In this paper, we present Seeking Insanity (working title), a video game developed in the Godot engine. The game is a Metroidvania style action exploration platformer with lore inspired by H.P. Lovecraft’s works. The project was designed and created with industry standard techniques in order to best simulate a professional game development environment. Seeking Insanity explores the notion of building upon the successes of its predecessors in the genre – via shared gameplay mechanics and elements of progression – while innovating through the addition of new mechanics. In particular, we seek to differentiate the game from its competitors through the Sanity system, the likes of which is (to our knowledge) completely new to the genre.
C. Daly Proposal (PDF)
C. Daly Final (PDF)
Procedural generation provides an automatic alternative to manually creating data. With this automatic generation of data, programs are capable of outpacing human generation of resources. A problem arises with automatic generation: unnatural patterns that make the automatic nature of the data obvious. This is particularly apparent in visual data such as textures or maps. There are a multitude of algorithms designed for various applications, such as Perlin noise generation for textures and simulation of airflow within a wind system. Procedural generation has the potential for providing more variety to visual systems than humans reasonably could, and its advancement could allow for more dynamic scientific simulations and more interesting media. Of particular interest for this project are the interactions between the various concepts in procedural generation, as this allows existing algorithms to be applied in a manner that leads to a multitude of permutations. The hope is that by applying cellular automata and Perlin noise together, the end result will be a more appealing solution than either may be on their own. Cellular automata and Perlin noise should allow us to adjust the complexity of generated caves.
J. Hignite Proposal (PDF)
J. Hignite Final (PDF)
The purpose of this research is to compare the effectiveness of traditional Denial of Service (DoS) attack vectors to a new attack method that is specifically designed for use in devices that have limited resources, such as Internet of Things (IoT) devices. New mitigation techniques will also be explored to help prevent, or reduce the effectiveness of, these attacks. While classical DoS attacks generally require both a large source of computing power and a specially crafted payload to be able to efficiently render the target machine or service inoperable, this research will focus on utilizing an attack that uses a generalized payload that targets a wide variety of internet services, and uses as little resources as possible. We will port the attack to common DoS utilities, as well as to a powerful IoT worm, so that the original tools’ attack methods can be compared to the new attack’s effectiveness and resource consumption. Once done, they will again be compared, but when attacking new mitigation techniques specifically designed to thwart both these and other attacks of their class. The results of this research can be applied to helping defend internet-facing web services from attack in both the public and private sector, because a free and open local proxy is cheaper and easier to setup than an online, paid, cloud solution.
R. Roe Proposal (PDF)
R. Roe Final (PDF)
Today there is no ”Google for video” that actually searches the con- tent of the video. Content-based video search can be achieved by indexing video scene transcriptions much like a film script. We have developed a tool that automatically processes video and produces a transcript representing its contents. Processing involves scene segmentation, face recognition, speech recognition, scene recognition and behavior recognition. In the first semester we accomplished scene segmentation by implementing existing research. During the second semester we applied modern techniques for face recognition, speech recognition, scene recognition, and behavior recognition.
C. Decker Proposal (PDF)
C. Decker Final (PDF)