SoutheastCon 

Abstract: In the past decade Unmanned Aerial Vehicles (UAVs) have become a topic of interest in many research organizations. UAVs are finding applications in various areas ranging from military applications to traffic surveillance. This paper is a survey for a certain kind of UAV called quadrotor or quadcopter. Researchers are frequently choosing quadrotors for their research because a quadrotor can accurately and efficiently perform tasks that would be of high risk for a human pilot to perform. This paper encompasses the dynamic models of a quadrotor and the different model-dependent and model-independent control techniques and their comparison. Recently, focus has shifted to designing autonomous quadrotors. A summary of the various localization and navigation techniques has been given. Lastly, the paper investigates the potential applications of quadrotors and their role in multi-agent systems.

Abstract: Electronic applications are described that would benefit from the availability of high temperature semiconductor devices. Comparisons are made among potential materials for these devices and the problems of each are discussed. Recent progress in developing silicon carbide as a high temperature semiconductor is described.

Abstract: Basic types of DC-DC converters, when operating in discontinuous conduction mode, have self power factor correction (PFC) property, that is, if these converters are connected to the rectified AC line, they have the capability to give higher power factor by the nature of their topologies. Input current feedback is unnecessary when these converters are employed to improve power factor. In this paper, basic types of DC-DC converter topologies are studied to investigate their self-PFC capabilities. Their input characteristics are compared and their input line current waveforms are predicted.

Abstract: Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. These models learn in a supervised fashion where a set of feature vectors with expected output is provided. It is very common practice to engineer new features from the provided feature set. Such engineered features will either augment, or replace portions of the existing feature vector. These engineered features are essentially calculated fields, based on the values of the other features. Engineering such features is primarily a manual, time-consuming task. Additionally, each type of model will respond differently to different types of engineered features. This paper reports on empirical research to demonstrate what types of engineered features are best suited to which machine learning model type. This is accomplished by generating several datasets that are designed to benefit from a particular type of engineered feature. The experiment demonstrates to what degree the machine learning model is capable of synthesizing the needed feature on its own. If a model is capable of synthesizing an engineered feature, it is not necessary to provide that feature. The research demonstrated that the studied models do indeed perform differently with various types of engineered features.