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 140
Increasing the efficiency of Doppler processing and backend processing in medical ultrasound systems
Description
Ultrasound imaging is one of the major medical imaging modalities. It is cheap, non-invasive and has low power consumption. Doppler processing is an important part of many ultrasound imaging systems. It is used to provide blood velocity information and is built on top of B-mode systems. We investigate the performance of two velocity estimation schemes used in Doppler processing systems, namely, directional velocity estimation (DVE) and conventional velocity estimation (CVE). We find that DVE provides better estimation performance and is the only functioning method when the beam to flow angle is large. Unfortunately, DVE is computationally expensive and also requires divisions and square root operations that are hard to implement. We propose two approximation techniques to replace these computations. The simulation results on cyst images show that the proposed approximations do not affect the estimation performance. We also study backend processing which includes envelope detection, log compression and scan conversion. Three different envelope detection methods are compared. Among them, FIR based Hilbert Transform is considered the best choice when phase information is not needed, while quadrature demodulation is a better choice if phase information is necessary. Bilinear and Gaussian interpolation are considered for scan conversion. Through simulations of a cyst image, we show that bilinear interpolation provides comparable contrast-to-noise ratio (CNR) performance with Gaussian interpolation and has lower computational complexity. Thus, bilinear interpolation is chosen for our system.
ContributorsWei, Siyuan (Author) / Chakrabarti, Chaitali (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Stroke remains the leading cause of adult disability in developed countries. Most survivors live with residual motor impairments that severely diminish independence and quality of life. After stroke, the only accepted treatment for these patients is motor rehabilitation. However, the amount and kind of rehabilitation required to induce clinically significant improvements in motor function is rarely given due to the constraints of our current health care system. Research reported in this dissertation contributes towards developing adjuvant therapies that may augment the impact of motor rehabilitation and improve functional outcome. These studies have demonstrated reorganization of maps within motor cortex as a function of experience in both healthy and brain-injured animals by using intracortical microstimulation technique. Furthermore, synaptic plasticity has been identified as a key neural mechanism in directing motor map plasticity, evidenced by restoration of movement representations within the spared cortical tissue accompanied by increase in synapse number translating into motor improvement after stroke. There is increasing evidence that brain-derived neurotrophic factor (BDNF) modulates synaptic and morphological plasticity in the developing and mature nervous system. Unfortunately, BDNF itself is a poor candidate because of its short half-life, low penetration through the blood brain barrier, and activating multiple receptor units, p75 and TrkB on the neuronal membrane. In order to circumvent this problem efficacy of two recently developed novel TrkB agonists, LM22A-4 and 7,8-dihydroxyflavone, that actively penetrate the blood brain barrier and enhance functional recovery. Findings from these dissertation studies indicate that administration of these pharmacological compounds, accompanied by motor rehabilitation provide a powerful therapeutic tool for stroke recovery.
ContributorsWarraich, Zuha (Author) / Kleim, Jeffrey A (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Tillery, Stephen-Helms (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
The development of advanced, anthropomorphic artificial hands aims to provide upper extremity amputees with improved functionality for activities of daily living. However, many state-of-the-art hands have a large number of degrees of freedom that can be challenging to control in an intuitive manner. Automated grip responses could be built into artificial hands in order to enhance grasp stability and reduce the cognitive burden on the user. To this end, three studies were conducted to understand how human hands respond, passively and actively, to unexpected perturbations of a grasped object along and about different axes relative to the hand. The first study investigated the effect of magnitude, direction, and axis of rotation on precision grip responses to unexpected rotational perturbations of a grasped object. A robust "catch-up response" (a rapid, pulse-like increase in grip force rate previously reported only for translational perturbations) was observed whose strength scaled with the axis of rotation. Using two haptic robots, we then investigated the effects of grip surface friction, axis, and direction of perturbation on precision grip responses for unexpected translational and rotational perturbations for three different hand-centric axes. A robust catch-up response was observed for all axes and directions for both translational and rotational perturbations. Grip surface friction had no effect on the stereotypical catch-up response. Finally, we characterized the passive properties of the precision grip-object system via robot-imposed impulse perturbations. The hand-centric axis associated with the greatest translational stiffness was different than that for rotational stiffness. This work expands our understanding of the passive and active features of precision grip, a hallmark of human dexterous manipulation. Biological insights such as these could be used to enhance the functionality of artificial hands and the quality of life for upper extremity amputees.
ContributorsDe Gregorio, Michael (Author) / Santos, Veronica J. (Thesis advisor) / Artemiadis, Panagiotis K. (Committee member) / Santello, Marco (Committee member) / Sugar, Thomas (Committee member) / Helms Tillery, Stephen I. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Intracortical microstimulation (ICMS) within somatosensory cortex can produce artificial sensations including touch, pressure, and vibration. There is significant interest in using ICMS to provide sensory feedback for a prosthetic limb. In such a system, information recorded from sensors on the prosthetic would be translated into electrical stimulation and delivered directly to the brain, providing feedback about features of objects in contact with the prosthetic. To achieve this goal, multiple simultaneous streams of information will need to be encoded by ICMS in a manner that produces robust, reliable, and discriminable sensations. The first segment of this work focuses on the discriminability of sensations elicited by ICMS within somatosensory cortex. Stimulation on multiple single electrodes and near-simultaneous stimulation across multiple electrodes, driven by a multimodal tactile sensor, were both used in these experiments. A SynTouch BioTac sensor was moved across a flat surface in several directions, and a subset of the sensor's electrode impedance channels were used to drive multichannel ICMS in the somatosensory cortex of a non-human primate. The animal performed a behavioral task during this stimulation to indicate the discriminability of sensations evoked by the electrical stimulation. The animal's responses to ICMS were somewhat inconsistent across experimental sessions but indicated that discriminable sensations were evoked by both single and multichannel ICMS. The factors that affect the discriminability of stimulation-induced sensations are not well understood, in part because the relationship between ICMS and the neural activity it induces is poorly defined. The second component of this work was to develop computational models that describe the populations of neurons likely to be activated by ICMS. Models of several neurons were constructed, and their responses to ICMS were calculated. A three-dimensional cortical model was constructed using these cell models and used to identify the populations of neurons likely to be recruited by ICMS. Stimulation activated neurons in a sparse and discontinuous fashion; additionally, the type, number, and location of neurons likely to be activated by stimulation varied with electrode depth.
