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- All Subjects: Signal Processing
- Creators: Bliss, Daniel
- Creators: Kosut, Oliver
- Resource Type: Text
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 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.
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
Description
The world of a hearing impaired person is much different than that of somebody capable of discerning different frequencies and magnitudes of sound waves via their ears. This is especially true when hearing impaired people play video games. In most video games, surround sound is fed through some sort of digital output to headphones or speakers. Based on this information, the gamer can discern where a particular stimulus is coming from and whether or not that is a threat to their wellbeing within the virtual world. People with reliable hearing have a distinct advantage over hearing impaired people in the fact that they can gather information not just from what is in front of them, but from every angle relative to the way they're facing. The purpose of this project was to find a way to even the playing field, so that a person hard of hearing could also receive the sensory feedback that any other person would get while playing video games To do this, visual surround sound was created. This is a system that takes a surround sound input, and illuminates LEDs around the periphery of glasses based on the direction, frequency and amplitude of the audio wave. This provides the user with crucial information on the whereabouts of different elements within the game. In this paper, the research and development of Visual Surround Sound is discussed along with its viability in regards to a deaf person's ability to learn the technology, and decipher the visual cues.
ContributorsKadi, Danyal (Co-author) / Burrell, Nathaneal (Co-author) / Butler, Kristi (Co-author) / Wright, Gavin (Co-author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.
ContributorsCrider, Lauren Nicole (Author) / Cochran, Douglas (Thesis director) / Renaut, Rosemary (Committee member) / Kosut, Oliver (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
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 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.
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
Description
Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.
ContributorsHirte, Amanda (Author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
An analysis is presented of a network of distributed receivers encumbered by strong in-band interference. The structure of information present across such receivers and how they might collaborate to recover a signal of interest is studied. Unstructured (random coding) and structured (lattice coding) strategies are studied towards this purpose for a certain adaptable system model. Asymptotic performances of these strategies and algorithms to compute them are developed. A jointly-compressed lattice code with proper configuration performs best of all strategies investigated.
ContributorsChapman, Christian Douglas (Author) / Bliss, Daniel W (Thesis advisor) / Richmond, Christ D (Committee member) / Kosut, Oliver (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Aortic aneurysms and dissections are life threatening conditions addressed by replacing damaged sections of the aorta. Blood circulation must be halted to facilitate repairs. Ischemia places the body, especially the brain, at risk of damage. Deep hypothermia circulatory arrest (DHCA) is employed to protect patients and provide time for surgeons to complete repairs on the basis that reducing body temperature suppresses the metabolic rate. Supplementary surgical techniques can be employed to reinforce the brain's protection and increase the duration circulation can be suspended. Even then, protection is not completely guaranteed though. A medical condition that can arise early in recovery is postoperative delirium, which is correlated with poor long term outcome. This study develops a methodology to intraoperatively monitor neurophysiology through electroencephalography (EEG) and anticipate postoperative delirium. The earliest opportunity to detect occurrences of complications through EEG is immediately following DHCA during warming. The first observable electrophysiological activity after being completely suppressed is a phenomenon known as burst suppression, which is related to the brain's metabolic state and recovery of nominal neurological function. A metric termed burst suppression duty cycle (BSDC) is developed to characterize the changing electrophysiological dynamics. Predictions of postoperative delirium incidences are made by identifying deviations in the way these dynamics evolve. Sixteen cases are examined in this study. Accurate predictions can be made, where on average 89.74% of cases are correctly classified when burst suppression concludes and 78.10% when burst suppression begins. The best case receiver operating characteristic curve has an area under its convex hull of 0.8988, whereas the worst case area under the hull is 0.7889. These results demonstrate the feasibility of monitoring BSDC to anticipate postoperative delirium during burst suppression. They also motivate a further analysis on identifying footprints of causal mechanisms of neural injury within BSDC. Being able to raise warning signs of postoperative delirium early provides an opportunity to intervene and potentially avert neurological complications. Doing so would improve the success rate and quality of life after surgery.
ContributorsMa, Owen (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Kosut, Oliver (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2020
Description
Power spectral analysis is a fundamental aspect of signal processing used in the detection and \\estimation of various signal features. Signals spaced closely in frequency are problematic and lead analysts to miss crucial details surrounding the data. The Capon and Bartlett methods are non-parametric filterbank approaches to power spectrum estimation. The Capon algorithm is known as the "adaptive" approach to power spectrum estimation because its filter impulse responses are adapted to fit the characteristics of the data. The Bartlett method is known as the "conventional" approach to power spectrum estimation (PSE) and has a fixed deterministic filter. Both techniques rely on the Sample Covariance Matrix (SCM). The first objective of this project is to analyze the origins and characteristics of the Capon and Bartlett methods to understand their abilities to resolve signals closely spaced in frequency. Taking into consideration the Capon and Bartlett's reliance on the SCM, there is a novelty in combining these two algorithms using their cross-coherence. The second objective of this project is to analyze the performance of the Capon-Bartlett Cross Spectra. This study will involve Matlab simulations of known test cases and comparisons with approximate theoretical predictions.
ContributorsYoshiyama, Cassidy (Author) / Richmond, Christ (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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