1. Ameriranikistan Muhammad Ahmad Kyle Huston Farhad Majdeteimouri Dan Mackin

2. Project Overview Uses Self Organizing Maps to Model Fiber Optic Cables in Matlab. Speckle Pattern is recorded in your typical webcam and then passed into Matlab. We will use the Image Acquisition Toolbox, in Matlab, to analyze the speckle pattern. The speckle pattern will change as the user taps on the fiber strand in various locations. The Self-Organizing Map algorithm will learn the topology of the fiber.

3. Synopsis Laser light is shone through an optical fiber Camera (CCD) receives the light on the other end User taps random points on the fiber Algorithm gradually learns to make a map of the fiber Display shows the location of all touches going forward

4. System Block Diagram LaserMulti-Mode FiberCamera Algorithm Display Hardware Software USB

5. Hardware Optical Breadboard Red Laser Diode Focusing and Collimating Lenses Mirrors Multi-Mode Fiber Camera (USB Web Cam or CCD Sensor) Personal Computer

6. Self Organizing Map (SOM) Unsupervised Artificial Neural Network (ANN) Algorithm Pattern Recognition Cluster the Distribution of the Input Space Reduce the dimensionality and visualize high dimensional data preserving the most significant features Extract the underlying topology in the input (signal) space

7. Software Matlab is our choice of software: – SOM is based on linear algebra – Efficient matrix and vector computations – Easy creation of scientific and engineering graphics – Extensibility (Tool Boxes) – I/O functions

8. Matlab Demo

9. System Setup

10. Division of Labor Hardware/Calibration – Kyle, Dan Image Processing – Muhammad, Farhad SOM Algorithm – Farhad, Kyle Display Routines – Dan, Muhammad

11. Timeline

12. Cost Estimate Optical Breadboard$700 Red Laser Diode$20 Focusing and collimating lenses$20 Multi-Mode Fiber$30 Camera (USB Web Cam)$80 Personal ComputerN/A Total$850

13. Risk analysis The experimental nature of SOM could lead to delays in the project timeline Any learning curve for the Matlab Image Acquisition Toolbox Running the algorithm using a scripting language (Matlab) on a best effort OS (Windows) could prove costly Hardware issues (e.g. Fiber breakage and calibration issues)

14. Safeguards against risks Early Integration and Testing Prototyping the SOM Algorithm

15. Possible Improvements Automated fiber tapping mechanism for training the SOM algorithm Optimizing the SOM

16. Applications Using multiple fibers to model the behavior of flexible structures in space Animation development without any sensor dependencies

17. References Self-Organizing Maps by T. Kohonen Professor Dana Anderson in the physics department

18. ¿Questions?