ADC & Sensor Interfacing Notes

Analog Signals

• Continuous-time signals that represent physical parameters.
• Values vary smoothly and infinitely over time.
• Examples: temperature, light, sound.
• Core characteristics: continuity, amplitude range, time-varying, smooth curves.

Analog vs Digital Signals

• Analog: Continuous, infinite values, varying voltage, prone to noise.
• Digital: Discrete, finite values (0 and 1), noise-resistant, compatible with digital logic.
• Analog signals require ADC for processing in microcontrollers.

ADC (Analog-to-Digital Converter)

• Translates continuous analog voltage into discrete digital value.
• Physical quantity (e.g., temperature, light) → Binary number
• Arduino Uno uses analogRead() for this conversion.

ADC Block Diagram

• Sampler: Takes samples from the continuous analog signal according to its sample frequency, converting the continuous-time-continuous amplitude signal into a continuous amplitude-discrete time signal.
• Holding Circuit: Holds the samples generated by the sampler circuit.
• Quantizer: Converts the continuous amplitude-discrete time signal into a discrete time-discrete amplitude signal.
• Encoder: Generates the digital signal in binary form.

ADC in Arduino Uno (ATmega328P)

• Resolution: 10-bit (0 to 1023)
• Voltage Range: 0V to $V_{ref}$ (usually 5V)
• Number of Channels: 6 analog input pins (A0–A5)
• Default Reference: 5V (from USB or external supply)
• Internal Reference: 1.1V (optional via analogReference(INTERNAL))

Internal Reference Voltage (Vref)

• Fixed, stable voltage inside the microcontroller.
• Used as the upper limit for ADC conversions.
• Using 1.1V internal reference provides more stable and precise ADC results for low-voltage sensors.

Available Reference Options on Arduino Uno

• DEFAULT: ~5.0V (Vcc)
• INTERNAL: 1.1V (built-in)
• EXTERNAL: Custom voltage applied to AREF pin (< 5V)

Basic Conversion Process

1. Sensor produces analog voltage.
2. ADC samples this voltage.
3. Converts it to a digital number based on reference voltage and resolution.
   With 10-bit ADC & 5V reference.

Example Code

• Reading analog input:

  int rawValue = analogRead(A0);
  float voltage = rawValue * (5.0 / 1023.0);

• Using internal reference:

  analogReference(INTERNAL); // Set ADC reference to 1.1V
  float voltage = sensorValue * (1.1 / 1023.0); // Use 1.1V as reference

Resolution in ADC

• Smallest measurable change in analog input that can be detected by the ADC.
• Formula: $Resolution = \frac{V_{ref}}{2^n}$
  • $V_{ref}$ = Reference Voltage
  • n = bit depth of the ADC (10 for Arduino Uno)

Resolution Calculation for Arduino Uno (10-bit ADC)

• ADC range: 0 to 1023 ($2^{10} - 1$)
• Reference Voltage ($V_{ref}$): 5V (default)
• Formula: $Resolution = \frac{5V}{1024} \approx 0.0049V$

Sampling Rate in ADC

• Number of times per second the analog signal is sampled.
• Measured in samples per second (SPS) or Hertz (Hz).
• Arduino Uno: Default sampling rate for analogRead() is approximately 9.6 kHz.

Calculation of Arduino Sampling Rate

• ADC Clock: Derived from the system clock (16 MHz) and divided by a prescaler.
• Formula: Sampling Rate = ADC Clock / Conversion Time.
• Arduino Uno (16 MHz clock and default prescaler of 128): ≈ 9.6 kHz.

Why is Sampling Rate Important?

• Higher Sampling Rate = More Accurate Representation.
• Trade-Offs: Higher sampling rate → more power consumption, processing load.
• Nyquist Theorem: Ensure the signal you're measuring is below half of the sampling rate to avoid aliasing.

Optimizing Sampling Rate in Arduino

• Reduce the delay between readings.
• Use analogRead() with lower resolution.
• Consider using direct register access.

Improving Accuracy in ADC Readings

• Raw analog readings can fluctuate due to electrical noise, sensor instability, and rapid environmental changes.
• Techniques: Averaging (Smoothing), Median Filtering, Software Debouncing, Shielding/Grounding, Low-pass Filtering.

Common Accuracy-Improving Techniques

• Averaging (Smoothing): Reduces noise from erratic ADC readings.
• Median Filtering: Remove outliers by choosing the middle value.

Rolling Average (Moving Average Filter)

• Keeps a fixed number of past readings, adds the new one, removes the oldest, and averages the rest.

Exponential Smoothing (Low-Pass Filter)

• Blends the previous smoothed value and the new sensor reading using a weighting factor (alpha).
• Formula: $smoothedValue = alpha * raw + (1 - alpha) * previousSmoothedValue$

Rolling vs. Exponential Smoothing

• Rolling Average: Higher memory usage, slower reaction to changes, best for stable, noise-heavy data, moderate complexity.
• Exponential Smoothing: Very low memory usage, faster reaction to changes, best for real-time feedback with smoothness, simple complexity.