Sensors (Week 3) Flashcards
Transducers
A decive that converts one form of energy or signal into another one (same of different form)
For example (Temperature -> voltage)
So it takes input energy and turns it into output energy, often in a different form.
These include sensors, and actuators
Sensor
A sensor is an input device. It detects the changes in the physical environment (like temp, light, pressure, etc). It converts these changes into an electrical signal that the usstem can read.
Example:
A temperature sensor converts heat into a voltage
microprocessor or microcontroller (like an Arduino or ESP32) can’t directly “feel” temperature — it only understands electrical signals, like voltages or digital values (0s and 1s).
Actuator
This is an output device. It takes electrical signal from the system and turns it into a physical action.
Example: A motor converts electrical signal into motion
Six types of signals:
- Radiant (Radio waves, visable light, infared)
- Mechanical(Motion and forces)
- Thermal (Kinetic energy of atoms and molecules - temp, heat flow, conduction)
- Electrical (current voltage resistance)
- Magnetic (Magnetic flux, field strength)
- Chemical (pH value, chemical composition)
Transducer Signal: Analog
The signal’s voltage or current directly matches the real-world measurement.
Transducer Signal: Digital
A signal that represents data using patterns or timing, not direct voltage levels.
Often involves frequency, pulses, or timing intervals.
Example:
The faster a pulse is sent, the brighter the light it represents.
Transducer Signal: Coded Digital
A signal that represents a value using binary code (0s and 1s).
It’s often sent as a parallel or serial digital message.
Example:
25°C might be sent as 00011001 (which is 25 in binary, using 8 bits).
Static charateristics
These describe how the system performs when the input stays constant (steady-state).
It tells you how accurate, precise, and stable the system is under stable conditions.
Dynamic Charateristics
These describe the system’s behavior when the input changes over time.
Focuses on the transition — what happens between the input change and the system’s response stabilizing.
Accuracy and uncertainty
Accuracy: Its a meadure of how closely a measured value agrees with its true value.
- its the summation of all possible errors (Bias+ precision)
Uncertainity: An estimate of the limits of error in the measurements.
Bias Error (systematic/ fixed error)
These are consistent and repeatable because it happens everytime in the same way (the measurement is always off by the same amount)
Causes of Bias Error:
- Calibration Error (System is not properly adjusted to match the true value)—- TEmp sensor always reads 2 degrees to high
-Loading error (The sensor interferears with what ist measuring)—– Sensor touches the object and changes its temperature slightly
-External Variables (Other factors affecting the measurement that are not the main thing being measured.)
Range and span
Range: Defines the limits betweens which the input can vary
Span: Max value-min value
ex.
range=-3 to 3
span =6
Precision Error (random error)
These are random errors. Unpreditable and incsistenet. Each measurement may be slightly different even if your measuring the same thing.
Causes:
- Flcutuations inside the sensor
- environmental interferarence
-imperfections in the system design (The system the sensor is integrated into)
Percision
It’s about the “sharpness” or repeatability of a measurement.
If you measure the same thing multiple times and get very similar values each time, that means the system is precise.
Precision ≠ Accuracy
A measurement can be precise but wrong (consistently off due to calibration).
Sensitivity (Scale factor)
which tells us how strongly a sensor reacts to changes in input.
- Ratio of the change in input to the change in output
-Usually indicates sensitivity to inputs other than those being measured. (i.e., impact of environmental changes)
-Important for where detecting small changes are require
Hysteresis
Hysteresis is when a system gives different outputs for the same input, depending on whether the input is increasing or decreasing.
When input is increasing, output follows the upper curve.
When input is decreasing, output follows the lower curve.
The gap between the two is the hysteresis error.
Example:
You’re measuring temperature, and your input is going up:
At 50°C, the sensor gives an output of, say, 5V.
Now, you’re cooling it down:
At 50°C again (coming down this time), the sensor now gives 4.8V instead of 5V.
Resolution
Resolution is the smallest change in input that the system can detect and reflect in the output.
Resolution must be at least as good as the required precision — otherwise your data isn’t meaningful.
Imagine a super-sharp kitchen scale that gives readings like:
5.000g
5.001g
5.002g
But if your display can only show full grams, you’d just see:
5g
5g
5g
➡️ That means your precision is high, but your resolution is too low, so it’s useless.
Threshold? Explain what the dead zone is?
The smallest input change (starting from zero) that causes a noticeable change in the output.
If the input changes a tiny bit, and the output doesn’t react yet —
➡️ you’re below the threshold.
Once you cross the threshold —
➡️ the system starts responding.
Dead Zone- range of inputs where the output stays completely frozen at zero (no reaction at all)
Reaons for dead zone:
- Your in between the threshold for positive and negative input changes
-Could be static friction, or hysteresis, etc.
Repeatability
Able to produce the same output for repeated applications of the input value.
-Same measurement process and environment
-same physical variable conditions every time
Repeatability is the closeness of the repeated measurements
Stability? What is drifting?
The ability of a sensor to give the same output when used to measure a constant input over a period of time.
If the sensor output drifts even though nothing has changed in the environment, that means the sensor is unstable.
Drift: The output shifts over time even though the input is constant
Zero drift: It doesn’t shift at all
Cause:
- Enviormental factors
-Internal sensor changes (Components inside the sensor age/ behave differently over time_)
Linearity
Linearity is a measure of how steadily a sensors output increases as the input increases.
If the output changes in equal steps for every equal change in input its linear behavior (same slop, so straight line)
If its non linear it wont change at a consistent rate.
Nonlinearity error
Nonlinearity error used to describe difference from a straight- line response
How it looks:
You have a curved graph then either you draw a:
-End to end straight line
-line of best fit
-best straight line through zero
and the vertical spacing in between these two graphs is the percent error
Input/Output impedances (comment on low output impedance and high input impedance)
When you’re connecting a sensor (output) to a device that reads the signal (input), the goal is to make sure the signal doesn’t get messed up or weakened.
This all depends on impedance, which is kind of like “resistance to signal flow.”
Low output impedance (FROM the sensor)- Want the sensor to have a low impedance so that it can send current easily, it wont get affected much by what its connected to and the signal stays strong. Helps to minimize signal loss and distortion.
High input impedance (TO the receiving system)- you want the device reading the signal (like a microcontroller) to have high input impedance. That means it will listen without interfearing.The sensor’s reading stays accurate — because the input device isn’t “pulling” on the signal too hard.
Impedance matching
Some systems may require impedance matching. This is when the input and output impedance are equal. This helps transfer maximum power, and ensure accurate signal transmission between sensor and system.
