Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 16
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- All Subjects: engineering
- Creators: Turaga, Pavan
Description
Humans are capable of transferring learning for anticipatory control of dexterous object manipulation despite changes in degrees-of-freedom (DoF), i.e., switching from lifting an object with two fingers to lifting the same object with three fingers. However, the role that tactile information plays in this transfer of learning is unknown. In this study, subjects lifted an L-shaped object with two fingers (2-DoF), and then lifted the object with three fingers (3-DoF). The subjects were divided into two groups--one group performed the task wearing a glove (to reduce tactile sensibility) upon the switch to 3-DoF (glove group), while the other group did not wear the glove (control group). Compensatory moment (torque) was used as a measure to determine how well the subject could minimize the tilt of the object following the switch from 2-DoF to 3-DoF. Upon the switch to 3-DoF, subjects wearing the glove generated a compensatory moment (Mcom) that had a significantly higher error than the average of the last five trials at the end of the 3-DoF block (p = 0.012), while the control subjects did not demonstrate a significant difference in Mcom. Additional effects of the reduction in tactile sensibility were: (1) the grip force for the group of subjects wearing the glove was significantly higher in the 3-DoF trials compared to the 2-DoF trials (p = 0.014), while the grip force of the control subjects was not significantly different; (2) the difference in centers of pressure between the thumb and fingers (ΔCoP) significantly increased in the 3-DoF block for the group of subjects wearing the glove, while the ΔCoP of the control subjects was not significantly different; (3) lastly, the control subjects demonstrated a greater increase in lift force than the group of subjects wearing the glove (though results were not significant). Combined together, these results suggest different force modulation strategies are used depending on the amount of tactile feedback that is available to the subject. Therefore, reduction of tactile sensibility has important effects on subjects' ability to transfer learned manipulation across different DoF contexts.
ContributorsGaw, Nathan (Author) / Helms Tillery, Stephen (Thesis advisor) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ability to identify unoccupied resources in the radio spectrum is a key capability for opportunistic users in a cognitive radio environment. This paper draws upon and extends geometrically based ideas in statistical signal processing to develop estimators for the rank and the occupied subspace in a multi-user environment from multiple temporal samples of the signal received at a single antenna. These estimators enable identification of resources, such as the orthogonal complement of the occupied subspace, that may be exploitable by an opportunistic user. This concept is supported by simulations showing the estimation of the number of users in a simple CDMA system using a maximum a posteriori (MAP) estimate for the rank. It was found that with suitable parameters, such as high SNR, sufficient number of time epochs and codes of appropriate length, the number of users could be correctly estimated using the MAP estimator even when the noise variance is unknown. Additionally, the process of identifying the maximum likelihood estimate of the orthogonal projector onto the unoccupied subspace is discussed.
ContributorsBeaudet, Kaitlyn (Author) / Cochran, Douglas (Thesis advisor) / Turaga, Pavan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Situations of sensory overload are steadily becoming more frequent as the ubiquity of technology approaches reality--particularly with the advent of socio-communicative smartphone applications, and pervasive, high speed wireless networks. Although the ease of accessing information has improved our communication effectiveness and efficiency, our visual and auditory modalities--those modalities that today's computerized devices and displays largely engage--have become overloaded, creating possibilities for distractions, delays and high cognitive load; which in turn can lead to a loss of situational awareness, increasing chances for life threatening situations such as texting while driving. Surprisingly, alternative modalities for information delivery have seen little exploration. Touch, in particular, is a promising candidate given that it is our largest sensory organ with impressive spatial and temporal acuity. Although some approaches have been proposed for touch-based information delivery, they are not without limitations including high learning curves, limited applicability and/or limited expression. This is largely due to the lack of a versatile, comprehensive design theory--specifically, a theory that addresses the design of touch-based building blocks for expandable, efficient, rich and robust touch languages that are easy to learn and use. Moreover, beyond design, there is a lack of implementation and evaluation theories for such languages. To overcome these limitations, a unified, theoretical framework, inspired by natural, spoken language, is proposed called Somatic ABC's for Articulating (designing), Building (developing) and Confirming (evaluating) touch-based languages. To evaluate the usefulness of Somatic ABC's, its design, implementation and evaluation theories were applied to create communication languages for two very unique application areas: audio described movies and motor learning. These applications were chosen as they presented opportunities for complementing communication by offloading information, typically conveyed visually and/or aurally, to the skin. For both studies, it was found that Somatic ABC's aided the design, development and evaluation of rich somatic languages with distinct and natural communication units.
ContributorsMcDaniel, Troy Lee (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea behind discontinuity is locating the abrupt changes in intensity of images, as are often seen in edges or boundaries. Similarity subdivides an image into regions that fit the pre-defined criteria. The algorithm utilized in this thesis is the second category.
This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations.
This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations.
