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- Creators: Barrett, The Honors College
- Creators: Bliss, Daniel
- Creators: Kosut, Oliver
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
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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
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
This report describes the findings of an experiment designed to explore the nature of human hearing using binaural sound. The experiment also set out to determine a way to accurately find positional data from sound. Binaural recordings were made of high frequency sounds at various angles and the data was postprocessed to find the group delay and difference of intensity between the two channels. To do this, two methods were used. The first relied on manually analyzing the data by visually looking for the points of interest. The second method used a MATLAB program to scan the data for the points of interest by using a Fourier analysis. It was determined that while the first method has the potential to provide better results it is impractical and not representative of how human hearing works. The second method was far more efficient and demonstrated the reliance of human hearing on the difference of intensities. It was determined that through the use of the second method accurate positional data could be obtained by comparing the differences with experimental data.
ContributorsCruz, Benjamin (Author) / Takahashi, Timothy (Thesis director) / Aukes, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2023-05
Description
The objective of this project was to research and experimentally test methods of localization, waypoint following, and actuation for high-speed driving by an autonomous vehicle. This thesis describes the implementation of LiDAR localization techniques, Model Predictive Control waypoint following, and communication for actuation on a 2016 Chevrolet Camaro, Arizona State University’s former EcoCAR. The LiDAR localization techniques include the NDT Mapping and Matching algorithms from the open-source autonomous vehicle platform, Autoware. The mapping algorithm was supplemented by that of Google Cartographer due to the limitations of map size in Autoware’s algorithms. The Model Predictive Control for waypoint following and the computer-microcontroller-actuator communication line are described. In addition to this experimental work, the thesis discusses an investigation of alternative approaches for each problem.
ContributorsCopenhaver, Bryce Stone (Author) / Berman, Spring (Thesis director) / Yong, Sze Zheng (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Who would imagine that playing games is considered as a sport and became one of the most popular sports in the world? Who would imagine that over 100 million people watch someone else playing a game 10 years ago? Maybe even 5 years ago a lot of people did not believe that many people were watching one Esports championship series in the world in 2019. I believe that most people would not believe that fact. Nowadays the gaming industry has become 134 billion dollars industry (Warman), but most of the general public does not even know that Esports is a globally popular sport. The uniqueness of Esports is that fans are located everywhere in the world, unlike American football. This sport’s popularity is borderless and there are not that many sports leagues that have a huge global fan population in the sports industry. The reason Esports was able to capture popularity from everywhere in this world is because the gaming community is often beyond the border. For example, a person who lives in South Korea is teaming up with a man whom he has never met in person before and fighting against the players who are living on the other side of the world in a single match. This is how modern gaming society is. Those players are physically existing in different places, but there is no border that exists in this gaming virtual world and people are playing in the same match with the players who live in different places. This is one thing that we are not able to see in the traditional sports and the biggest strength of the Esports. The uniqueness of Esports is that all the players do not need to physically get together to play a game. If you want to play soccer, obviously all the 22 players need to be in the same field physically. People do not have a sense of local attachment from the beginning in the world of modern Esports because the gaming community is existing in the virtual world and the border does not exist in this virtual community. This unique environment is one of the biggest factors that makes Esports the fastest growing sport in the entire sports industry these days, and this rapid growth is supported by those younger gamers. Esports is still a new sport compared to other traditional sports, so they will follow a similar or different path that traditional sports took and will be part of those popular leagues in the future.
ContributorsSannomiya, Akie (Author) / Eaton, John (Thesis director) / McIntosh, Daniel (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05