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Full-Text Articles in Computer Engineering
Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta
Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where security and privacy are not the foremost concern. When an IoT device is connected to a network, currently there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices, respectively. We introduce mechanisms and protocols for authenticating a …
Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque
Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …
Deep Learning And Transfer Learning In The Classification Of Eeg Signals, Jacob M. Williams
Deep Learning And Transfer Learning In The Classification Of Eeg Signals, Jacob M. Williams
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Deep learning is seldom used in the classification of electroencephalography (EEG) signals, despite achieving state of the art classification accuracies in other spatial and time series data. Instead, most research has continued to use manual feature extraction followed by a traditional classifier, such as SVMs or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. In this thesis, several deep learning architectures are compared to traditional techniques for the classification of visually evoked EEG signals. We …
User Modeling Via Machine Learning And Rule-Based Reasoning To Understand And Predict Errors In Survey Systems, Leonard Cleve Stuart
User Modeling Via Machine Learning And Rule-Based Reasoning To Understand And Predict Errors In Survey Systems, Leonard Cleve Stuart
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
User modeling is traditionally applied to systems were users have a large degree of control over their goals, the content they view, and the manner in which they navigate through the system. These systems aim to both recommend useful goals to users and to assist them in achieving perceived goals. Systems such as online or telephone surveys are different in that users have only a singular goal of survey completion, extremely limited control over navigation, and content is restricted to prescribed set of survey tasks; changing the user modeling problem to one in which the best means of assisting users …