Project

# Title Team Members TA Documents Sponsor
52 Waterski Tracker
Jack Bay
Ryder Heit
Sam Knight
Jialiang Zhang design_document2.pdf
final_paper1.pdf
presentation1.pdf
proposal1.pdf
#Waterski Tracker



Team Members:

-Jack Bay (jackrb2)

-Ryder Heit (ryderch2)

-Sam Knight (sknight5)



#Problem

Our idea for the project revolves around the sport of competitive waterskiing. In 3 event competitive waterskiing, one of the events is slalom skiing which involves going around 6 buoys, and passing through entrance and exit gates. Many skiers are very precise and particular people who love their sport, getting specialized gear and practicing their 16 second runs for hours on end. Ask any skier about their pass and they will spit technical lingo at you at rapid fire, things like "Well I didn't have my hips up around ball four, and I didn't have enough angle going into 6 to make the pass". One of the caveats with getting to the ‘next level’ in competitive waterskiing is finding adequate feedback that allows a skier to quantify their runs in such a way that form can be adjusted, and results can be seen in said feedback. Outside of direct film of the water skier running the course, there is no method of recording and analyzing a skier’s runs.



#Solution

My project aims to put data to these claims, providing a way for skiers to quantify their passes and combine data and video into a cohesive tracker that allows them to study their attempts in a new way, and isolate problems with their skiing better than the guesswork that accompanies watching film.



#Components



So what data are we looking for? We want all the movement data, as well as a line on the map. So, this means we need a gyro, accelerometer, and GPS chip to collect all this data, and use Kalman filtering as well as the maps api to smooth it out, and then put the path taken over a map of the course. We will also need a way to map the course data itself. As slalom courses are taken in and out each year, satellite images will not be enough to ensure accurate course bouy locations. We can expand this to sync with phone video of the run, providing all the data at once while the visual plays to better identify errors.



##Collection



For our device, there are 3 main sensors that we will be using to get the most accurate assessment of your skiing.



###Gyroscope



A gyroscope will be added to monitor the tilt of the skis. This will allow the skier to learn if they were properly “on edge” at the right time. It is important when skiing to ensure that your ski is properly rolled to cut through the wake. It is currently a very qualitative process, so being able to quantify it with a gyroscope is invaluable. The gyro is also used in conjunction with the GPS to figure out the skier’s “angle” coming out of each buoy. This allows the skier to better hone their runs to perfection.



###Accelerometer



An accelerometer will be used to gather the skier's speed data. This is important so that the skiier can know where they are losing and gaining speed throughout the run. It is critical to ensure speed is maintained through the turn. This is also to be used in conjunction with the Gyroscope data to get more accurate readings during the run.



###GPS



Finally, a GPS will be in the system to provide location data. The location information is important for our analysis. This will allow us to take the data and put it on a map, which gives the angle coming out of the buoys as well as invaluable visualization of the line taken. That means that a skier can see how they did at all positions during the run. The GPS is also used to piece together our various data sources. Using Kalman Filtering we can remove noise from our three sensors.



##Storage

###Data Storage

Since this project requires very heavy data analysis of many sensors and graphical display, we think that doing direct analysis on a microcontroller is unwise. We will be using the board to take data from the various sensors and store it on a local SD card. This allows us to analyze the data asynchronously. We will also store multiple runs so that you do not need to replace the sd card after every slalom run, simply press a button and a new run will be made.





##Analysis



###Kalman Filtering

Kalman filtering is a technique wherein multiple sensors are intelligently averaged to smooth out the data collected by each. Using the GPS, gyro, and accelerometer together, we can paint the clearest picture of what happened and smooth out any anomalies or outliers in the data.



###Data Syncing

By using timestamping in all of our data collecting, we can put together a visual presentation of the run in some kind of interface that allows the skier to watch back the run with the video, GPS (map data), and gyro data all playing back at the same time to get the best picture of exactly what they’re doing during the pass.



##Power

The main power component will be a central battery that supplies each component. The battery itself will be rechargeable and will be linked to a system that indicates the status of the battery’s charge via LEDs.



##Mechanical Hardware



The mechanical subsystem has two main components, waterproofing and attachment.



