Our project’s purpose is to teach beginner players how to play a game of chess by showing them how each piece moves
on the board. Our project will enable two people that have little to no knowledge of the game to play a game of chess
against each other without an instructor. The chess board will light up the squares in which a chess piece can move once a
player picks up a piece, no matter the state of the game. This is done by using an illumination system that is built into the
bases that are under each piece. After the IR light is transmitted through a hole in the square, a photodiode will receive
this light and record an output given as a voltage. We can differentiate between piece types as each type of chess piece
will have filters on the bottom of the base that will adjust the intensity of the IR light emitted. Once the photodiodes of the
piece identification system receive light, they will send a voltage reading to the microcontroller that will enable the correct
RGB LEDs to light up the board. By showing where a chess piece can move visually on the board, this will let the players
learn how each of the different chess pieces move around on the board faster and easier while providing a fun and
engaging experience during their game. We believe that this project is an improvement of other electronic based chess
boards through the use of photonics as our board will provide a quicker and more seamless playing experience without
sacrificing any of the natural chess qualities.
RASS aims to remove the objectiveness of gem characterization through a proof-of-concept technological approach. It
will combine the most popular forms of ruby identification tests, fluorescence and dichroism, and unify them into one
system that will test specifically rubies and be able to tell whether its real (whether that be synthetic or natural) or fake
(made of garnet, dyed quartz, etc.). It will use both a white LED and green laser to illuminate the ruby and read back
dichroic images of the ruby and the fluorescence spectra. Our system will be more reliable than the standard tests today,
as our results will be quantitative and available to people who are colorblind.
Project Website
(Must be on UCF WIFI or VPN to access)Advisors: Peter Delfyett, Kalpathy Sundaram, Alfons Schulte