ContributorsHan, Dongmin (Author) / Frakes, David (Thesis advisor) / Adrian, Ronald (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Several music players have evolved in multi-dimensional and surround sound systems. The audio players are implemented as software applications for different audio hardware systems. Digital formats and wireless networks allow for audio content to be readily accessible on smart networked devices. Therefore, different audio output platforms ranging from multispeaker high-end surround systems to single unit Bluetooth speakers have been developed. A large body of research has been carried out in audio processing, beamforming, sound fields etc. and new formats are developed to create realistic audio experiences.
An emerging trend is seen towards high definition AV systems, virtual reality gears as well as gaming applications with multidimensional audio. Next generation media technology is concentrating around Virtual reality experience and devices. It has applications not only in gaming but all other fields including medical, entertainment, engineering, and education. All such systems also require realistic audio corresponding with the visuals.
In the project presented in this thesis, a new portable audio hardware system is designed and developed along with a dedicated mobile android application to render immersive surround sound experiences with real-time audio effects. The tablet and mobile phone allow the user to control or “play” with sound directionality and implement various audio effects including sound rotation, spatialization, and other immersive experiences. The thesis describes the hardware and software design, provides the theory of the sound effects, and presents demonstrations of the sound application that was created.
An emerging trend is seen towards high definition AV systems, virtual reality gears as well as gaming applications with multidimensional audio. Next generation media technology is concentrating around Virtual reality experience and devices. It has applications not only in gaming but all other fields including medical, entertainment, engineering, and education. All such systems also require realistic audio corresponding with the visuals.
In the project presented in this thesis, a new portable audio hardware system is designed and developed along with a dedicated mobile android application to render immersive surround sound experiences with real-time audio effects. The tablet and mobile phone allow the user to control or “play” with sound directionality and implement various audio effects including sound rotation, spatialization, and other immersive experiences. The thesis describes the hardware and software design, provides the theory of the sound effects, and presents demonstrations of the sound application that was created.
ContributorsDharmadhikari, Chinmay (Author) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Ingalls, Todd (Committee member) / Arizona State University (Publisher)
Created2016
Description
The human motion is defined as an amalgamation of several physical traits such as bipedal locomotion, posture and manual dexterity, and mental expectation. In addition to the “positive” body form defined by these traits, casting light on the body produces a “negative” of the body: its shadow. We often interchangeably use with silhouettes in the place of shadow to emphasize indifference to interior features. In a manner of speaking, the shadow is an alter ego that imitates the individual.
The principal value of shadow is its non-invasive behaviour of reflecting precisely the actions of the individual it is attached to. Nonetheless we can still think of the body’s shadow not as the body but its alter ego.
Based on this premise, my thesis creates an experiential system that extracts the data related to the contour of your human shape and gives it a texture and life of its own, so as to emulate your movements and postures, and to be your extension. In technical terms, my thesis extracts abstraction from a pre-indexed database that could be generated from an offline data set or in real time to complement these actions of a user in front of a low-cost optical motion capture device like the Microsoft Kinect. This notion could be the system’s interpretation of the action which creates modularized art through the abstraction’s ‘similarity’ to the live action.
Through my research, I have developed a stable system that tackles various connotations associated with shadows and the need to determine the ideal features that contribute to the relevance of the actions performed. The implication of Factor Oracle [3] pattern interpretation is tested with a feature bin of videos. The system also is flexible towards several methods of Nearest Neighbours searches and a machine learning module to derive the same output. The overall purpose is to establish this in real time and provide a constant feedback to the user. This can be expanded to handle larger dynamic data.
In addition to estimating human actions, my thesis best tries to test various Nearest Neighbour search methods in real time depending upon the data stream. This provides a basis to understand varying parameters that complement human activity recognition and feature matching in real time.
The principal value of shadow is its non-invasive behaviour of reflecting precisely the actions of the individual it is attached to. Nonetheless we can still think of the body’s shadow not as the body but its alter ego.
Based on this premise, my thesis creates an experiential system that extracts the data related to the contour of your human shape and gives it a texture and life of its own, so as to emulate your movements and postures, and to be your extension. In technical terms, my thesis extracts abstraction from a pre-indexed database that could be generated from an offline data set or in real time to complement these actions of a user in front of a low-cost optical motion capture device like the Microsoft Kinect. This notion could be the system’s interpretation of the action which creates modularized art through the abstraction’s ‘similarity’ to the live action.
Through my research, I have developed a stable system that tackles various connotations associated with shadows and the need to determine the ideal features that contribute to the relevance of the actions performed. The implication of Factor Oracle [3] pattern interpretation is tested with a feature bin of videos. The system also is flexible towards several methods of Nearest Neighbours searches and a machine learning module to derive the same output. The overall purpose is to establish this in real time and provide a constant feedback to the user. This can be expanded to handle larger dynamic data.
In addition to estimating human actions, my thesis best tries to test various Nearest Neighbour search methods in real time depending upon the data stream. This provides a basis to understand varying parameters that complement human activity recognition and feature matching in real time.
