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Full-Text Articles in Engineering
Real-Time Object Detection And Tracking On Drones, Tu Le
Real-Time Object Detection And Tracking On Drones, Tu Le
Undergraduate Research & Mentoring Program
Unmanned aerial vehicles, also known as drones, have been more and more widely used in recent decades because of their mobility. They appear in many applications such as farming, search and rescue, entertainment, military, and so on. Such high demands for drones lead to the need of developments in drone technologies. Next generations of commercial and military drones are expected to be aware of surrounding objects while flying autonomously in different terrains and conditions. One of the biggest challenges to drone automation is the ability to detect and track objects of interest in real-time. While there are many robust machine …
From A Locally Competitive Algorithm To Sensory Relevance Models, Walter Woods
From A Locally Competitive Algorithm To Sensory Relevance Models, Walter Woods
Electrical and Computer Engineering PhD Day
This poster addresses the development of a new Machine Learning (ML) mechanism, the Sensory Relevance Model (SRM), as a means of splitting information processing tasks into two sub-tasks with more intuitive properties. Specifically, SRMs are a front-end for other ML techniques, re-mapping the input data to a similar space with significantly more sparsity, achieved through the transformation and suppression of inputs irrelevant to the task. Prior work has attempted to reveal this information for Neural Networks (NNs) either as a post-processing step via saliency maps or through a simple masking of the input achieved with a dot product (so-called ``attention'' …
Hierarchical Random Boolean Network Reservoirs, Sai Kiran Cherupally
Hierarchical Random Boolean Network Reservoirs, Sai Kiran Cherupally
Dissertations and Theses
Reservoir Computing (RC) is an emerging Machine Learning (ML) paradigm. RC systems contain randomly assembled computing devices and can be trained to solve complex temporal tasks. These systems are computationally cheaper to train than other ML paradigms such as recurrent neural networks, and they can also be trained to solve multiple tasks simultaneously. Further, hierarchical RC systems with fixed topologies, were shown to outperform monolithic RC systems by up to 40% when solving temporal tasks. Although the performance of monolithic RC networks was shown to improve with increasing network size, building large monolithic networks may be challenging, for example because …
Early Emerging Pathogen Detection, Mackenzie Wangenstein
Early Emerging Pathogen Detection, Mackenzie Wangenstein
Undergraduate Research & Mentoring Program
A supervised learning technique was employed to identify emerging pathogen species. Portland State University has partnered with the University of New Mexico to take encodings of unknown pathogen molecular structures to determine emerging species.
Water Quality Factor Prediction Using Supervised Machine Learning, Kathleen Joslyn
Water Quality Factor Prediction Using Supervised Machine Learning, Kathleen Joslyn
REU Final Reports
The objective of this research is to explore prediction accuracy of water quality factors, with techniques and algorithms in machine learning consisting of a variation of support vector machines - Support Vector Regression (SVR) and the gradient boosting algorithm Extreme Gradient Boosting (XGBoost). Both the XGBoost and SVR algorithms were used to predict nine different factors with success rates ranging from 79% to 99%. Parameters of these algorithms were also explored to test the prediction accuracy levels of individual water quality factors. These parameters included normalizing the data, filling missing data points, and training and testing on a large set …