Matching Items (19)
Filtering by
- All Subjects: engineering
Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (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
The use of electromyography (EMG) signals to characterize muscle fatigue has been widely accepted. Initial work on characterizing muscle fatigue during isometric contractions demonstrated that its frequency decreases while its amplitude increases with the onset of fatigue. More recent work concentrated on developing techniques to characterize dynamic contractions for use in clinical and training applications. Studies demonstrated that as fatigue progresses, the EMG signal undergoes a shift in frequency, and different physiological mechanisms on the possible cause of the shift were considered. Time-frequency processing, using the Wigner distribution or spectrogram, is one of the techniques used to estimate the instantaneous mean frequency and instantaneous median frequency of the EMG signal using a variety of techniques. However, these time-frequency methods suffer either from cross-term interference when processing signals with multiple components or time-frequency resolution due to the use of windowing. This study proposes the use of the matching pursuit decomposition (MPD) with a Gaussian dictionary to process EMG signals produced during both isometric and dynamic contractions. In particular, the MPD obtains unique time-frequency features that represent the EMG signal time-frequency dependence without suffering from cross-terms or loss in time-frequency resolution. As the MPD does not depend on an analysis window like the spectrogram, it is more robust in applying the timefrequency features to identify the spectral time-variation of the EGM signal.
ContributorsAustin, Hiroko (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2012
Description
Parkinson's disease is a neurodegenerative condition diagnosed on patients with
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
ContributorsDutta, Arindam (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Holbert, Keith E. (Committee member) / Bliss, Daniel W. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Tracking a time-varying number of targets is a challenging
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
ContributorsEbenezer, Samuel P (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2015
Description
This work considers the problem of multiple detection and tracking in two complex time-varying environments, urban terrain and underwater. Tracking multiple radar targets in urban environments is rst investigated by exploiting multipath signal returns, wideband underwater acoustic (UWA) communications channels are estimated using adaptive learning methods, and multiple UWA communications users are detected by designing the transmit signal to match the environment. For the urban environment, a multi-target tracking algorithm is proposed that integrates multipath-to-measurement association and the probability hypothesis density method implemented using particle filtering. The algorithm is designed to track an unknown time-varying number of targets by extracting information from multiple measurements due to multipath returns in the urban terrain. The path likelihood probability is calculated by considering associations between measurements and multipath returns, and an adaptive clustering algorithm is used to estimate the number of target and their corresponding parameters. The performance of the proposed algorithm is demonstrated for different multiple target scenarios and evaluated using the optimal subpattern assignment metric. The underwater environment provides a very challenging communication channel due to its highly time-varying nature, resulting in large distortions due to multipath and Doppler-scaling, and frequency-dependent path loss. A model-based wideband UWA channel estimation algorithm is first proposed to estimate the channel support and the wideband spreading function coefficients. A nonlinear frequency modulated signaling scheme is proposed that is matched to the wideband characteristics of the underwater environment. Constraints on the signal parameters are derived to optimally reduce multiple access interference and the UWA channel effects. The signaling scheme is compared to a code division multiple access (CDMA) scheme to demonstrate its improved bit error rate performance. The overall multi-user communication system performance is finally analyzed by first estimating the UWA channel and then designing the signaling scheme for multiple communications users.
ContributorsZhou, Meng (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Horn antennas have been used for over a hundred years. They have a wide variety of uses where they are a basic and popular microwave antenna for many practical applications, such as feed elements for communication reflector dishes on satellite or point-to-point relay antennas. They are also widely utilized as gain standards for calibration and gain measurement of other antennas.
The gain and loss factor of conical horns are revisited in this dissertation based on
spherical and quadratic aperture phase distributions. The gain is compared with published classical data in an attempt to confirm their validity and accuracy and to determine whether they were derived based on spherical or quadratic aperture phase distributions. In this work, it is demonstrated that the gain of a conical horn antenna obtained by using a spherical phase distribution is in close agreement with published classical data. Moreover, more accurate expressions for the loss factor, to account for amplitude and phase tapers over the horn aperture, are derived. New formulas for the design of optimum gain conical horns, based on the more accurate spherical aperture phase distribution, are derived.
