Matching Items (31)
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- All Subjects: Machine Learning
- All Subjects: Technology
- Creators: Electrical Engineering Program
- Creators: Berisha, Visar
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
The Lightning Audio capstone group, consisting of Brian Boerhinger, Rahul Nandan, Jaime Ramirez, and Niccolo Magnotto (myself), united in the effort to prove the feasibility of a consumer grade plasma arc speaker. This was achieved in group's prototype design, which demonstrates the potential for a refined product in its conventional interfacing, casing, size, safety, and aesthetics. If the potential for an excellent ionization-based loudspeaker product were realized, it would be highly profitable in its reasonable cost of production, novelty, and place in a large and fitting market.
ContributorsMagnotto, Niccolo John (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
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
Description
The Founders Lab Thesis tasked each team with taking an idea and trying to form a business out of it. In the process, the thesis director would be there to guide each team and provide expertise where needed. The venture that was assigned originally to our team was a posture correcting device, however after numerous attempts to correspond reliably with the developers of this technology, it was decided that the team should move on to a new idea. Therefore, our team took on a venture named Altion Security: an initiative with the main goal being the safekeeping of customers interests. The product that we were tasked with is a bike alarm that simply rings out when it detects someone tampering with it. This product is a solution to the problem of bike thefts. 2 million bikes are stolen each year in North America, which translates roughly to a theft every 30 seconds (Project 529).
There are quite a few readily available products that one can buy if one looks past some of their flaws. A lot of these alarms either require a user to carry an extra communication device, or they are too big or expensive. The proposed solution merges all desirable features of a bike alarm into one module. In light of this, surveys were conducted to ascertain what these qualities would need to be. The top considerations for purchasing this alarm were how costly it would be, the false detection rate, and also the battery life. Additionally, the features that were most requested was the inclusion of a GPS and a camera. In order to incorporate these features, a three year plan was formulated which would culminate into a bike network in which each bike could communicate with other bikes. This would allow for an IOT network to be established, thus far exceeding expectations. The price point for this alarm is USD $10.00-15.00 and can come in a variety of colors. Additionally, this concept can be applied to many different scenarios, from protecting boats/jet skis and other aquatic vehicles, to houses as well. Furthermore, one could miniaturize this technology to be used in jewelry or accessories.
There are quite a few readily available products that one can buy if one looks past some of their flaws. A lot of these alarms either require a user to carry an extra communication device, or they are too big or expensive. The proposed solution merges all desirable features of a bike alarm into one module. In light of this, surveys were conducted to ascertain what these qualities would need to be. The top considerations for purchasing this alarm were how costly it would be, the false detection rate, and also the battery life. Additionally, the features that were most requested was the inclusion of a GPS and a camera. In order to incorporate these features, a three year plan was formulated which would culminate into a bike network in which each bike could communicate with other bikes. This would allow for an IOT network to be established, thus far exceeding expectations. The price point for this alarm is USD $10.00-15.00 and can come in a variety of colors. Additionally, this concept can be applied to many different scenarios, from protecting boats/jet skis and other aquatic vehicles, to houses as well. Furthermore, one could miniaturize this technology to be used in jewelry or accessories.
ContributorsOgunmefun, Adeoluwa (Co-author) / Gong, Alan (Co-author) / Parra, Rocio Ivette (Co-author) / Byrne, Jared (Thesis director) / Sebold, Brent (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Machine learning is a powerful tool for processing and understanding the vast amounts of data produced by sensors every day. Machine learning has found use in a wide variety of fields, from making medical predictions through correlations invisible to the human eye to classifying images in computer vision applications. A wide range of machine learning algorithms have been developed to attempt to solve these problems, each with different metrics in accuracy, throughput, and energy efficiency. However, even after they are trained, these algorithms require substantial computations to make a prediction. General-purpose CPUs are not well-optimized to this task, so other hardware solutions have developed over time, including the use of a GPU, FPGA, or ASIC.
This project considers the FPGA implementations of MLP and CNN feedforward. While FPGAs provide significant performance improvements, they come at a substantial financial cost. We explore the options of implementing these algorithms on a smaller budget. We successfully implement a multilayer perceptron that identifies handwritten digits from the MNIST dataset on a student-level DE10-Lite FPGA with a test accuracy of 91.99%. We also apply our trained network to external image data loaded through a webcam and a Raspberry Pi, but we observe lower test accuracy in these images. Later, we consider the requirements necessary to implement a more elaborate convolutional neural network on the same FPGA. The study deems the CNN implementation feasible in the criteria of memory requirements and basic architecture. We suggest the CNN implementation on the same FPGA to be worthy of further exploration.
This project considers the FPGA implementations of MLP and CNN feedforward. While FPGAs provide significant performance improvements, they come at a substantial financial cost. We explore the options of implementing these algorithms on a smaller budget. We successfully implement a multilayer perceptron that identifies handwritten digits from the MNIST dataset on a student-level DE10-Lite FPGA with a test accuracy of 91.99%. We also apply our trained network to external image data loaded through a webcam and a Raspberry Pi, but we observe lower test accuracy in these images. Later, we consider the requirements necessary to implement a more elaborate convolutional neural network on the same FPGA. The study deems the CNN implementation feasible in the criteria of memory requirements and basic architecture. We suggest the CNN implementation on the same FPGA to be worthy of further exploration.
ContributorsLythgoe, Zachary James (Author) / Allee, David (Thesis director) / Hartin, Olin (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
In the world we live in today, nothing is impossible. Due to the advancements of technology, humans around the globe are able to hold computers that fit within the size of their pocket. These computers can do marvelous things, however run off batteries. These batteries need to be charged and up until a little while ago there was only one option available: wired chargers; however, because of the advancement of technology society has created a way to transfer power via magnetic fields. Now this concept has been around for a long time since the days of Nikola Tesla but just recently society has been able to apply his discoveries to charging these computers in our pockets. Unfortunately, the current models of these chargers come with a drawback as they are less efficient than wired chargers. However, this is the question our group has set out to answer. Is there any way possible to improve the efficiency of these wireless chargers so they are equal or even more efficient than wired chargers. This paper explores how to improve the efficiency in wireless chargers. Through research, simulations and testing the group has discovered areas that efficiency can be improved as well as makes recommendations to change the current wireless chargers on the market today. This paper also explores future applications of wireless chargers that can not only make life much easier but could also save lives in some cases. These applications can have many effects on hospitality, the medical field, as well as the supply chain and logistics of America.
ContributorsMcCulley, Matthew Alan (Co-author) / Cole, Kennedy (Co-author) / Chickamenahalli, Shamala (Thesis director) / Chakrabarti, Chaitali (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
This paper introduces a wireless reconfigurable “button-type” pressure sensor system, via machine learning, for gait analysis application. The pressure sensor system consists of an array of independent button-type pressure sensing units interfaced with a remote computer. The pressure sensing unit contains pressure-sensitive resistors, readout electronics, and a wireless Bluetooth module, which are assembled within footprint of 40 × 25 × 6mm3. The small-footprint, low-profile sensors are populated onto a shoe insole, like buttons, to collect temporal pressure data. The pressure sensing unit measures pressures up to 2,000 kPa while maintaining an error under 10%. The reconfigurable pressure sensor array reduces the total power consumption of the system by 50%, allowing extended period of operation, up to 82.5 hrs. A robust machine learning program identifies the optimal pressure sensing units in any given configuration at an accuracy of up to 98%.
ContributorsBooth, Jayden Charles (Author) / Chae, Junseok (Thesis director) / Chen, Ang (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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