The goal of the research is to discover and create a means to convert a text representation of UML class diagrams into its corresponding visual form. UML class diagrams describe the design of a software system and are widely used throughout industry and academics.

The text representation of UML used here is called yUML. The approach translates yUML to a graph representation language, called dot, which is then rendered via the graphical rendering engine Graphviz. Each component of yUML must be parsed and translated into the corresponding graph component in dot. A parser generator, ANTLR, is used to develop the necessary software to parse yUML and convert it into dot. An ANTLR grammar for yUML is created for this process.

The research is realized as a software tool that takes yUML as input and generates the dot representation. Then, Graphviz is used to graphically render the diagram. The yUML is automatically generated from an existing software system that leverages the srcML infrastructure. srcML is an infrastructure to support the analysis and exploration of source code. The results of this research allow the ability to recover a visual UML class diagram for any given software system. A current prototype has been developed and it is being evaluated for accuracy and scalability.

Future work consists of adding different layout algorithms and evaluating their comprehensibility. The resulting tool will be an effective and useful means to assist programmers in understanding large software systems.

Simulation and control of rigid bodies are important aspects of modern aerospace science. Recent decades have seen considerable development of algorithms known as Lie group variational integrators which can be used for simulation of a controlled rigid body. These methods draw both on classical and numerical aspects of mathematics.

In this project we develop C++ code for a Lie group variational integrator with the goal of simulating a Tie Fighter, a fictional spacecraft from Star Wars.

An interesting challenge in this problem is the coordination of the physics, computer science, and mathematical aspects of the simulation. Particular challenges in this problem include interpreting and implementing pseudocode from mathematics publications; mathematically describing the shape and mass distribution of a Tie Fighter and calculating associated physical quantities such as the inertia tensor; understanding aspects of pure mathematics which appear in the problem such as matrix Lie groups and Lie algebras; designing and testing thrust sequences for the Tie-Fighter; and creating useful visualizations of output from the C++ program. Results will be presented in poster format.

Hybrid Unmanned Aircraft System with Long Flight Autonomy and Capable of Lifting 20 lbs of Payload

Students: Seth Mathew Miller, Cody Thomas Langenfeld, Jim Keyser, Derek Ellinger and Robert Menarcheck Advisor: Dr. Vladimir Gurau

Engineering Technology Department Kent State University at Tuscarawas, 330 University Dr. N.E., New Philadelphia, OH 44663

Introduction Unmanned Aircraft Systems (UASs) are systems comprising of an unmanned aircraft vehicle (UAV), its payloads, the control station, its support subsystem and its communication subsystem [1]. UASs can be used for cargo / package delivery or as sensor platforms for data acquisition [2] such as aerial mapping, aerial surveying, precision agriculture (crop health or crop damage assessment), natural resource management (wildlife census, impact of human activities on wildlife), inspection of industrial and civil infrastructure, aerial filming and photography, news reporting or intelligence, surveillance, reconnaissance and emergency response. Objectives The current project represents Phase 3 of a four-phase endeavor at Kent State University at Tuscarawas. Its general objectives are to design, build and test an UAS consisting of an octocopter as UAV (Figure 1) for data acquisition and capable of operating under manual control, stabilized control and automated control (autopilot). The system has video acquisition and recording subsystem consisting of an SJ 4000 action camera mounted on a 3–axes gimbal for image stabilization (Figure 2) and first person view (FPV) subsystem consisting of an HD camera, 5.8 GHz video transmitter and receiver modules and on-screen display (OSD) for transmitting telemetry data (Figure 3).

Figure 1 The Unmanned Aircraft Vehicle (Octocopter) Built at KSU Tuscarawas To secure an increased flight autonomy, the power electrical system is hybrid and will consist of six lithium-polymer batteries recharged by a 0.5 kW High-Temperature Proton Exchange Membrane Fuel Cell (HT-PEMFC) stack designed and built at KSU Tuscarawas (Figure 4).

Since CNC machine tools for additive manufacturing, subtractive manufacturing and laser cutting operations have similar design features, it would be of interest to design and build a universal manufacturing / prototyping machine capable of performing all three types of operations. The objective of the current project is to design, build and operate a CNC rapid prototyping machine that can be used alternatively for CNC milling operations, 3D printing and laser cutting. The universal machine will consist of a stage that supports the workpiece and capable of moving along the Y axis of a Cartesian coordinate system, and two piggybacked X and Z axes supporting the tool. The axes will move along two linear slides [2], each containing two ball bearing blocks [3]. The motion is controlled by NEMA 17 unipolar stepper motors with a step angle of 1.8^o per pulse [4], connected to leadscrews [5] through flexible couplers [6]. A common tool holder plate designed to hold a spindle [7] for CNC milling operations, a filament extruder [8] for 3D printing or a laser [9] for laser cutting operations will be attached to the X axis. A Smoothieboard controller [10] will be used to interface the computer to the stepper motors and convert the coded alphanumeric data into train of pulses sent to the stepper motors.

The advantage of the proposed technology consists in its low cost and in its capability to perform a variety of CNC manufacturing operations using a single machine tool.

Comparative Graph Model is a facial recognition algorithm that takes several images of the same face, and identifies common features of that face. The algorithm looks at the closeness of features along with how many times each exists, to decide which features are the best representation of a face. The algorithm takes features from several images and condenses them down to one image, which is then used for comparison to other images. This algorithm uses a different approach than many facial recognition algorithms, which use a template to seek out facial features and make associations. CGM uses only data that is present to form associations. This makes the algorithm unsupervised, and can easily be used for other domains like object recognition. In this experiment, CGM is used in conjunction with a perceptron neural network to compare positive and negative images. Ten training images were condensed to one image via CGM, and compared against one positive image (the same person), and one negative image (a different person). In the experiment, each trial was run for 100 iterations. The algorithm recognized the same person 71% correctly, did not recognize a different person 79% correctly, with an overall correctness of 61%. Using an unsupervised algorithm is beneficial across many disciplines, especially where a pattern can be too hard to find or represent. The algorithm does not need to know what it is looking for to make associations, which makes it a good candidate for finding complex patterns and associations in data.