ContributorsSeshasayee, Sudarshan Prashanth (Author) / Sha, Xin Wei (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Tinapple, David A (Committee member) / Arizona State University (Publisher)
Created2016
Description
When dancers are granted agency over music, as in interactive dance systems, the actors are most often concerned with the problem of creating a staged performance for an audience. However, as is reflected by the above quote, the practice of Argentine tango social dance is most concerned with participants internal experience and their relationship to the broader tango community. In this dissertation I explore creative approaches to enrich the sense of connection, that is, the experience of oneness with a partner and complete immersion in music and dance for Argentine tango dancers by providing agency over musical activities through the use of interactive technology. Specifically, I create an interactive dance system that allows tango dancers to affect and create music via their movements in the context of social dance. The motivations for this work are multifold: 1) to intensify embodied experience of the interplay between dance and music, individual and partner, couple and community, 2) to create shared experience of the conventions of tango dance, and 3) to innovate Argentine tango social dance practice for the purposes of education and increasing musicality in dancers.
ContributorsBrown, Courtney Douglass (Author) / Paine, Garth (Thesis advisor) / Feisst, Sabine (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2017
Description
Millions of individuals suffer from gait impairments due to stroke or other neurological disorders. A primary goal of patients is to walk independently, but most patients only achieve a poor functional outcome five years after injury. Despite the growing interest in using robotic devices for rehabilitation of sensorimotor function, state-of-the-art robotic interventions in gait therapy have not resulted in improved outcomes when compared to traditional treadmill-based therapy. Because bipedal walking requires neural coupling and dynamic interactions between the legs, a fundamental understanding of the sensorimotor mechanisms of inter-leg coordination during walking is needed to inform robotic interventions in gait therapy. This dissertation presents a systematic exploration of sensorimotor mechanisms of inter-leg coordination by studying the effect of unilateral perturbations of the walking surface stiffness on contralateral muscle activation in healthy populations. An analysis of the contribution of several sensory modalities to the muscle activation of the opposite leg provides new insight into the sensorimotor control mechanisms utilized in human walking, including the role of supra-spinal neural circuits in inter-leg coordination. Based on these insights, a model is created which relates the unilateral deflection of the walking surface to the resulting neuromuscular activation in the opposite leg. Additionally, case studies with hemiplegic walkers indicate the existence of the observed mechanism in neurologically impaired walkers. The results of this dissertation suggest a novel approach to gait therapy for hemiplegic patients in which desired muscle activity is evoked in the impaired leg by only interacting with the healthy leg. One of the most significant advantages of this approach over current rehabilitation protocols is the safety of the patient since there is no direct manipulation of the impaired leg. Therefore, the methods and results presented in this dissertation represent a potential paradigm shift in robot-assisted gait therapy.
ContributorsSkidmore, Jeffrey Alan (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2017
Description
This thesis encompasses a comprehensive research effort dedicated to overcoming the critical bottlenecks that hinder the current generation of neural networks, thereby significantly advancing their reliability and performance. Deep neural networks, with their millions of parameters, suffer from over-parameterization and lack of constraints, leading to limited generalization capabilities. In other words, the complex architecture and millions of parameters present challenges in finding the right balance between capturing useful patterns and avoiding noise in the data. To address these issues, this thesis explores novel solutions based on knowledge distillation, enabling the learning of robust representations. Leveraging the capabilities of large-scale networks, effective learning strategies are developed. Moreover, the limitations of dependency on external networks in the distillation process, which often require large-scale models, are effectively overcome by proposing a self-distillation strategy. The proposed approach empowers the model to generate high-level knowledge within a single network, pushing the boundaries of knowledge distillation. The effectiveness of the proposed method is not only demonstrated across diverse applications, including image classification, object detection, and semantic segmentation but also explored in practical considerations such as handling data scarcity and assessing the transferability of the model to other learning tasks. Another major obstacle hindering the development of reliable and robust models lies in their black-box nature, impeding clear insights into the contributions toward the final predictions and yielding uninterpretable feature representations. To address this challenge, this thesis introduces techniques that incorporate simple yet powerful deep constraints rooted in Riemannian geometry. These constraints confer geometric qualities upon the latent representation, thereby fostering a more interpretable and insightful representation. In addition to its primary focus on general tasks like image classification and activity recognition, this strategy offers significant benefits in real-world applications where data scarcity is prevalent. Moreover, its robustness in feature removal showcases its potential for edge applications. By successfully tackling these challenges, this research contributes to advancing the field of machine learning and provides a foundation for building more reliable and robust systems across various application domains.
ContributorsChoi, Hongjun (Author) / Turaga, Pavan (Thesis advisor) / Jayasuriya, Suren (Committee member) / Li, Wenwen (Committee member) / Fazli, Pooyan (Committee member) / Arizona State University (Publisher)
Created2023