Matching Items (362)
Filtering by
- All Subjects: Music
- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
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
The saxophone is privileged to have a wide variety of repertoire from contemporary composers. Due to its invention in the later half of the nineteenth century, it has no repertoire written by baroque composers, including Johann Sebastian Bach. There are several published arrangements of Bach’s three solo violin partitas including that of Ronald Caravan and Raaf Hekkema. These collections either do not present every movement of each of these three partitas, or they do not present them in their original keys. An advantage to arranging these works in their original keys is that saxophonists have the opportunity to learn more about the works by playing along with recordings of great violinists such as Itzhak Perlman and Hilary Hahn, something that would be very difficult to do if they were not in the original keys. In Ronald Caravan’s Bach for Solo Saxophone, Caravan includes a collection of many unaccompanied works by Bach for saxophone but does not include all of the movements from the three partitas and they are not in the original keys that Bach wrote for. In Raaf Hekkema’s Bach for Saxophone, Hekkema arranges the entirety of the three partitas, however they are not set in the original keys that Bach wrote for. In addition to these points, those collections do not provide information of the life of J.S. Bach, baroque performance practice, mechanics of the baroque violin, baroque dances, and advice on going about the mechanics of these pieces from a saxophonist’s perspective. This information is very useful to a young saxophonist who is trying to fully understand and perform Bach’s three solo violin partitas.
ContributorsSalazar, Nathan John (Author) / Creviston, Christopher (Thesis director) / Saucier, Catherine (Committee member) / School of Music (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
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis evaluates the viability of an original design for a cost-effective wheel-mounted dynamometer for road vehicles. The goal is to show whether or not a device that generates torque and horsepower curves by processing accelerometer data collected at the edge of a wheel can yield results that are comparable to results obtained using a conventional chassis dynamometer. Torque curves were generated via the experimental method under a variety of circumstances and also obtained professionally by a precision engine testing company. Metrics were created to measure the precision of the experimental device's ability to consistently generate torque curves and also to compare the similarity of these curves to the professionally obtained torque curves. The results revealed that although the test device does not quite provide the same level of precision as the professional chassis dynamometer, it does create torque curves that closely resemble the chassis dynamometer torque curves and exhibit a consistency between trials comparable to the professional results, even on rough road surfaces. The results suggest that the test device provides enough accuracy and precision to satisfy the needs of most consumers interested in measuring their vehicle's engine performance but probably lacks the level of accuracy and precision needed to appeal to professionals.
ContributorsKing, Michael (Author) / Ren, Yi (Thesis director) / Spanias, Andreas (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This research project will test the structural properties of a 3D printed origami inspired structure and compare them with a standard honeycomb structure. The models have equal face areas, model heights, and overall volume but wall thicknesses will be different. Stress-deformation curves were developed from static loading testing. The area under these curves was used to calculate the toughness of the structures. These curves were analyzed to see which structures take more load and which deform more before fracture. Furthermore, graphs of the Stress-Strain plots were produced. Using 3-D printed parts in tough resin printed with a Stereolithography (SLA) printer, the origami inspired structure withstood a larger load, produced a larger toughness and deformed more before failure than the equivalent honeycomb structure.
ContributorsMcGregor, Alexander (Author) / Jiang, Hanqing (Thesis director) / Kingsbury, Dallas (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This creative project explores the concept of how music is like a language and how, as a teacher, I plan to enforce this concept through my teaching. The aim of this project is to highlight the importance of completing research and acquiring knowledge of aspects, such as the composer's life, historical background and literary references, when learning a piece of music. Through this project, I address connections between the brain and music pertaining to memorization, the components of language, the similarities between language and music, the role of the teacher and the development of a "toolbox" of knowledge for studying a piece of music. I present my own research on Schumann's Novelette Op. 21, No. 8 in f-sharp minor as well as my own experiences of learning the piece to demonstrate an example of the process and discoveries I hope my students will make in their own studies of repertoire.
ContributorsLee, Kara Jia-Shan (Author) / Pagano, Caio (Thesis director) / Creviston, Hannah (Committee member) / Division of Teacher Preparation (Contributor) / School of Music (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The SolarSPELL is an offline, ruggedized, digital library, created by Dr. Laura Hosman for the U.S. Peace Corps. It has thousands of pieces of educational content that can be accessed through a self-contained Wi-Fi hotspot on the device itself. Currently, there are more than 200 deployed in several Pacific Island nations. After visiting one of these nations, Tonga, in December of 2016, I learned that almost all of the Peace Corps volunteers stationed around the Pacific Islands suffered from a lack of resources due to a variety of reasons. While the SolarSPELL helps to remedy that, the device is lacking classroom activities and resources for creative work and educational drama. Furthermore, for many students in these environments, schools are for learning information and producing high scores on exams, not for learning about creative strengths and identity. After researching curriculum development and the use of drama in an educational setting, I compiled over 50 pieces of content to include on the SolarSPELL involving art, drama, music, movement, and most importantly, imagination. These resources will allow Peace Corps volunteers to explore additional ways to teach English in their schools, while also creating a classroom environment that allows for creative expression. All the content is compiled into one folder as "Teaching Resources", and is then broken down into seven sub- categories. In the first sub-category, Art Projects, there is a collection of several hands-on projects, many of which involve recyclable or readily available materials. These projects will allow for a greater understanding of conservation and "green" living, concepts that are crucial to the stability of these island nations. The next 5 categories are Drama Readings, Music, Movement, and Video, Group Exercises, Creative Writing, and Worksheets. The second sub- category is a collection of beginner-level "Reader's Theater" scripts. The third sub-category involves music and video to engage students in movement activities. The fourth sub-category is a compilation of group games and activities to help students express themselves and learn social skills. The fifth sub-category includes a collection of activities such as fill-in-the-blank story worksheets and journal prompts which will aid in creative thinking and the practice of the English language. The sixth sub-category involves a collection of worksheets that mainly focus on self-reflection and identity. The seventh and final sub-category, Content Guide and Information, works to explain the benefits of using of drama and creative play in the classroom, as well as strategies teachers can implement in order to further engage their students in dramatic learning and play. Overall, these pieces of content are meant to be used as resources for the Peace Corps volunteers in order to provide alternative ways to practice reading, writing, and speaking the English language, a critical part of education in the Pacific Islands.
ContributorsTaylor, Amanda Nicole (Author) / Hosman, Laura (Thesis director) / McAvoy, Mary (Committee member) / School of Film, Dance and Theatre (Contributor) / Division of Teacher Preparation (Contributor) / Barrett, The Honors College (Contributor) / School for the Future of Innovation in Society (Contributor)
Created2017-12
Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05