ContributorsOverstreet, Cynthia K (Author) / Helms Tillery, Stephen I (Thesis advisor) / Santos, Veronica (Committee member) / Buneo, Christopher (Committee member) / Otto, Kevin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
In order to successfully implement a neural prosthetic system, it is necessary to understand the control of limb movements and the representation of body position in the nervous system. As this development process continues, it is becoming increasingly important to understand the way multiple sensory modalities are used in limb representation. In a previous study, Shi et al. (2013) examined the multimodal basis of limb position in the superior parietal lobule (SPL) as monkeys reached to and held their arm at various target locations in a frontal plane. Visual feedback was withheld in half the trials, though non-visual (i.e. somatic) feedback was available in all trials. Previous analysis showed that some of the neurons were tuned to limb position and that some neurons had their response modulated by the presence or absence of visual feedback. This modulation manifested in decreases in firing rate variability in the vision condition as compared to nonvision. The decreases in firing rate variability, as shown through decreases in both the Fano factor of spike counts and the coefficient of variation of the inter-spike intervals, suggested that changes were taking place in both trial-by-trial and intra-trial variability. I sought to further probe the source of the change in intra-trial variability through spectral analysis. It was hypothesized that the presence of temporal structure in the vision condition would account for a regularity in firing that would have decreased intra-trial variability. While no peaks were apparent in the spectra, differences in spectral power between visual conditions were found. These differences are suggestive of unique temporal spiking patterns at the individual neuron level that may be influential at the population level.
ContributorsDyson, Keith (Author) / Buneo, Christopher A (Thesis advisor) / Helms-Tillery, Stephen I (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.
ContributorsDasarathan, Sivaraman (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Reisslein, Martin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Reaching movements are subject to noise in both the planning and execution phases of movement production. Although the effects of these noise sources in estimating and/or controlling endpoint position have been examined in many studies, the independent effects of limb configuration on endpoint variability have been largely ignored. The present study investigated the effects of arm configuration on the interaction between planning noise and execution noise. Subjects performed reaching movements to three targets located in a frontal plane. At the starting position, subjects matched one of two desired arm configuration 'templates' namely "adducted" and "abducted". These arm configurations were obtained by rotations along the shoulder-hand axis, thereby maintaining endpoint position. Visual feedback of the hand was varied from trial to trial, thereby increasing uncertainty in movement planning and execution. It was hypothesized that 1) pattern of endpoint variability would be dependent on arm configuration and 2) that these differences would be most apparent in conditions without visual feedback. It was found that there were differences in endpoint variability between arm configurations in both visual conditions, but these differences were much larger when visual feedback was withheld. The overall results suggest that patterns of endpoint variability are highly dependent on arm configuration, particularly in the absence of visual feedback. This suggests that in the presence of vision, movement planning in 3D space is performed using coordinates that are largely arm configuration independent (i.e. extrinsic coordinates). In contrast, in the absence of vision, movement planning in 3D space reflects a substantial contribution of intrinsic coordinates.
ContributorsLakshmi Narayanan, Kishor (Author) / Buneo, Christopher (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving reliable composite systems is a strong capability of assessing and inspecting physical damage of critical structural components. Installation of a robust Structural Health Monitoring (SHM) system would be very valuable in detecting the onset of composite failure. A number of major issues still require serious attention in connection with the research and development aspects of sensor-integrated reliable SHM systems for composite structures. In particular, the sensitivity of currently available sensor systems does not allow detection of micro level damage; this limits the capability of data driven SHM systems. As a fundamental layer in SHM, modeling can provide in-depth information on material and structural behavior for sensing and detection, as well as data for learning algorithms. This dissertation focusses on the development of a multiscale analysis framework, which is used to detect various forms of damage in complex composite structures. A generalized method of cells based micromechanics analysis, as implemented in NASA's MAC/GMC code, is used for the micro-level analysis. First, a baseline study of MAC/GMC is performed to determine the governing failure theories that best capture the damage progression. The deficiencies associated with various layups and loading conditions are addressed. In most micromechanics analysis, a representative unit cell (RUC) with a common fiber packing arrangement is used. The effect of variation in this arrangement within the RUC has been studied and results indicate this variation influences the macro-scale effective material properties and failure stresses. The developed model has been used to simulate impact damage in a composite beam and an airfoil structure. The model data was verified through active interrogation using piezoelectric sensors. The multiscale model was further extended to develop a coupled damage and wave attenuation model, which was used to study different damage states such as fiber-matrix debonding in composite structures with surface bonded piezoelectric sensors.
ContributorsMoncada, Albert (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2012