Project

# Title Team Members TA Documents Sponsor
50 Urban Noise Pollution Monitoring System
 Cj Kompare
Cornell Horne
Marc Rhymes
 Surya Vasanth design_document2.pdf
final_paper1.pdf
photo1.png
photo2.png
presentation1.pdf
proposal2.pdf
video
# Urban Noise pollution Monitoring system

Team Members:
- CJ Kompare (kompare3)
- Cornell Horne (chorne7)
- Marc Rhymes (mrhymes2)



# Problem:
Cities face escalating issues related to noise pollution, affecting the well-being of residents and the environment. Traditional methods of noise monitoring lack granularity and real-time adaptability, hindering effective intervention strategies.

# Solution:
Develop a comprehensive Urban Noise Pollution Monitoring System that employs wireless, battery-powered microphones strategically placed outdoors. This system will utilize a concentrator or gateway to collect and process data from distributed microphones, providing accurate and real-time noise pollution insights for urban planning and environmental conservation.

# Solution Components:

- Wireless, Battery-Powered Microphones
- Concentrator/Gateway Device
- Centralized Data Processing Platform
- Geographic Information System (GIS)
- User Interface (Web Application)

# Subsystem 1: Wireless, Battery-Powered Microphones:
Deploy multiple wireless, battery-powered microphones an area to capture diverse noise sources. Ensure these microphones are durable, weather-resistant, and equipped with noise level sensing capabilities.

# Subsystem 2: Concentrator/Gateway Device:
Implement a concentrator or gateway device to receive, aggregate, and forward data from all distributed microphones. This device will serve as the central hub for data collection and transmission.

# Subsystem 3: Centralized Data Processing Platform:
Develop a centralized platform for processing and analyzing noise data received from the concentrator. This platform will perform real-time noise level calculations, identify patterns, and store historical data for future analysis.

# Subsystem 4: Geographic Information System (GIS):
Integrate a GIS component to map noise levels spatially, allowing for visual representations of noise distribution across the city. This would enhance and support targeted noise reduction initiatives.

# Subsystem 5: User Interface (Web Application):
Develop a web application for users to visualize noise data. The interface should provide real-time updates, historical trends, and customizable features for specific areas of interest.

# Criteria for Success:

Hourly Data Reporting: The system should successfully report noise data to the central web application every hour, providing a consistent and reliable stream of information for analysis and decision-making.

Real-time Monitoring: Achieve real-time noise level monitoring with a latency of no more than 5 minutes, ensuring users have timely access to critical noise pollution information.

Accuracy of Noise Identification: Ensure an accuracy rate of at least 90% in identifying noise sources, allowing for precise insights into the types and sources of noise affecting urban areas.

Low Cost Myoelectric Prosthetic Hand

Michael Fatina, Jonathan Pan-Doh, Edward Wu

Low Cost Myoelectric Prosthetic Hand

Featured Project

According to the WHO, 80% of amputees are in developing nations, and less than 3% of that 80% have access to rehabilitative care. In a study by Heidi Witteveen, “the lack of sensory feedback was indicated as one of the major factors of prosthesis abandonment.” A low cost myoelectric prosthetic hand interfaced with a sensory substitution system returns functionality, increases the availability to amputees, and provides users with sensory feedback.

We will work with Aadeel Akhtar to develop a new iteration of his open source, low cost, myoelectric prosthetic hand. The current revision uses eight EMG channels, with sensors placed on the residual limb. A microcontroller communicates with an ADC, runs a classifier to determine the user’s type of grip, and controls motors in the hand achieving desired grips at predetermined velocities.

As requested by Aadeel, the socket and hand will operate independently using separate microcontrollers and interface with each other, providing modularity and customizability. The microcontroller in the socket will interface with the ADC and run the grip classifier, which will be expanded so finger velocities correspond to the amplitude of the user’s muscle activity. The hand microcontroller controls the motors and receives grip and velocity commands. Contact reflexes will be added via pressure sensors in fingertips, adjusting grip strength and velocity. The hand microcontroller will interface with existing sensory substitution systems using the pressure sensors. A PCB with a custom motor controller will fit inside the palm of the hand, and interface with the hand microcontroller.

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