### Attachment



The first mechanical challenge we will run into is attaching our PCB and system to the waterski. This is important so that the data can be gathered from the actual ski. We need to devise an enclosure that can attach to the already in place binding holes so that the ski does not need to be physically modified in any sense.



### Waterproofing



The next challenge of our mechanical enclosure is waterproofing. Since this device will be used during intense waterskiing, it is imperative that it is completely watertight. If any water were to enter the enclosure, we would have complete electronic failure and potentially worse. There will be external buttons and LEDs, but these also will have to be watertight so as not to leak water into our pcb.







#Criterion For Success (High Level Goals)



## Efficacy for Water Skiing

It is important to us that our product is usable by a water skier. This means that it must both attach to a water ski and be waterproof. It must also have usable controls and information that can be seen by a water skier. This will be buttons and LEDs that will show status and provide control. The enclosure being waterproof is critical because it must be able to both withstand the spray created when skiing and possible submersion after a fall.

Resonant Cavity Field Profiler

Salaj Ganesh, Max Goin, Furkan Yazici

Resonant Cavity Field Profiler

Featured Project

# Team Members:

- Max Goin (jgoin2)

- Furkan Yazici (fyazici2)

- Salaj Ganesh (salajg2)

# Problem

We are interested in completing the project proposal submitted by Starfire for designing a device to tune Resonant Cavity Particle Accelerators. We are working with Tom Houlahan, the engineer responsible for the project, and have met with him to discuss the project already.

Resonant Cavity Particle Accelerators require fine control and characterization of their electric field to function correctly. This can be accomplished by pulling a metal bead through the cavities displacing empty volume occupied by the field, resulting in measurable changes to its operation. This is typically done manually, which is very time-consuming (can take up to 2 days).

# Solution

We intend on massively speeding up this process by designing an apparatus to automate the process using a microcontroller and stepper motor driver. This device will move the bead through all 4 cavities of the accelerator while simultaneously making measurements to estimate the current field conditions in response to the bead. This will help technicians properly tune the cavities to obtain optimum performance.

# Solution Components

## MCU:

STM32Fxxx (depending on availability)

Supplies drive signals to a stepper motor to step the metal bead through the 4 quadrants of the RF cavity. Controls a front panel to indicate the current state of the system. Communicates to an external computer to allow the user to set operating conditions and to log position and field intensity data for further analysis.

An MCU with a decent onboard ADC and DAC would be preferred to keep design complexity minimum. Otherwise, high MIPS performance isn’t critical.

## Frequency-Lock Circuitry:

Maintains a drive frequency that is equal to the resonant frequency. A series of op-amps will filter and form a control loop from output signals from the RF front end before sampling by the ADCs. 2 Op-Amps will be required for this task with no specific performance requirements.

## AC/DC Conversion & Regulation:

Takes an AC voltage(120V, 60Hz) from the wall and supplies a stable DC voltage to power MCU and motor driver. Ripple output must meet minimum specifications as stated in the selected MCU datasheet.

## Stepper Drive:

IC to control a stepper motor. There are many options available, for example, a Trinamic TMC2100. Any stepper driver with a decent resolution will work just fine. The stepper motor will not experience large loading, so the part choice can be very flexible.

## ADC/DAC:

Samples feedback signals from the RF front end and outputs the digital signal to MCU. This component may also be built into the MCU.

## Front Panel Indicator:

Displays the system's current state, most likely a couple of LEDs indicating progress/completion of tuning.

## USB Interface:

Establishes communication between the MCU and computer. This component may also be built into the MCU.

## Software:

Logs the data gathered by the MCU for future use over the USB connection. The position of the metal ball and phase shift will be recorded for analysis.

## Test Bed:

We will have a small (~ 1 foot) proof of concept accelerator for the purposes of testing. It will be supplied by Starfire with the required hardware for testing. This can be left in the lab for us to use as needed. The final demonstration will be with a full-size accelerator.

# Criterion For Success:

- Demonstrate successful field characterization within the resonant cavities on a full-sized accelerator.

- Data will be logged on a PC for later use.

- Characterization completion will be faster than current methods.

- The device would not need any input from an operator until completion.

Project Videos