Computer face recognition and identification is an important problem with numerous applications in intelligent user interfaces, security, and accessibility. For example, identification of faces can be used for access to restricted areas, authentication without keyboards, and personalized interfaces. Traditional algorithms that solve this problem identify faces individually or use co-occurrence information for a given set of people (For example, person X is often photographed with person Y). However, such systems have many errors, and cannot generalize well to people the system has not seen together previously. In contrast, humans are much better at this because we have "common sense." We can look at a photograph and recognize that people tend to photograph with family, friends, and co-workers. We can further guess why the photograph (birthday, conference) was taken, and even where (person's house, a certain park). This abstract knowledge is very difficult for computer algorithms to learn because there are not sets of these "rules" available explicitly. In our work, we tackle this problem using structured relational graphs like Freebase, a large online graph of billions of facts about the world. Given many specific examples of people who are in photographs together and data about those photographs, we can see what connects these people on graphs like Freebase, and generalize to the "common sense" rules that are notoriously difficult to learn. Then, given a new image and possible candidates for identification of people in it, we can compute the most likely set of correct identifications. We can also hypothesize unknown faces to our existing faces. This allows us to learn, on-the-fly, a new recognizer for that specific person. Finally, by knowing relationships behind photograph subjects, we can discover and generate useful contextual information about the photograph, such as location and possible reason for the photograph. Broadly, this means that computer systems can more accurately identify meaning behind photographs; a problem that has been historically hard for computers to accomplish.

Multi-layer circuit boards are expensive; each additional layer significantly increases their production cost, however multiple layers are commonly necessary to avoid routing conflicts. Due to the large number and high density of pins on some components, a design issue is determining the minimum number of layers required to resolve all routing conflicts. Some buses must be assigned to consecutive layers, which add further complexity to the routing problem. We propose an improvement to an algorithm based on branch-and-bound for designing an optimal layer assignment for printed circuit board (PCB) bus escape routing. We use concepts from vehicle routing and scheduling algorithms to efficiently traverse the branch-and-bound tree. Our algorithm is guaranteed to provide a feasible layer assignment for each bus with the minimum number of layers.

Random fractal patterns exhibit an overwhelming distribution throughout the natural world, from the smallest scale we can see, to the most universal scales we can imagine, and appear to affect everything in between. In order to explore some small-scale examples of this, we used high voltage to burn surface tracking patterns into various media, then analyzed the fractal dimension of some of the better samples. Next, we theoretically modeled radial diffusion-limited aggregation clusters (DLA) and a linear dielectric breakdown model by computer simulations, and analyzed the Hausdorff dimensionality of those, as well. Similar to previous experiments by Kim and Roh, and Niemeyer et. al., the experimentally and physically modeled results agreed in their dimensionality. This comparative analysis model implies a method of non-destructive analysis for a broad spectrum of insulating materials. These processes could help engineers design more robust dielectric components or safer airplanes, help geologists better understand fault-fracture spreading and aid in earthquake prediction, or enable atmospheric scientists to study elusive lightning structures known as sprites.

The prevalence of smartphones has dramatically increased over the last several years, and with this comes an unparalleled medium for data collection. Smartphones are constantly transmitting data, and with an approximated 56% of American adults owning a smartphone, we now have a data medium that encompasses the majority of the population. One of the most valuable data that we can obtain through this medium is location data. By measuring the signal strength of a smartphone from a number external access points, we can collect accurate, real-time location data about a given user. This system has wide spread applications, whether it is being used academically for research purposes to measure travel patterns of users, or commercially to develop automated systems for building control. However, there is yet an easily accessible way to utilize the valuable information that surrounds us. Inexpensive, readily available hardware and software can be configured to develop a system that can be used to harness this data, and successfully doing so offers many opportunities. This can be done by using wireless adapters as access points, and calculating the location with triangulation with multiple nodes.

It is often forgotten students are learning to build a better future. As a group of students, we are looking to create a form of innovative technology that provides a learning experience through two of the most important learning methods: visual and kinesthetic. To provide hands on learning experience, we have created the Dynamic Mathematical Display (DMD). The DMD is a device with pins placed within a three by three grid. The pins are 10 cm long, 3.5 cm wide, and each controlled by individual motors and slide potentiometers. The dynamic movement of each pin is measured by the potentiometer's linear resistance, which is monitored electronically by a computer program. That program will engage the pin to move to a given point projected from 3D graph. The main purpose of the DMD project is to create a morphing table that will allow a user to visualize complicated 3D graphs with all their components such as depths and peaks. These components will be represented by red, green, and blue light emitting diodes (RGB LEDs), which are attached to each pin. The LED's are programmed to provide color based on the physical location of the pin at the time according to the projected graph. Both instructors and students will have the option to manipulate the brightness and color of the LEDs. While the pins provide kinesthetic information to a graph, the LEDs provide additional visual information similar to a topographic map, enhancing the learning experience of a student by presenting more realistic and information-rich representations of physical models.