To better understand the impact of edge diffractions on aperture antenna performance, an extensive investigation of the edge diffractions impact is undertaken in this dissertation for commercial aperture antennas. The impact of finite uncoated and coated PEC ground plane edge diffractions on the amplitude patterns in the principal planes of circular apertures is intensively examined. Similarly, aperture edge diffractions of aperture antennas without ground planes are examined. Computational results obtained by the analytical model are compared with experimental and HFSS-simulated results for all cases studied. In addition, the impact of the ground plane size, coating thickness, and relative permittivity of the dielectric layer on the radiation amplitude in the back region has been examined.
This investigation indicates that the edge diffractions do impact the main forward lobe pattern, especially in the E plane. Their most significant contribution appears in far side and back lobes. This work demonstrates that the finite edge contributors must be considered to obtain more accurate amplitude patterns of aperture antennas.
The gain and loss factor of conical horns are revisited in this dissertation based on
spherical and quadratic aperture phase distributions. The gain is compared with published classical data in an attempt to confirm their validity and accuracy and to determine whether they were derived based on spherical or quadratic aperture phase distributions. In this work, it is demonstrated that the gain of a conical horn antenna obtained by using a spherical phase distribution is in close agreement with published classical data. Moreover, more accurate expressions for the loss factor, to account for amplitude and phase tapers over the horn aperture, are derived. New formulas for the design of optimum gain conical horns, based on the more accurate spherical aperture phase distribution, are derived.
To better understand the impact of edge diffractions on aperture antenna performance, an extensive investigation of the edge diffractions impact is undertaken in this dissertation for commercial aperture antennas. The impact of finite uncoated and coated PEC ground plane edge diffractions on the amplitude patterns in the principal planes of circular apertures is intensively examined. Similarly, aperture edge diffractions of aperture antennas without ground planes are examined. Computational results obtained by the analytical model are compared with experimental and HFSS-simulated results for all cases studied. In addition, the impact of the ground plane size, coating thickness, and relative permittivity of the dielectric layer on the radiation amplitude in the back region has been examined.
This investigation indicates that the edge diffractions do impact the main forward lobe pattern, especially in the E plane. Their most significant contribution appears in far side and back lobes. This work demonstrates that the finite edge contributors must be considered to obtain more accurate amplitude patterns of aperture antennas.
ContributorsAboserwal, Nafati Abdasallam (Author) / Balanis, Constantine A (Thesis advisor) / Aberle, James T (Committee member) / Pan, George (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Demands in file size and transfer rates for consumer-orientated products have escalated in recent times. This is primarily due to the emergence of high definition video content. Now factor in the consumer desire for convenience, and we find that wireless service is the most desired approach for inter-connectivity. Consumers expect wireless service to emulate wired service with little to virtually no difference in quality of service (QoS). The background section of this document examines the QoS requirements for wireless connectivity of high definition video applications. I then proceed to look at proposed solutions at the physical (PHY) and the media access control (MAC) layers as well as cross-layer schemes. These schemes are subsequently are evaluated in terms of usefulness in a multi-gigabit, 60 GHz wireless multimedia system targeting the average consumer. It is determined that a substantial gap in published literature exists pertinent to this application. Specifically, little or no work has been found that shows how an adaptive PHYMAC cross-layer solution that provides real-time compensation for varying channel conditions might be actually implemented. Further, no work has been found that shows results of such a model. This research proposes, develops and implements in Matlab code an alternate cross-layer solution that will provide acceptable QoS service for multimedia applications. Simulations using actual high definition video sequences are used to test the proposed solution. Results based on the average PSNR metric show that a quasi-adaptive algorithm provides greater than 7 dB of improvement over a non-adaptive approach while a fully-adaptive alogrithm provides over18 dB of improvement. The fully adaptive implementation has been conclusively shown to be superior to non-adaptive techniques and sufficiently superior to even quasi-adaptive algorithms.
ContributorsBosco, Bruce (Author) / Reisslein, Martin (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011