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A Campus Kiosk Kendal Hershberger & David Moses Union University Scholarship Symposium April 30, 2007

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Purpose ► ► Make an application to give directions to people new to campus who may not know their way around ► ► Make the application easily accessible ► ► Scalability

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Choosing the Language ► ► OpenGL using C Advantage - already familiar with the language and graphics library Disadvantage - difficulty of drawing an entire campus ► ► DirectX Disadvantage – Microsoft-specific systems

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Choosing the Language ► ► Flash and ActionScript Advantages ► ► Very accessible and easy to update ► ► Opportunity to learn a new language ► ► ActionScript has many similarites to Java and other high-level languages ► ► Easy drawing utilities Disadvantages ► ► Unfamiliar

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ActionScript ► ► scripting language behind Flash applications ► ► ActionScript 2.0 new object-oriented model ► ► Class-based, but classes defined in external.as file

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Graph Searching ► ► Graphs consist of nodes and vectors (or edges). ► ► A node can be connected to another node via an edge. ► ► For our case, the edges are paths that traverse Union's campus, and each node represents a geographical point where they connect or terminate.

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Graph Searching ► ► Example:

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Graph Searching ► ► Depth Depth First Iterative Deepening Best First ► Breadth Breadth First Uniform Cost Dijkstra's Algorithm ► Problem: Find the best path from one node (one point on campus) to another node (another point on campus) ► Techniques:

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Graph Searching ► ► Depth First Go to a node, mark it as visited, push it on a stack Go to a neighbour node, and do the same thing If you’re at a dead end, pop the stack until you find an unvisited neighbour Repeat until you’ve traversed the entire tree (optimal path) or until you have found the destination node (non-optimal)

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Graph Searching ► ► Depth First

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Graph Searching ► ► Iterative Deepening Same as Depth First, but depth of search is controlled Better for very large graphs, still slow. ► ► Best First Greedy algorithm (not uninformed) that simply selects the closest node to the destination VERY fast, but can generate horrible paths under certain conditions

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Graph Searching ► ► Breadth First Go to a node, put all of its neighbour in a queue Visit each neighbour in the order they were placed, and put all of its neighbours in a queue Continue until all nodes have been visited (optimal path) or until destination node is found (non-optimal path)

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Graph Searching ► ► Breadth First

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Graph Searching ► ► Uniform Cost Same as Breadth First Search, but prioritizes nodes that have shortest path from origin rather than simply the next node in the queue More realistic paths, but still takes long ► ► Dijkstra's Algorithm Same as Uniform Cost, but actively keeps track of shortest path to node Uses relaxation of the path cost Much faster than all others

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Graph Searching ► dijkstra = function (origin:Node, destination:Node) { ► var S:Array = new Array();// list of visited nodes ► var Q:Array = Node.getNodes(); // list of unvisited nodes ► origin.dv = 0;// initialize distance at origin to ► ► do { ► var u:Node = extractMin(Q);// remove node with lowest distance ► S.push(u);// mark it as visited ► var neighbors:Array = u.getNeighbors();// relax u's neighbors ► for (i=0; i

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Graph Searching ► ► Dijkstra’s Algorithm

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Creating the Campus Map ► ► Deciding on using an image or drawing the map ourselves ► ► Using Flash to draw graphics vector vs. raster graphics ► ► Quality ► ► Scalability

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Creating the Campus Map

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Creating Paths ► ► Create a datatype to store the nodes and edges in ActionScript ► ► Make a list of points to place the nodes ► ► Display it in a Flash scene

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Creating Paths ► ► Kirupa’s Graph ADT ► ► Node and Edge classes var a:Node; a = new Node("a", 200, 50); a.addEdge(b, "a->b"); ► ► Uses Depth First to find path ► ► Dynamically draws the path from origin to destination

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Finding the Best Path ► ► Our implementation of the Graph ADT ► ► Tested using points around the Belltower area ► ► Depth First search is fairly accurate and fast

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Finding the Best Path ► ► Completed entry of points for the rest of campus ► ► Our naïve Depth First search algorithm reveals its limitations

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Finding the Best Path ► ► Switched from Kirupa’s Depth First Search and implemented our own Dijkstra’s algorithm in ActionScript ► ► Still needed to provide interface and integrate map with graph

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Building an Interface ► ► Added map layer to Flash scene ► ► Started adding buttons and designing interface components

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Building an Interface ► ► Choosing locations in a List Box

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Debugging ► ► Remaining Bugs Refresh problem ► ► Fixed by deleting the calls to draw. Speed degradation ► ► Caused by unintended doubling of the graph size. Faulty edges ► ► Incorrect data entry… over 500 individual edges!

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Future Possibilities ► ► Interiors of buildings, including classrooms and faculty offices Zoom feature Database integration ► ► Touch screen kiosks located around campus ► ► Print feature

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Demonstation / Questions

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