Electrical & Computer Engineering
Group 8: Automated Kegerator
Brenden Barnhart, bbarnha@siue.edu
Bradley Johnson, bradjoh@siue.edu
Matthew Sekardi, msekard@siue.edu
Stephen Spade, sspade@siue.edu
Automated Kegerator Video (19:07)
Description: Pint Perfect is a company that provides beverage consultation as well as full-service bar cleaning. They claim that their method of cleaning draft lines by filtering the cleaning material through the lines with a recirculatory pump is up to 80 times more effective than the standard method, which involves letting the cleaning material sit in the draft lines over a period of time.
To prove that this claim is true, Pint Perfect has sought the help of a team of biology students at SIUE. The biology team will conduct an experiment where they will test the draft lines of an Edgestar kegerator for microbial growth before cleaning them, and test after using each method of cleaning is used. In preparation for this, the biology team needs a way to empty the kegerator at a rate similar to that of a bar-like scenario to achieve accurate results. They have approached us to accurately automate the emptying of the kegerator.
The goal is to create a system that can operate a kegerator automatically and relay useful information to the biology team. The system will guarantee a hands-free consistent pouring of the beer which appropriately simulates real-life bar scenarios. This system will also easily relay the number of beers poured as well as the amount of beer left in the keg.
Group 6: Automatic Window Blinds
Clarke Baker, clabake@siue.edu
Marie Beatty, mbeatty@siue.edu
Joel Rahlfs, jrahlfs@siue.edu
Zachary Stegman, zastegm@siue.edu
Automatic Window Blinds Video (11:39)
Description: Every home has windows. Letting natural light into your living space is a useful feature in a home. However, how and when that light enters is something humans have been solving for centuries. From light blocking reeds in ancient Egypt, to today’s vertical blinds, controlling the sun has always been always a high priority. We use our window blinds to twist and slide our way to the level of light that suits the sun outside or even to our current mood. What if there were blinds that could be mounted in a window and make those decisions for you?
Our project aims to create an autonomous set of window blinds that can adapt to the light levels inside and outside the home and create the desired “light climate” in your home. With the push of a button, you can tell your blinds what kind of lighting you want. At the same time, you can still manually tilt the blinds up and down. Lastly, there will be no need for a charging cable; our blinds will charge from the sun that you are trying to control. Automatic window blinds will be another way to make your home “smarter”.
Group 3: Universal Brake Light
Savannah Jones, savajon@siue.edu
Derek Marsh, dmarsh@siue.edu
Nicholas Pollos, npollos@siue.edu
McKenzie Woods, mcwoods@siue.edu
Universal Brake Light Video (13:48)
Description: Driving is a dangerous part of everyone’s life. Whether one is a passenger or driving a car, there is no doubt that everyone’s safety is at risk when riding in a vehicle. When driving behind another car, paying attention to their braking is immensely important. The standard brakelights that all cars have are easily noticed if the driver is looking straight ahead. However, if a driver fails to look straight ahead for even a split second, major collisions can occur due to not being able to spot the brake lights of the vehicle.
This product will be a safety addition for any existing vehicle by dynamically changing the lights according to how quickly you slow down. This product enhances safety by alerting the driver behind you so they can react accordingly. The Universal Brake Light will be an easy one-time installation for all vehicles with a rear window.
Group 5: Obstacle Collision Sensor
Nicholas Abalos, nabalos@siue.edu
Derek Flaugher, deflaug@siue.edu
Cameron Milon, cmilon@siue.edu
Jesse Rogers, jesroge@siue.edu
Obstacle Collision Sensor Video (15:19)
Description: The technology that automates obstacle avoidance is new and is being improved on every day. Tesla has a feature within their cars called Autopilot, which enables their vehicles to
automatically avoid pedestrians, vehicles, and other obstructions in its path. We wanted to adopt this concept and apply it on a smaller scale to help those that are blind. The blind have a fundamental need of knowing which direction to walk in order to avoid getting hurt or hurting others by accident for that matter. As of now, the two main ways this is accomplished is by use of a cane or seeing-eye dog. Canes can be an annoyance to those who pass by the blind that use them while seeing-eye dogs are expensive and require much care. In addition, canes and service dogs only help with obstructions that are below the waist. So, we want to design a device that will help the user see above the waist as well as below the waist. With this device, it will be possible the blind to avoid poles, signs, or unpredictable structures that could not be avoided with a cane or seeing-eye dog. Our device will not replace the two current methods for assisting the blind but add functionality that is cheap and is friendly towards other pedestrians.
Our device will be an Obstacle Collision Sensor. It will assist the blind by alerting them through vibration as to how far and in what direction objects are relative to their body. This device will be tailored to individuals that are born blind instead of those that are visually impaired. For the visually impaired, their visual acuity is worse than 20/40 or 20/60 while those that are blind have complete or nearly complete vision loss. We have decided to design for the blind because there are very different factors that play into the kind of information that would be useful to both types of people. For the visually impaired, we would need more components that would assist them in learning how to navigate spaces as someone who cannot see everything.
3However, for those that are born blind, they are already familiar with their inability to see and therefore require less training.
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