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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

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.
ContributorsDutta, Arindam (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Holbert, Keith E. (Committee member) / Bliss, Daniel W. (Committee member) / Arizona State University (Publisher)
Created2015
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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

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
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Description
Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or

Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
ContributorsBliss, Nadya Travinin (Author) / Laubichler, Manfred (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used to identify antigen antibody binding patterns or immunosignatures. In this thesis, an advanced signal processing method is proposed to estimate

Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used to identify antigen antibody binding patterns or immunosignatures. In this thesis, an advanced signal processing method is proposed to estimate epitope antigen subsequences as well as identify mimotope antigen subsequences that mimic the structure of epitopes from random-sequence peptide microarrays. The method first maps peptide sequences to linear expansions of highly-localized one-dimensional (1-D) time-varying signals and uses a time-frequency processing technique to detect recurring patterns in subsequences. This technique is matched to the aforementioned mapping scheme, and it allows for an inherent analysis on how substitutions in the subsequences can affect antibody binding strength. The performance of the proposed method is demonstrated by estimating epitopes and identifying potential mimotopes for eight monoclonal antibody samples.

The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein.

In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations.
ContributorsO'Donnell, Brian (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Johnston, Stephen A. (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition

Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using

Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using a particle filter (PF) integrated with Interacting Multiple Models (IMMs) to compensate and adapt to the transition between different clutter densities. This model was implemented for the case of a monostatic sensor tracking a single target moving with constant velocity along a two-dimensional trajectory, which crossed between regions of drastically different clutter densities. Multiple combinations of clutter density transitions were considered, using up to three different clutter densities. It was shown that the integrated IMM PF algorithm outperforms traditional approaches such as the PF in terms of tracking results and performance. The minimal additional computational expense of including the IMM more than warrants the benefits of having it supplement and amplify the advantages of the PF.
ContributorsDutson, Karl (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Bliss, Daniel W (Committee member) / Arizona State University (Publisher)
Created2015
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Description
In recent years, there has been an increased interest in sharing available bandwidth to avoid spectrum congestion. With an ever-increasing number wireless users, it is critical to develop signal processing based spectrum sharing algorithms to achieve cooperative use of the allocated spectrum among multiple systems in order to reduce

In recent years, there has been an increased interest in sharing available bandwidth to avoid spectrum congestion. With an ever-increasing number wireless users, it is critical to develop signal processing based spectrum sharing algorithms to achieve cooperative use of the allocated spectrum among multiple systems in order to reduce interference between systems. This work studies the radar and communications systems coexistence problem using two main approaches. The first approach develops methodologies to increase radar target tracking performance under low signal-to-interference-plus-noise ratio (SINR) conditions due to the coexistence of strong communications interference. The second approach jointly optimizes the performance of both systems by co-designing a common transmit waveform.

When concentrating on improving radar tracking performance, a pulsed radar that is tracking a single target coexisting with high powered communications interference is considered. Although the Cramer-Rao lower bound (CRLB) on the covariance of an unbiased estimator of deterministic parameters provides a bound on the estimation mean squared error (MSE), there exists an SINR threshold at which estimator covariance rapidly deviates from the CRLB. After demonstrating that different radar waveforms experience different estimation SINR thresholds using the Barankin bound (BB), a new radar waveform design method is proposed based on predicting the waveform-dependent BB SINR threshold under low SINR operating conditions.

A novel method of predicting the SINR threshold value for maximum likelihood estimation (MLE) is proposed. A relationship is shown to exist between the formulation of the BB kernel and the probability of selecting sidelobes for the MLE. This relationship is demonstrated as an accurate means of threshold prediction for the radar target parameter estimation of frequency, time-delay and angle-of-arrival.



For the co-design radar and communications system problem, the use of a common transmit waveform for a pulse-Doppler radar and a multiuser communications system is proposed. The signaling scheme for each system is selected from a class of waveforms with nonlinear phase function by optimizing the waveform parameters to minimize interference between the two systems and interference among communications users. Using multi-objective optimization, a trade-off in system performance is demonstrated when selecting waveforms that minimize both system interference and tracking MSE.
ContributorsKota, John S (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Berisha, Visar (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2016
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Description
The continuous time-tagging of photon arrival times for high count rate sources isnecessary for applications such as optical communications, quantum key encryption, and astronomical measurements. Detection of Hanbury-Brown and Twiss (HBT) single photon correlations from thermal sources, such as stars, requires a combination of high dynamic range, long integration times, and low systematics

The continuous time-tagging of photon arrival times for high count rate sources isnecessary for applications such as optical communications, quantum key encryption, and astronomical measurements. Detection of Hanbury-Brown and Twiss (HBT) single photon correlations from thermal sources, such as stars, requires a combination of high dynamic range, long integration times, and low systematics in the photon detection and time tagging system. The continuous nature of the measurements and the need for highly accurate timing resolution requires a customized time-to-digital converter (TDC). A custom built, two-channel, field programmable gate array (FPGA)-based TDC capable of continuously time tagging single photons with sub clock cycle timing resolution was characterized. Auto-correlation and cross-correlation measurements were used to constrain spurious systematic effects in the pulse count data as a function of system variables. These variables included, but were not limited to, incident photon count rate, incoming signal attenuation, and measurements of fixed signals. Additionally, a generalized likelihood ratio test using maximum likelihood estimators (MLEs) was derived as a means to detect and estimate correlated photon signal parameters. The derived GLRT was capable of detecting correlated photon signals in a laboratory setting with a high degree of statistical confidence. A proof is presented in which the MLE for the amplitude of the correlated photon signal is shown to be the minimum variance unbiased estimator (MVUE). The fully characterized TDC was used in preliminary measurements of astronomical sources using ground based telescopes. Finally, preliminary theoretical groundwork is established for the deep space optical communications system of the proposed Breakthrough Starshot project, in which low-mass craft will travel to the Alpha Centauri system to collect scientific data from Proxima B. This theoretical groundwork utilizes recent and upcoming space based optical communication systems as starting points for the Starshot communication system.
ContributorsHodges, Todd Michael William (Author) / Mauskopf, Philip (Thesis advisor) / Trichopoulos, George (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Ultrasound B-mode imaging is an increasingly significant medical imaging modality for clinical applications. Compared to other imaging modalities like computed tomography (CT) or magnetic resonance imaging (MRI), ultrasound imaging has the advantage of being safe, inexpensive, and portable. While two dimensional (2-D) ultrasound imaging is very popular, three dimensional (3-D)

