Table of Contents
Embedded systems often rely on sensor data to perform various functions. Acquiring and processing this data accurately is essential for system performance and reliability. This article provides practical examples of how sensor data is collected and processed in embedded applications.
Temperature Sensor Data Acquisition
Temperature sensors, such as thermistors or digital temperature sensors, are commonly used in embedded systems. Data acquisition involves reading voltage or digital signals from the sensor through Analog-to-Digital Converters (ADC) or digital interfaces like I2C or SPI. For example, a microcontroller reads the ADC value, converts it to a temperature using a calibration formula, and then uses this data for control decisions.
Accelerometer Data Processing
Accelerometers measure acceleration forces and are used in applications like motion detection and orientation. Data acquisition involves sampling the sensor’s output via digital interfaces. Processing includes filtering noise, calculating magnitude, or detecting specific movement patterns. For instance, a low-pass filter can smooth raw data to improve accuracy.
Light Sensor Data Handling
Light sensors, such as photodiodes or light-dependent resistors (LDRs), provide analog or digital signals proportional to ambient light. Data acquisition involves reading these signals periodically. Processing may include adjusting display brightness or triggering alarms when light levels exceed thresholds. Calibration ensures consistent readings across different lighting conditions.
Common Data Processing Techniques
- Filtering: Removing noise using techniques like moving average or low-pass filters.
- Calibration: Adjusting raw data to account for sensor inaccuracies.
- Scaling: Converting raw sensor readings into meaningful units.
- Threshold Detection: Triggering actions when sensor data crosses predefined limits.