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An AI assistant suggests doubling a sensor's integration time, ""then the readings will get better"". Why is this advice not enough?

  1. Because the suggestion comes from general knowledge about sensors; only confirmation by an expert makes it reliable.
  2. Because the assistant does not explain why a longer window yields calmer readings; with a sound explanation the advice would be sufficient.
  3. Because nothing but the setup itself knows how the setup behaves; a measurement decides that.
  4. Because ""better"" can only be judged once the assistant also predicts which values the sensor will deliver after the change.
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A suggestion, an explanation and a prediction are all claims, and only a measurement on the setup itself turns one of them into evidence.

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Between two measurement series, a group changes both the distance between sender and sensor and the integration time. The second series is clearly better. What do they know now?

  1. That both changes helped, otherwise the second series would not be so clearly better.
  2. That it got better, but not what caused it.
  3. That the distance was decisive, since it has the stronger physical effect.
  4. That the series is unusable and has to be repeated before anything can be said at all.

Change exactly one quantity per series, otherwise a difference cannot be put down to its cause.

Before measuring the colours of an LED with a colour sensor, you take a control measurement: readings while the LED is switched off. What for?

  1. To check whether the sensor responds at all, before time goes into the real measurements.
  2. So that the sensor adjusts to the ambient light and afterwards subtracts it from the readings by itself.
  3. To know what the sensor measures from ambient light anyway; only against that can the readings with the LED on be interpreted.
  4. To see how much the readings scatter without light; once the LED is on, the noise disappears in the signal anyway.

Every reading contains ambient light, the light of the LED and noise; the control reading measures the first part, so that the second can be read off.

You measure the same red LED five times in a row and get these red components: 198, 205, 201, 195, 206. What follows?

  1. The sensor is broken, otherwise the same value would appear five times.
  2. Readings scatter; if you need a stable value, average over several measurements.
  3. The LED slowly gets weaker while being measured.
  4. Only the last value counts, since the sensor had settled by then.

A single reading is a draw; the mean of several is stable, and the spread around it is a result of its own.

Which statements about the values of a colour sensor that reads the light of an LED are true? (Several answers may be correct.)

  1. It delivers three values, red, green and blue, matching the three lamps inside the LED.
  2. Besides red, green and blue there is a fourth, unfiltered channel (clear).
  3. The measured values do not simply match the RGB numbers the sender puts into its LED.
  4. With the LED off, all channels read zero, because without a sender no light reaches the sensor.

The sensor returns four numbers, r, g, b and c, and none of them is the number that was sent.

A sensor can read the same light with long, medium or short reading windows. To transmit faster, a group shortens the sensor's integration time drastically. What should it expect?

  1. The readings stay as calm as before and arrive more often; the window only sets how often the sensor delivers a new value.
  2. The readings get calmer because the sensor has less time to be noisy.
  3. More readings per second, but with more spread; single readings may cross into the wrong level.
  4. The readings scatter more, but the number of readings per second drops too.

Longer is calmer and shorter is faster: the integration time trades calm readings against speed.

A sender puts (255, 0, 0) into its LED, but the sensor reads r=203, g=41, b=57. How do you deal with that?

  1. Complain about the sensor: an intact sensor should read (255, 0, 0) when (255, 0, 0) is sent.
  2. Measure how each transmitted colour arrives, and match future readings to these learned patterns.
  3. Derive a conversion formula that computes the exact transmitted RGB numbers back from the readings.
  4. Turn the LED brighter until the readings match the transmitted values.

Calibrate, then classify: references of known colours, and every new reading goes to the nearest of them.

What belongs in the record of a measurement series? (Several answers may be correct.)

  1. What was changed and what stayed constant.
  2. The setup: distance, ambient light, sensor settings.
  3. All readings, including the inconvenient ones.
  4. How many times the measurement was repeated.

The record holds the conditions, the changed quantity, the number of repetitions and every value, so that someone else could repeat the series.

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A receiver is supposed to tell red from blue. In a test it is right 5 times out of 5, and someone says: ""Done, it works."" What is the catch?

  1. Nothing, five hits without a single miss are solid proof that the distinction works.
  2. Five hits can still be luck with two colours; only many repetitions separate skill from chance.
  3. The test should have used more than two colours.
  4. Five hits only show that the threshold happens to fit; what matters is how far the mean of the readings sits from the threshold.
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The chance of five hits in a row by guessing is 0.5⁵ = 1/32, the chance of fifty is 1 in 2⁵⁰.

Why do you decide before a measurement series what result you expect, instead of just measuring and then having a look?

  1. Because the sensor settings can only be chosen sensibly once it is clear beforehand which values are supposed to come out.
  2. So the series can be stopped as soon as the expected result is reached, avoiding superfluous repetitions.
  3. Without an expectation set beforehand, almost any result can be interpreted as a success afterwards.
  4. Because a record without a written expectation does not count as a measurement series, only as a collection of single values.
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The expectation written down beforehand protects you from reading your own hopes into the data afterwards.

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What makes a measurement series, as opposed to a single measurement?

  1. That the measuring is done with special care: a steady setup, a properly configured sensor, and every value written down immediately.
  2. Several measurements under equal, recorded conditions, with at most one quantity changed on purpose.
  3. As many measurements as possible at as many different settings as possible, so that every relationship can be read from the data afterwards.
  4. The same measurement at different times of day, so that random fluctuations cancel out on average.

Repetition, recorded conditions and one changed quantity turn single readings into a measurement series.

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An AI assistant suggested increasing the sensor's gain, ""that improves recognition"". Your clean measurement series shows the opposite: the recognition rate drops. What do you do? (Several answers may be correct.)

  1. Document the measurement series including its conditions.
  2. Distrust the measurement; the AI knows such sensors better than you.
  3. Briefly check that the series was clean: one quantity changed, enough repetitions, a control measurement in place.
  4. Add the case to a log of mistakes: suggestion, measurement, outcome.
  5. Keep the higher gain anyway, since a single contradicting measurement series carries less weight than a well-reasoned recommendation.
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Check your own series first; if it was clean, it outweighs the suggestion, and the case goes into the log of mistakes.