Cary Xiao
ABSTRACT
In our current world of increasingly common natural disasters, new technologies need to be developed to bolster our society's infrastructure during disaster-relief scenarios. Among these services, our communication networks currently serve to be particularly vulnerable, requiring both a constantly operational power grid and a fully functional network to connect residents. This paper aims to design two low cost, low maintenance wireless Free Space Optical Communications (FSOC) designs and connections that are able to function under most points of weather. The devices should sustain a high uptime, have a low Bit Error Rate (BER), and use inexpensive materials and devices to maintain their connection. This system was developed to help reduce this vulnerability of communication infrastructure during disaster relief scenarios. In this paper, we will test two devices' ability to collect data under different weather conditions. First, Arduino Unos sent information by flashing handheld laser diodes and received information by decoding light levels from an array of photodiodes. Second, each device was then upgraded by changing to Arduino Dues and redesigning the Printed Circuit Board, gear mechanism, and enclosure. A Machine Learning-based error correction system was then designed and implemented, using Manchester Encoding and a restricted character set to detect and automatically correct trivial errors. After sending the information to a connected computer, messages with erroneous characters were pushed through a Java-based Machine Learning algorithm to further reduce errors before being displayed. Testing has shown this system to be highly effective in correcting errors within messages that used words trained with the algorithm. With further development, this system aims to be rapidly deployed directly after natural disasters, providing emergency communications until the primary power grid and network infrastructure can be fully repaired.
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Index Terms
- A low-power, machine learning-based optical communications system for disaster relief
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