Matching Items (5)
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- Creators: Electrical Engineering Program
- Creators: Kosut, Oliver
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Member of: Theses and Dissertations
- Resource Type: Text
Description
In many systems, it is difficult or impossible to measure the phase of a signal. Direct recovery from magnitude is an ill-posed problem. Nevertheless, with a sufficiently large set of magnitude measurements, it is often possible to reconstruct the original signal using algorithms that implicitly impose regularization conditions on this ill-posed problem. Two such algorithms were examined: alternating projections, utilizing iterative Fourier transforms with manipulations performed in each domain on every iteration, and phase lifting, converting the problem to that of trace minimization, allowing for the use of convex optimization algorithms to perform the signal recovery. These recovery algorithms were compared on a basis of robustness as a function of signal-to-noise ratio. A second problem examined was that of unimodular polyphase radar waveform design. Under a finite signal energy constraint, the maximal energy return of a scene operator is obtained by transmitting the eigenvector of the scene Gramian associated with the largest eigenvalue. It is shown that if instead the problem is considered under a power constraint, a unimodular signal can be constructed starting from such an eigenvector that will have a greater return.
ContributorsJones, Scott Robert (Author) / Cochran, Douglas (Thesis director) / Diaz, Rodolfo (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
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
This document introduces the need for the Rest Egg system and defines an accessible method of smartphone integration. Excessive noise can prevent recovering patients and special needs persons from resting correctly. The Rest Egg was designed for these people- people who are in critical need of quality rest but are often unable to eliminate stressors themselves. This system ensures their environment is calm by alerting caretakers' smartphones if noise reaches abrasive levels. Smartphones were the preferred device due to the wide spread of such devices in today's market. After making open sourcing a goal, something ubiquitous and affordable \u2014 yet usable and dependable \u2014 was necessary for the alert system. These requirements lead to the election an online alert service: Pushover, a trademark and product of Superblock, LLC.
ContributorsJennings, Tyler Blake (Author) / Goryll, Michael (Thesis director) / Kozicki, Michael (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2016-05
Description
This paper analyzes existing literature regarding how excessive aggravating stimuli in a hospital environment can reduce the quality and quantity of sleep. The sick and injured are most sensitive to aggravating stimuli and the most vulnerable to poor sleep conditions. For individuals with anxiety, stress, hypersensitivity, or conditions such as Autism Spectrum Disorder (ASD), as additional stress during rest periods could seriously harm development and overall well-being. While solutions have been proposed and tested, there is no one solution to the problem. One possible solution is to design a device that monitors a patient's room and alerts a nurse or parent of aggravating stimuli so that it can be removed.
ContributorsKhan, Zarah Noor (Author) / Goryll, Michael (Thesis director) / Adams, James B. (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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