Filtering by
- All Subjects: Machine Learning
- Creators: Electrical Engineering Program
- Creators: Baral, Chitta
- Member of: Theses and Dissertations
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.
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.
In wireless communication systems, the process of data transmission includes the estimation of channels. Implementing machine learning in this process can reduce the amount of time it takes to estimate channels, thus, resulting in an increase of the system’s transmission throughput. This maximizes the performance of applications relating to device-to-device communications and 5G systems. However, applying machine learning algorithms to multi-base-station systems is not well understood in literature, which is the focus of this thesis.
The stability of cheerleading stunts is crucial to athlete safety and team success. Consistency in stunt technique contributes to success in stunting skills, giving a team the tools to win competitions. Increased stunt technique reduces the chances of falls and the severity of those falls. Proper technique also prevents injuries caused by improper positions that place pressure on the lower back and shoulders. Bases must maintain strong technique with proper lines of support in order to maximize stunt stability. Through exploration of the EmbeddedML system, involving a neural network implemented using a SensorTile, cheerleading motions can be successfully classified. Using this system, it is possible to identify motions that result in both weak and injurious positions almost instantly. By alerting athletes to these incorrect motions, improper stunt technique can be corrected quickly and without the involvement of a coach. This automated technique correction would be incredibly beneficial to the sport of competitive cheerleading
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