Ultrasound B-mode imaging is an increasingly significant medical imaging modality for clinical applications. Compared to other imaging modalities like computed tomography (CT) or magnetic resonance imaging (MRI), ultrasound imaging has the advantage of being safe, inexpensive, and portable. While two dimensional (2-D) ultrasound imaging is very popular, three dimensional (3-D) ultrasound imaging provides distinct advantages over its 2-D counterpart by providing volumetric imaging, which leads to more accurate analysis of tumor and cysts. However, the amount of received data at the front-end of 3-D system is extremely large, making it impractical for power-constrained portable systems.



In this thesis, algorithm and hardware design techniques to support a hand-held 3-D ultrasound imaging system are proposed. Synthetic aperture sequential beamforming (SASB) is chosen since its computations can be split into two stages, where the output generated of Stage 1 is significantly smaller in size compared to the input. This characteristic enables Stage 1 to be done in the front end while Stage 2 can be sent out to be processed elsewhere.



The contributions of this thesis are as follows. First, 2-D SASB is extended to 3-D. Techniques to increase the volume rate of 3-D SASB through a new multi-line firing scheme and use of linear chirp as the excitation waveform, are presented. A new sparse array design that not only reduces the number of active transducers but also avoids the imaging degradation caused by grating lobes, is proposed. A combination of these techniques increases the volume rate of 3-D SASB by 4\texttimes{} without introducing extra computations at the front end.



Next, algorithmic techniques to further reduce the Stage 1 computations in the front end are presented. These include reducing the number of distinct apodization coefficients and operating with narrow-bit-width fixed-point data. A 3-D die stacked architecture is designed for the front end. This highly parallel architecture enables the signals received by 961 active transducers to be digitalized, routed by a network-on-chip, and processed in parallel. The processed data are accumulated through a bus-based structure. This architecture is synthesized using TSMC 28 nm technology node and the estimated power consumption of the front end is less than 2 W.



Finally, the Stage 2 computations are mapped onto a reconfigurable multi-core architecture, TRANSFORMER, which supports different types of on-chip memory banks and run-time reconfigurable connections between general processing elements and memory banks. The matched filtering step and the beamforming step in Stage 2 are mapped onto TRANSFORMER with different memory configurations. Gem5 simulations show that the private cache mode generates shorter execution time and higher computation efficiency compared to other cache modes. The overall execution time for Stage 2 is 14.73 ms. The average power consumption and the average Giga-operations-per-second/Watt in 14 nm technology node are 0.14 W and 103.84, respectively.
ContributorsZhou, Jian (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Wenisch, Thomas F. (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2019
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Description
The propagation of waves in solids, especially when characterized by dispersion, remains a topic of profound interest in the field of signal processing. Dispersion represents a phenomenon where wave speed becomes a function of frequency and results in multiple oscillatory modes. Such signals find application in structural healthmonitoring for identifying

The propagation of waves in solids, especially when characterized by dispersion, remains a topic of profound interest in the field of signal processing. Dispersion represents a phenomenon where wave speed becomes a function of frequency and results in multiple oscillatory modes. Such signals find application in structural healthmonitoring for identifying potential damage sensitive features in complex materials. Consequently, it becomes important to find matched time-frequency representations for characterizing the properties of the multiple frequency-dependent modes of propagation in dispersive material. Various time-frequency representations have been used for dispersive signal analysis. However, some of them suffered from poor timefrequency localization or were designed to match only specific dispersion modes with known characteristics, or could not reconstruct individual dispersive modes. This thesis proposes a new time-frequency representation, the nonlinear synchrosqueezing transform (NSST) that is designed to offer high localization to signals with nonlinear time-frequency group delay signatures. The NSST follows the technique used by reassignment and synchrosqueezing methods to reassign time-frequency points of the short-time Fourier transform and wavelet transform to specific localized regions in the time-frequency plane. As the NSST is designed to match signals with third order polynomial phase functions in the frequency domain, we derive matched group delay estimators for the time-frequency point reassignment. This leads to a highly localized representation for nonlinear time-frequency characteristics that also allow for the reconstruction of individual dispersive modes from multicomponent signals. For the reconstruction process, we propose a novel unsupervised learning approach that does not require prior information on the variation or number of modes in the signal. We also propose a Bayesian group delay mode merging approach for reconstructing modes that overlap in time and frequency. In addition to using simulated signals, we demonstrate the performance of the new NSST, together with mode extraction, using real experimental data of ultrasonic guided waves propagating through a composite plate.
ContributorsIkram, Javaid (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chattopadhyay, Aditi (Thesis advisor) / Bertoni, Mariana (Committee member) / Sinha, Kanu (Committee member) / Arizona State University (Publisher)
Created2023