Solution: Complete Data Processing Cycle

Definition

Data processing converts raw acquired samples into meaningful information that can be stored, displayed, transmitted or used for control.

Processing Cycle

Data Acquisition
      ↓
Data Validation
      ↓
Pre-processing / Filtering
      ↓
Calibration & Scaling
      ↓
Feature Extraction / Computation
      ↓
Analysis
      ↓
Storage / Display / Communication / Control

1. Data Acquisition

Sensors and ADCs generate digital samples at a selected sampling rate.

2. Data Validation

The system checks for missing, out-of-range or corrupted values.

3. Pre-processing

Noise may be reduced using averaging or digital filters. Signals can also be normalized or resampled when appropriate.

4. Calibration and Scaling

ADC codes are converted into engineering units such as °C, V, Pa or rpm.

5. Feature Extraction

Useful quantities are calculated from raw samples, such as peak, RMS, mean, frequency or heart rate.

For a set of $N$ samples $x_i$, the mean is:

$$\bar{x}=\frac{1}{N}\sum_{i=1}^{N}x_i$$

6. Analysis

Statistical, frequency-domain or trend analysis can be applied depending on the application.

7. Storage and Output

Processed or raw data can be saved to flash, EEPROM, SD card or a computer and displayed or transmitted.

Real-Time Considerations

The processing time must be sufficiently small compared with the sampling interval. Buffering and interrupts/DMA can help prevent sample loss.

Conclusion

A complete data-processing cycle transforms raw sensor samples into reliable, useful information through validation, filtering, calibration, computation and analysis.