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- Genre: Academic theses
- Creators: Papandreou-Suppappola, Antonia
Description
This research presents advances in time-synchronized phasor (i.e.,synchrophasor) estimation and imaging with very-low-frequency electric fields.
Phasor measurement units measure and track dynamic systems, often power
systems, using synchrophasor estimation algorithms. Two improvements to
subspace-based synchrophasor estimation algorithms are shown. The first
improvement is a dynamic thresholding method for accurately determining the
signal subspace when using the estimation of signal parameters via rotational
invariance techniques (ESPRIT) algorithm. This improvement facilitates
accurate ESPRIT-based frequency estimates of both the nominal system frequency
and the frequencies of interfering signals such as harmonics or out-of-band
interference signals. Proper frequency estimation of all signals present in
measurement data allows for accurate least squares estimates of synchrophasors
for the nominal system frequency. By including the effects of clutter signals
in the synchrophasor estimate, interference from clutter signals can be
excluded. The result is near-flat estimation error during nominal system
frequency changes, the presence of harmonic distortion, and out-of-band
interference. The second improvement reduces the computational burden of the
ESPRIT frequency estimation step by showing that an optimized Eigenvalue
decomposition of the measurement data can be used instead of a singular value
decomposition. This research also explores a deep-learning-based inversion
method for imaging objects with a uniform electric field and a 2D planar D-dot
array. Using electric fields as an illumination source has seen multiple
applications ranging from medical imaging to mineral deposit detection. It is
shown that a planar D-dot array and deep neural network can reconstruct the
electrical properties of randomized objects. A 16000-sample dataset of objects
comprised of a three-by-three grid of randomized dielectric constants was
generated to train a deep neural network for predicting these dielectric
constants from measured field distortions. Increasingly complex imaging
environments are simulated, ranging from objects in free space to objects
placed in a physical cage designed to produce uniform electric fields.
Finally, this research relaxes the uniform electric field constraint, showing
that the volume of an opaque container can be imaged with a copper tube antenna
and a 1x4 array of D-dot sensors. Real world experimental results
show that it is possible to image buckets of water (targets) within a plastic
shed These experiments explore the detectability of targets as a function of
target placement within the shed.
ContributorsDrummond, Zachary (Author) / Allee, David R (Thesis advisor) / Claytor, Kevin E (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Aberle, James (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is crowding the unlicensed spectrum. Licensed network such as LTE also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work we present a reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE-LAA agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. We turned the reinforcement learning into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.
ContributorsSHIH, PO-KAN (Author) / Moraffah, Bahman (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Dasarathy, Gautam (Committee member) / Shih, YiChang (Committee member) / Arizona State University (Publisher)
Created2021
Description
The work presented in this manuscript has the overarching theme of radiation. The two forms of radiation of interest are neutrons, i.e. nuclear, and electric fields. The ability to detect such forms of radiation have significant security implications that could also be extended to very practical industrial applications. The goal is therefore to detect, and even image, such radiation sources.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
ContributorsChung, Hugh E (Author) / Allee, David R. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2015
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 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
Description
Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for 30% of all numbers in the dataset and digits that start with the number 9 accounting for 5% of all numbers in the dataset. This phenomenon, known as Benford's Law, is highly repeatable and appears in lists of numbers from electricity bills, stock prices, tax returns, house prices, death rates, lengths of rivers, and naturally occurring images. This paper will demonstrate that human speech spectra also follow Benford's Law. This observation is used to motivate a new set of features that can be efficiently extracted from speech and demonstrate that these features can be used to classify between human speech and synthetic speech.
ContributorsHsu, Leo (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2022
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 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
Description
This dissertation centers on the development of Bayesian methods for learning differ- ent types of variation in switching nonlinear gene regulatory networks (GRNs). A new nonlinear and dynamic multivariate GRN model is introduced to account for different sources of variability in GRNs. The new model is aimed at more precisely capturing the complexity of GRN interactions through the introduction of time-varying kinetic order parameters, while allowing for variability in multiple model parameters. This model is used as the drift function in the development of several stochastic GRN mod- els based on Langevin dynamics. Six models are introduced which capture intrinsic and extrinsic noise in GRNs, thereby providing a full characterization of a stochastic regulatory system. A Bayesian hierarchical approach is developed for learning the Langevin model which best describes the noise dynamics at each time step. The trajectory of the state, which are the gene expression values, as well as the indicator corresponding to the correct noise model are estimated via sequential Monte Carlo (SMC) with a high degree of accuracy. To address the problem of time-varying regulatory interactions, a Bayesian hierarchical model is introduced for learning variation in switching GRN architectures with unknown measurement noise covariance. The trajectory of the state and the indicator corresponding to the network configuration at each time point are estimated using SMC. This work is extended to a fully Bayesian hierarchical model to account for uncertainty in the process noise covariance associated with each network architecture. An SMC algorithm with local Gibbs sampling is developed to estimate the trajectory of the state and the indicator correspond- ing to the network configuration at each time point with a high degree of accuracy. The results demonstrate the efficacy of Bayesian methods for learning information in switching nonlinear GRNs.
ContributorsVélez-Cruz, Nayely (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2023