Solution: Data Retrieval and Plotting Techniques

Data Retrieval

Data retrieval means reading stored or acquired data from a microcontroller, data logger or computer for further processing and visualization.

Common methods

1. UART/Serial: Simple method for transferring measurements to a PC terminal or application.
2. USB: High-speed transfer between embedded hardware and a computer.
3. SPI/SD card: Data is retrieved from removable storage.
4. I²C: Used to read data from sensors and EEPROMs.
5. Network/Wireless: Data can be transferred remotely using appropriate communication hardware.

Typical Retrieval Flow

Sensor Data → Storage/Data Logger → Communication Interface → Computer → Analysis Software

Plotting Techniques

1. Time-Domain Plot

Plots signal amplitude against time.

Useful for temperature, heart rate, vibration and other time-varying signals.

2. XY Plot

Plots one measured variable against another, such as pressure versus temperature.

3. Frequency Spectrum

Uses techniques such as the Fast Fourier Transform (FFT) to show signal magnitude versus frequency.

Useful for vibration and periodic-signal analysis.

4. Trend Plot

Shows long-term changes such as temperature over several hours or days.

Typical Software Workflow

1. Connect to the data logger.
2. Retrieve raw samples.
3. Parse the data format.
4. Add timestamps/channel information.
5. Apply calibration and units.
6. Remove invalid values if required.
7. Plot the data.
8. Save the graph or processed dataset.

Example

For vibration data, the time-domain graph can show transient events, while an FFT plot can identify dominant vibration frequencies.

Conclusion

Data retrieval transfers stored measurements into an analysis environment, while plotting converts numerical data into visual information that makes trends, abnormalities and frequency components easier to identify.

Solution: Data Analysis Techniques in Embedded and DAQ Systems

1. Statistical Analysis

Basic statistics summarize acquired data.

Mean

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

Variance

$$\sigma^2=\frac{1}{N}\sum_{i=1}^{N}(x_i-\bar{x})^2$$

Other useful measures include minimum, maximum, range and standard deviation.

2. Filtering

Digital low-pass, high-pass, band-pass and notch filters can remove unwanted frequency components.

3. Time-Domain Analysis

Examines how the signal changes with time. Peak value, RMS value, rise time, pulse width and zero crossings may be measured.

For samples $x_i$, RMS is:

$$x_{RMS}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_i^2}$$

4. Frequency-Domain Analysis

The FFT (Fast Fourier Transform) converts sampled time-domain data into frequency-domain information. It is widely used for vibration, audio and periodic-signal analysis.

5. Trend Analysis

Long-term trends are examined to detect gradual changes, drift or degradation.

6. Threshold and Event Detection

The system can compare a measurement against predefined limits and generate an alarm or control action.

7. Correlation Analysis

Correlation measures the relationship between two signals or variables and can help identify related system behavior.

8. Feature Extraction

Important features such as mean, RMS, peak, frequency and pulse rate are extracted from raw data for classification or monitoring.

9. Calibration and Error Analysis

Measured values are compared with known references to determine offset, gain error, sensitivity and other measurement errors.

Embedded Implementation

To perform analysis on a microcontroller, efficient algorithms, fixed-point arithmetic where appropriate, buffering and interrupt/DMA-based acquisition may be used.

Conclusion

DAQ analysis combines statistical, time-domain, frequency-domain, filtering, trend, threshold, correlation and calibration techniques to convert raw measurements into useful engineering information.