Instrumentation and DAQ

Why it matters

Every physics result starts as a voltage in an instrument. Data acquisition (DAQ) is the chain that turns a physical quantity (temperature, acceleration, light, charge) into numbers in a file. Physicists design, calibrate and debug these chains constantly, and many analysis problems are really acquisition problems: the wrong sample rate, a missing timestamp, an uncalibrated sensor.

The DAQ chain

1. Sensor or transducer: converts the quantity into an electrical signal. Examples: a thermistor (resistance changes with temperature), a photodiode (current from light), a MEMS accelerometer (capacitance changes with acceleration), a photomultiplier (charge pulses from single photons).
2. Signal conditioning: amplifiers, filters and dividers that bring the signal into the converter's range and remove noise. An anti-aliasing low-pass filter removes frequencies the converter can't sample correctly.
3. Analog-to-digital converter (ADC): samples the voltage at fixed times and rounds it to an integer.
4. Timing and storage: a clock stamps every sample; software writes the data with its metadata.

Resolution

An n-bit ADC splits its input range into 2^n steps.

step size = V_range / 2^n
10-bit, 0-5 V:   5 / 1024  = 4.9 mV per count
12-bit, 0-3.3 V: 3.3 / 4096 = 0.81 mV per count
16-bit, +/-10 V: 20 / 65536 = 0.31 mV per count

A reading of 512 counts on a 10-bit, 5 V ADC is 512 * 5 / 1024 = 2.50 V. Resolution is not accuracy: an ADC with tiny steps can still be offset or noisy, which is why you calibrate against a known reference.

Sampling rate and aliasing

The Nyquist criterion says you must sample faster than twice the highest frequency in the signal. A 60 Hz signal sampled at 100 samples per second does not just look rough, it masquerades as a 40 Hz signal (100 - 60). Once aliased, no software can undo it, so you filter before the ADC and choose a sample rate well above twice the frequencies you care about (five to ten times is common).

Noise and averaging

Random noise shrinks when you average: the standard error of N independent readings is sigma / sqrt(N). Averaging does nothing for systematic errors, such as a sensor that reads 0.2 °C high; only calibration fixes those.

Phones and microcontrollers as DAQ

• Smartphone sensors are real MEMS instruments. The free phyphox app, from RWTH Aachen University (https://phyphox.org), records the accelerometer, gyroscope, magnetometer, light sensor and microphone and exports CSV files.
• Microcontrollers such as Arduino boards read sensors through built-in ADCs (analogRead returns 0 to 1023 on a 10-bit, 5 V Uno) and send data over USB serial. The free Wokwi browser simulator (https://wokwi.com) runs Arduino code with virtual sensors and potentiometers, so you can practise without hardware.

A minimal Arduino logger looks like this:

void setup() { Serial.begin(115200); }
void loop() {
  int counts = analogRead(A0);             // 0..1023
  float volts = counts * 5.0 / 1023.0;
  Serial.print(millis()); Serial.print(",");
  Serial.println(volts, 3);
  delay(10);                               // about 100 samples per second
}

Good data habits

• Record timestamps with every sample, not just a sample count.
• Save metadata: sensor model, settings, sample rate, units, date, who and where.
• Keep raw data untouched; write processed data to new files.
• Use plain, documented formats (CSV for small data, HDF5 for large arrays).

Real labs

Lab DAQ hardware, high-voltage detector supplies, lasers and cryogenics need institutional training and supervision. At home, stick to phones, simulators and battery-powered kits.

Common mistakes

• Sampling too slowly and analysing an aliased signal.
• Trusting raw counts without converting units and calibrating.
• Losing timing by assuming a perfectly steady sample rate; phones in particular vary.
• Averaging away a real signal with too long a window.

Recap

• Sensor, conditioning, ADC, timing and storage make up the chain.
• Step size = range / 2^n; resolution is not accuracy.
• Sample faster than twice the highest frequency, and filter first.
• Save timestamps, metadata and raw data.

Try it

Open the Do tab to record accelerometer data with phyphox (or a simulated sensor in Wokwi) and analyse its noise and sample rate.