Inspiration

It is not possible to imagine Halloween without Jack-o-Lanterns. We decided to make an enhanced version of this famed character.

What it does

Auto-Lantern is basically a hardware hack which resembles a Jack-o-Lantern. It has proximity sensors which detects the presence of a person nearby it and tries to spook them out using blinking LEDs and a creepy audio. This audio is played through the computer speakers as the micro-controller serial communicates with the computer on detecting the presence of the person. It also has a light sensor to ensure that there is sufficient darkness before it is triggered.

How we built it

We used a Node-MCU as the micro-controller for the project along with other hardware components like a HC-SR04 proximity sensor, an LDR as the light sensor and a couple of LEDs along with jumper wires and a breadboard. We also made use of a cut-out of the Jack-O-Lantern.

Challenges we ran into

We initially had no clue how to play the audio upon triggering of the lantern. After some deliberation, we decided to use the Pyserial python library for the purpose so that the audio can be played through the computer speakers. We also spent some significant amount of time fine-tuning the code so that the blinking lights perfectly match with the audio. We tried to implement cockroachdb to store the victim count but we were unable to execute it due to time constraint.

Accomplishments that we're proud of

We take pride in having been able to complete this hack in the limited time available since we haven't done a hardware hack of such complexity before. We are also proud to move into frameworks and learn the to interface the sensors.

What we learned

We learned to interface various sensors such as the HC-SR04 and the LDR with the micro-controller. We also learned to use the Pyserial library to communicate with the micro-controller via Python using serial communication.

What's next for Auto-Lantern

We're planning to make an upgraded version of the Auto-Lantern with Image and Voice-Recognition capabilities. So we'll make a deep learning model which will distinguish between different age groups and give them the scare accordingly.

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