 The ocean is a magnificent place, only to a majority of all life on Earth and covering over 70% of the Earth's surface, there's always something to learn from the sea. The definitions of coastline vary, but it is agreed that hundreds of thousands of miles of coastline exist on the Earth. Scientists believe that if all of the wave energy along the coastlines of the world is harnessed annually, it could satisfy the entire world's electricity for that time period. Understanding the ocean could lead to scientific developments in many different fields. However, there's a lack of resources available to provide measurements near the coastline. The smart fin is a solution to that issue. The smart fin is a long board surfboard fin that is capable of gathering much data through its sensors including, if it is in the water or not, the temperature of the water that it's in, its location, and acceleration. From these sensors we will be able to reduce much other information such as wave height. With the smart fin scientists will be able to gather denser data from many beaches, produce more accurate wave fight and water temperature forecasts, find out where, when, how long people are surfing, and more. Through experiments conducted by the San Diego SRIPS beer, we have been able to compare the smart fin's temperature readings with those of the beer and also analyze its GPS sensors. In the future we will compare wave height readings determined by the smart fin's accelerometer and algorithms to the beer's wave height readings as measured and analyzed through the pressure sensor. The data we got from the smart fin was in an encrypted format as you can see here. We had to implement a decoder in Python which was able to produce our data in a table. We also further optimized our decoder by incorporating data analysis methods. Therefore we were able to graph things such as temperature histograms and temperature over time and GPS sensor measurements. Our smart fin produces inertial data such as acceleration, gyroscope and magnetometer. We are currently working on determining the exact position of the smart fin. In order to get position we would need to double integrate our acceleration and along with that coupled with citizen science there comes a lot of noise in our data. Therefore we are implementing a Kalman filter which uses linear values from our sensors to predict the exact position of our fin. This flow chart right here represents our programming logic and we hope to add more angular measurements such as gyroscope data and heading to our program. So far we have values for our transition matrices and we have working code on Jupyter notebooks for these linear values and we will be optimizing this with process noise and we will also work on common smoothening in the future. We are using spectral analysis to determine the wave height from our acceleration data. Spectral analysis is a function that provides information about how power is distributed by analyzing the frequency domain of a function. A Kalman filter will determine the vertical displacement which spectral analysis will then process into wave height. Fourier transforms are used to show the frequency domain of a function in the time domain or vice versa. It's very useful for sine functions which are what we use to represent waves. As you can see below there is a combination of sine functions expressed as a few frequencies in the frequency domain. We currently have code that is capable of taking CDIP vertical displacement and producing accurate significant wave height graphs and we are using HM0 as opposed to HS. HS is the average height of the top one third of the waves. HM0 is a little bit more complicated than that. It involves an integral. None of the work we accomplished would have been possible without E4E and our amazing supervisors Ryan Kastner, Kurt Surgers and Nathan Huey. Thanks to their involvement and the help of our team shown in the picture above. We were able to accomplish much this summer and we'll accomplish much more in the future. Thank you.