Summary:
Project Title:
Indoor Environmental Sensing and Trend Modeling
Using DSP Algorithms
Application-Specific Focus:
This system is designed specifically for indoor spaces such as homes, offices, classrooms, or
labs. It helps in monitoring and understanding how indoor conditions (temperature and
humidity) evolve over time — which is important for comfort, energy efficiency, and health.
Classroom environment tracking: Monitoring air quality, temperature, and humidity in
classrooms to maintain an optimal learning environment, ensuring comfort and enhancing
focus for students.
Temperature + Humidity Sensors Used:
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LM35 = for accurate indoor temperature
DHT22 =for digital humidity readings
(We can use DHT22 alone, but using LM35 + DHT22 shows modularity)
Both sensors provide time-series signals for DSP analysis.
DSP Techniques:
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Signal Smoothing (e.g., moving average filter)
Noise Reduction
Sliding Window Analysis
Trend Detection (detecting increase/decrease/stable patterns in temperature and
humidity over time)
How It Works:
1. Sensors collect indoor data every second.
2. Arduino sends the data to MATLAB.
3. MATLAB filters the signal and removes noise.
4. A real-time graph updates and shows whether the room is getting hotter/colder or
more/less humid.
5. The system labels trends (e.g., "Temperature Rising", "Humidity Falling").
How Is DSP Being Applied In The Project:
In this project we will be:
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Collecting and processing digital time-series signals
Using filtering and trend detection algorithms
Displaying/analyzing the results in real time
This checks all boxes for a DSP project, tailored for an indoor monitoring application.