Project

# Title Team Members TA Documents Sponsor
35 Public Safety Alarm
Adrian Wells
Sagar Katiyar
Swetank Griyage
Dean Biskup final_paper1.pdf
other1.pdf
other3.pdf
proposal1.pdf
TEAM MEMBERS:

Swetank Griyage (griyage2), Adrian Wells (aswells4), Sagar Katiyar (sagarsk2)

PROBLEM DESCRIPTION:

Bullying is a major issue in the US and around the world. Many such incidents occur when there are no adults around. Often, it gets ignored and swept under the rug because of 1) stigma 2) lack of evidence. People obviously cannot be at the scene at all times and this becomes a hazard to public and domestic safety. Even with CCTV cameras, there cannot be people at monitoring stations to keep track .

**SOLUTION OVERVIEW:**

Our solution is to design a detection system that takes in the sounds of the environment and alerts authorities if someone, for example, is screaming or shrieking for help. In addition, heat vision and other sensors can analyze the scene to detect when someone is in serious trouble.

**SOLUTION COMPONENTS:**

- Sound input system [Hardware]: Convert sound waves into electrical inputs
- Interface system [Hardware]. Take generated electrical inputs for software analysis
- Training Model [Software] AI-based intelligent system to analyze the input
- Antenna/communication system [Hardware/Software]: Communicate alarm with the receiver or an app
- Feedback system [Hardware]: User-provided feedback at the end of every interaction for improving the model.

**CRITERION FOR SUCCESS:**

Our solution will be successful if

- it can successfully alert another device monitored by an authority when a potential dangerous event takes place.
- Monitoring works continuously without any pauses
- Alerted device is able to give feedback on whether the incident was a false positive or true
- Training model improves with more incidents

VoxBox Robo-Drummer

Craig Bost, Nicholas Dulin, Drake Proffitt

VoxBox Robo-Drummer

Featured Project

Our group proposes to create robot drummer which would respond to human voice "beatboxing" input, via conventional dynamic microphone, and translate the input into the corresponding drum hit performance. For example, if the human user issues a bass-kick voice sound, the robot will recognize it and strike the bass drum; and likewise for the hi-hat/snare and clap. Our design will minimally cover 3 different drum hit types (bass hit, snare hit, clap hit), and respond with minimal latency.

This would involve amplifying the analog signal (as dynamic mics drive fairly low gain signals), which would be sampled by a dsPIC33F DSP/MCU (or comparable chipset), and processed for trigger event recognition. This entails applying Short-Time Fourier Transform analysis to provide spectral content data to our event detection algorithm (i.e. recognizing the "control" signal from the human user). The MCU functionality of the dsPIC33F would be used for relaying the trigger commands to the actuator circuits controlling the robot.

The robot in question would be small; about the size of ventriloquist dummy. The "drum set" would be scaled accordingly (think pots and pans, like a child would play with). Actuators would likely be based on solenoids, as opposed to motors.

Beyond these minimal capabilities, we would add analog prefiltering of the input audio signal, and amplification of the drum hits, as bonus features if the development and implementation process goes better than expected.

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