Engineering Commons^™

Involuntary Signal-Based Grounding Of Civilian Unmanned Aerial Systems (Uas) In Civilian Airspace, Keith Conley

Master's Theses

This thesis investigates the involuntary signal-based grounding of civilian unmanned aerial systems (UAS) in unauthorized air spaces. The technique proposed here will forcibly land unauthorized UAS in a given area in such a way that the UAS will not be harmed, and the pilot cannot stop the landing. The technique will not involuntarily ground authorized drones which will be determined prior to the landing. Unauthorized airspaces include military bases, university campuses, areas affected by a natural disaster, and stadiums for public events. This thesis proposes an early prototype of a hardware-based signal based involuntary grounding technique to handle the problem …

An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi

Master's Theses

The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have been widely used for high-order nonlinear system control. This thesis studies the application of sliding mode control …

Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball

Master's Theses

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …

Multi-Vector Tracking Of Wifi And Zigbee Devices, Calvin Andrew Laverty

Master's Theses

Location privacy preservation has shifted to the forefront of discussions about next generation wireless networks. While pseudonym-changing schemes have been proposed to preserve an individual's privacy, simulation has shown that new association attack models render these schemes useless. The major contribution of this thesis is the implementation of a tracking network with commodity hardware on the California Polytechnic State University campus which leverages the combination of de-anonymization strategies on captured wireless network data to show the effectiveness of a pseudonym-changing scheme for wireless identification across WiFi and Zigbee protocols.

Evaluating Creative Choice In K-12 Computer Science Curriculum, Kirsten L. Mork

Master's Theses

Computer Science is an increasingly important topic in K-12 education. Ever since the "computing crisis" of the early 2000s, where enrollment in CS dropped by over half in a five year span, increasing research has gone into improving and broadening enrollment in CS courses. Research shows the importance of introducing CS at a young age and the need for more exposure for younger children and young adults alike in order to work towards equity in the field. While there are many reasons for disinterest in CS courses, studies found one reason young adults do not want to study CS is …

Smart Dc Wall Outlet Design With Improved Load Voltage Detection, Patrick Donovon Granieri

Master's Theses

A standard home in the United States has access to the 120V AC power grid for use with home appliances. Many electronics used at home are powered by a DC power supply, which loses energy in the conversion from AC power. The DC House project avoids any conversion between AC and DC by storing energy in batteries as DC power and supplying it directly to DC appliances. While AC systems feature a standardized output voltage, no such standard exists for DC systems. The Smart DC Wall Outlet solves this by automatically adjusting its output voltage to meet any required DC …

Robot Navigation In Cluttered Environments With Deep Reinforcement Learning, Ryan Weideman

Master's Theses

The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an unmapped, cluttered environment and be able to navigate in a desired direction with no prior knowledge. Deep Q-learning, coupled with the innovations of double Q-learning and dueling Q-networks, is applied. Two modifications of this architecture are presented to incorporate direction heading information that …

Surveying Underwater Shipwrecks With Probabilistic Roadmaps, Amy Jeannette Lewis

Master's Theses

Almost two thirds of the Earth's surface is covered in ocean, and yet, only about 5% of it is mapped. There are an unknown amount of sunken ships, planes, and other artifacts hidden below the sea. Extensive search via boat and a sonar tow fish following a standard lawnmower pattern is used to identify sites of interest. Then, if a site has been determined to potentially be historically significant, the most common next step is a survey by either a human dive team or remotely operated vehicle. These are time consuming, error prone, and potentially dangerous options, but autonomous underwater …

A Machine Learning Approach To Network Intrusion Detection System Using K Nearest Neighbor And Random Forest, Ilemona S. Atawodi

Master's Theses

The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date.

A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented …

Comparison Of Lqr And Lqr-Mrac For Linear Tractor-Trailer Model, Kevin Richard Gasik

Master's Theses

The United States trucking industry is immense. Employing over three million drivers and traveling to every city in the country. Semi-Trucks travel millions of miles each week and encompass roads that civilians travel on. These vehicles should be safe and allow efficient travel for all. Autonomous vehicles have been discussed in controls for many decades. Now fleets of autonomous vehicles are beginning their integration into society. The ability to create an autonomous system requires domain and system specific knowledge. Approaches to implement a fully autonomous vehicle have been developed using different techniques in control systems such as Kalman Filters, Neural …

Relevance Analysis For Document Retrieval, Eric Labouve

Master's Theses

Document retrieval systems recover documents from a dataset and order them according to their perceived relevance to a user’s search query. This is a difficult task for machines to accomplish because there exists a semantic gap between the meaning of the terms in a user’s literal query and a user’s true intentions. Even with this ambiguity that arises with a lack of context, users still expect that the set of documents returned by a search engine is both highly relevant to their query and properly ordered. The focus of this thesis is on document retrieval systems that explore methods of …