Matching Items (6)
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- All Subjects: Neuroscience
- Creators: Bliss, Daniel
- Creators: Daliri, Ayoub
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (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
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
Previous research has showed that auditory modulation may be affected by pure tone
stimuli played prior to the onset of speech production. In this experiment, we are examining the
specificity of the auditory stimulus by implementing congruent and incongruent speech sounds in
addition to non-speech sound. Electroencephalography (EEG) data was recorded for eleven adult
subjects in both speaking (speech planning) and silent reading (no speech planning) conditions.
Data analysis was accomplished manually as well as via generation of a MATLAB code to
combine data sets and calculate auditory modulation (suppression). Results of the P200
modulation showed that modulation was larger for incongruent stimuli than congruent stimuli.
However, this was not the case for the N100 modulation. The data for pure tone could not be
analyzed because the intensity of this stimulus was substantially lower than that of the speech
stimuli. Overall, the results indicated that the P200 component plays a significant role in
processing stimuli and determining the relevance of stimuli; this result is consistent with role of
P200 component in high-level analysis of speech and perceptual processing. This experiment is
ongoing, and we hope to obtain data from more subjects to support the current findings.
stimuli played prior to the onset of speech production. In this experiment, we are examining the
specificity of the auditory stimulus by implementing congruent and incongruent speech sounds in
addition to non-speech sound. Electroencephalography (EEG) data was recorded for eleven adult
subjects in both speaking (speech planning) and silent reading (no speech planning) conditions.
Data analysis was accomplished manually as well as via generation of a MATLAB code to
combine data sets and calculate auditory modulation (suppression). Results of the P200
modulation showed that modulation was larger for incongruent stimuli than congruent stimuli.
However, this was not the case for the N100 modulation. The data for pure tone could not be
analyzed because the intensity of this stimulus was substantially lower than that of the speech
stimuli. Overall, the results indicated that the P200 component plays a significant role in
processing stimuli and determining the relevance of stimuli; this result is consistent with role of
P200 component in high-level analysis of speech and perceptual processing. This experiment is
ongoing, and we hope to obtain data from more subjects to support the current findings.
ContributorsTaylor, Megan Kathleen (Author) / Daliri, Ayoub (Thesis director) / Liss, Julie (Committee member) / School of Life Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Transcranial Current Stimulation (TCS) is a long-established method of modulating neuronal activity in the brain. One type of this stimulation, transcranial alternating current stimulation (tACS), is able to entrain endogenous oscillations and result in behavioral change. In the present study, we used five stimulation conditions: tACS at three different frequencies (6Hz, 12Hz, and 22Hz), transcranial random noise stimulation (tRNS), and a no-stimulation sham condition. In all stimulation conditions, we recorded electroencephalographic data to investigate the link between different frequencies of tACS and their effects on brain oscillations. We recruited 12 healthy participants. Each participant completed 30 trials of the stimulation conditions. In a given trial, we recorded brain activity for 10 seconds, stimulated for 12 seconds, and recorded an additional 10 seconds of brain activity. The difference between the average oscillation power before and after a stimulation condition indicated change in oscillation amplitude due to the stimulation. Our results showed the stimulation conditions entrained brain activity of a sub-group of participants.
ContributorsChernicky, Jacob Garrett (Author) / Daliri, Ayoub (Thesis director) / Liss, Julie (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05