arizona State University (Publisher)
Created2022
Description
There has been a vast increase in applications of Unmanned Aerial Vehicles (UAVs) in civilian domains. To operate in the civilian airspace, a UAV must be able to sense and avoid both static and moving obstacles for flight safety. While indoor and low-altitude environments are mainly occupied by static obstacles, risks in space of higher altitude primarily come from moving obstacles such as other aircraft or flying vehicles in the airspace. Therefore, the ability to avoid moving obstacles becomes a necessity
for Unmanned Aerial Vehicles.
Towards enabling a UAV to autonomously sense and avoid moving obstacles, this thesis makes the following contributions. Initially, an image-based reactive motion planner is developed for a quadrotor to avoid a fast approaching obstacle. Furthermore, A Dubin’s curve based geometry method is developed as a global path planner for a fixed-wing UAV to avoid collisions with aircraft. The image-based method is unable to produce an optimal path and the geometry method uses a simplified UAV model. To compensate
these two disadvantages, a series of algorithms built upon the Closed-Loop Rapid Exploratory Random Tree are developed as global path planners to generate collision avoidance paths in real time. The algorithms are validated in Software-In-the-Loop (SITL) and Hardware-In-the-Loop (HIL) simulations using a fixed-wing UAV model and in real flight experiments using quadrotors. It is observed that the algorithm enables a UAV to avoid moving obstacles approaching to it with different directions and speeds.
for Unmanned Aerial Vehicles.
Towards enabling a UAV to autonomously sense and avoid moving obstacles, this thesis makes the following contributions. Initially, an image-based reactive motion planner is developed for a quadrotor to avoid a fast approaching obstacle. Furthermore, A Dubin’s curve based geometry method is developed as a global path planner for a fixed-wing UAV to avoid collisions with aircraft. The image-based method is unable to produce an optimal path and the geometry method uses a simplified UAV model. To compensate
these two disadvantages, a series of algorithms built upon the Closed-Loop Rapid Exploratory Random Tree are developed as global path planners to generate collision avoidance paths in real time. The algorithms are validated in Software-In-the-Loop (SITL) and Hardware-In-the-Loop (HIL) simulations using a fixed-wing UAV model and in real flight experiments using quadrotors. It is observed that the algorithm enables a UAV to avoid moving obstacles approaching to it with different directions and speeds.
ContributorsLin, Yucong (Author) / Saripalli, Srikanth (Thesis advisor) / Scowen, Paul (Committee member) / Fainekos, Georgios (Committee member) / Thangavelautham, Jekanthan (Committee member) / Youngbull, Cody (Committee member) / Arizona State University (Publisher)
Created2015
Description
This interpretive dissertation study draws upon a sociocultural framework to understand what happened when a seventh-grade teacher introduced drama-based pedagogy into her English Language Arts (ELA) classroom to aid emergent bilinguals’ participation and positioning within the classroom network of practice. The classroom teacher had little training in best practices for supporting emergent bilinguals and no training in drama-based pedagogical approaches for teaching and learning before she participated in this study. I trained the classroom teacher in these practices and provided guidance and feedback during the implementation of drama-based pedagogy. The following research question guided this investigation: What happens when drama-based pedagogy is introduced into a seventh-grade ELA class to support emergent bilinguals? Twenty-seven students from an urban middle school in the southwestern United States participated in this study. According to the state’s English language proficiency exam, three students were identified as English language learners. All three had attended schools in the United States since kindergarten. I conducted classroom observations and interviews with the student and teacher participants to gather data on how emergent bilinguals participated and were positioned during drama-based lessons. Then I analyzed the data corpus using multiple forms of coding, social network analysis, and multimodal interaction analysis. My findings describe the emergent bilinguals’ multimodal classroom interactions with their peers and the teacher during drama-based pedagogy. I present excerpts from interview, reflection meeting, and multimodal transcripts to support my analysis of participation and positioning. Based on my findings, I generated five assertions: (1) emergent bilinguals increased their access to academic resources within the peer academic network after engaging in drama-based pedagogy; (2) emergent bilinguals demonstrated moments of resistance and adaptation during drama-based pedagogy; (3) emergent bilinguals' participation during drama-based pedagogy fluctuated between moments of maintaining and becoming certain kinds of students; (4) incorporating drama-based pedagogy into the seventh-grade ELA class required the teacher to preserve time for more traditional ELA practices while also re-envisioning classroom instruction; and (5) students sometimes misinterpreted teacher facilitation as requirements which limited student agency during drama-based pedagogy. The dissertation concludes with implications for research and practice and outlines potential directions for future research.
ContributorsTroxel Deeg, Megan (Author) / Farrand, Kathleen M. (Thesis advisor) / Moses, Lindsey (Thesis advisor) / Bernstein, Katie (Committee member) / Arizona State University (Publisher)
Created2021