Matching Items (22)
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- All Subjects: Machine Learning
- All Subjects: Sensors
- Creators: Electrical Engineering Program
- 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
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and 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
This project details a magnetic field detection system that can be mounted on an unmanned aerial vehicle (UAV). The system is comprised of analog circuitry to detect and process the magnetic signals, digital circuitry to sample and store the data outputted from the analog front end, and finally a UAV to carry and mobilize the electronic parts. The system should be able to sense magnetic fields from power transmission lines, enabling the determination of whether or not current is running through the power line.
ContributorsTheoharatos, Dimitrios (Co-author) / Brazones, Ryan (Co-author) / Pagaduan, Patrick (Co-author) / Allee, David (Thesis director) / Karady, George (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
Active pixel sensors hold a lot of promise for space applications in star tracking because of their effectiveness against radiation, small size, and on-chip processing. The research focus is on documenting and validating ground test equipment for these types of sensors. Through demonstrating the utility of a commercial sensor, the research will be able to work on ensuring the accuracy of ground tests. This contribution allows for future research on improving active pixel sensor performance.
ContributorsDotson, Breydan Lane (Author) / White, Daniel (Thesis director) / Jansen, Rolf (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper summarizes the [1] ideas behind, [2] needs, [3] development, and [4] testing of 3D-printed sensor-stents known as Stentzors. This sensor was successfully developed entirely from scratch, tested, and was found to have an output of 3.2*10-6 volts per RMS pressure in pascals. This paper also recommends further work to render the Stentzor deployable in live subjects, including [1] further design optimization, [2] electrical isolation, [3] wireless data transmission, and [4] testing for aneurysm prevention.
ContributorsMeidinger, Aaron Michael (Author) / LaBelle, Jeffrey (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
Description
This work details the bootstrap estimation of a nonparametric information divergence measure, the Dp divergence measure, using a power law model. To address the challenge posed by computing accurate divergence estimates given finite size data, the bootstrap approach is used in conjunction with a power law curve to calculate an asymptotic value of the divergence estimator. Monte Carlo estimates of Dp are found for increasing values of sample size, and a power law fit is used to relate the divergence estimates as a function of sample size. The fit is also used to generate a confidence interval for the estimate to characterize the quality of the estimate. We compare the performance of this method with the other estimation methods. The calculated divergence is applied to the binary classification problem. Using the inherent relation between divergence measures and classification error rate, an analysis of the Bayes error rate of several data sets is conducted using the asymptotic divergence estimate.
ContributorsKadambi, Pradyumna Sanjay (Author) / Berisha, Visar (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the divergence between two data sets in the absence of fully labeled data. This semi-labeled case is common in many domains where labeling data by hand is expensive or time-consuming, or wherever large data sets are present. The theory derived in this paper is demonstrated on a simulated example, and then applied to a feature selection and classification problem from pathological speech analysis.
ContributorsGilton, Davis Leland (Author) / Berisha, Visar (Thesis director) / Cochran, Douglas (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The team has designed and built a golf swing analyzer that informs the user of his mistakes while putting with a golf club. The team also interfaced a Linux program with the analyzer that allows the user to review the flaws in his golf swing. In addition, the application is more personalized than existing devices and tailored to the individual based on his level of experience. The analyzer consists of an accelerometer, gyroscope, magnetometer, vibration motor, and microcontroller that are connected on a board that attaches to the top of the shaft of a golf club, fitting inside a 3D printed case. The team has assembled all of the necessary hardware, and is able to successfully display critical parameters of a golf putt, as well as send instant feedback to the user. The final budget for this project was $378.24
ContributorsKaur, Hansneet (Co-author) / Cox, Jeremy (Co-author) / Farnsworth, Chad (Co-author) / Zorob, Nabil (Co-author) / Chae, Junseok (Thesis director) / Aberle, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
ContributorsRangaswami, Sriram Madhav (Author) / Lalitha, Sankar (Thesis director) / Jayasuriya, Suren (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05