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- All Subjects: Machine Learning
- Creators: Electrical Engineering Program
- Status: Published
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
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.
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.
The increasing demand for clean energy solutions requires more than just expansion, but also improvements in the efficiency of renewable sources, such as solar. This requires analytics for each panel regarding voltage, current, temperature, and irradiance. This project involves the development of machine learning algorithms along with a data logger for the purpose of photovoltaic (PV) monitoring and control. Machine learning is used for fault classification. Once a fault is detected, the system can change its reconfiguration to minimize the power losses. Accuracy in the fault detection was demonstrated to be at a level over 90% and topology reconfiguration showed to increase power output by as much as 5%.
This thesis will bring together students to engage in entrepreneurship by finding, measuring and sharing strategic market opportunities. From a student’s perspective, it will take a deep dive into the world of startup ecosystems, markets and trends utilizing both qualitative and quantitative market research techniques. The information gathered has been curated into a productive, meaningful manner, through a report titled “The State of Startups: A Student Perspective.” <br/>The first key theme of this thesis is that market intelligence can be a powerful tool. The second key theme is the power of knowledge implementation towards competitive strategies. The first section of the thesis will focus on identifying and understanding the current “startup” landscape as a basis on which to build strategic and impactful business decisions. This will be accomplished as the team conducts a landscape analysis focused on the student perspective of the student-based North American “entrepreneurial” ecosystem. The second section of the thesis will focus specifically on the personal experiences of student startup founders. This will be accomplished through the analysis of interviews with founders of the startups researched from the first section of the thesis. This will provide us with a direct insight into the student perspective of the student-based North American “entrepreneurial” ecosystem.