Most sensors are electrical or electronic, although other types exist. A sensor is a type of transducer. Sensors are either direct indicating (e.g. a mercury thermometer or electrical meter) or are paired with an indicator (perhaps indirectly through an analog to digital converter, a computer and a display) so that the value sensed becomes human readable. Aside from other applications, sensors are heavily used in medicine, industry and robotics. Technical progress allows more and more sensors to be manufactured with MEMS technology. In most cases this offers the potential to reach a much higher sensitivity. See also MEMS sensor generations.
Classification of types
Since a signal is a form of energy, sensors can be classified according to the type of energy they detect.
electromagnetic time-of-flight. Generate an electromagnetic impulse, broadcast it, then measure the time a reflected pulse takes to return. Commonly known as - RADAR (Radio Detection And Ranging) are now accompanied by the analogous LIDAR (Light Detection And Ranging. See following line), all being electromagnetic waves. Acoustic sensors are a special case in that a pressure transducer is used to generate a compression wave in a fluid medium (air or water)
light time-of-flight. Used in modern surveying equipment, a short pulse of light is emitted and returned by a retroreflector. The return time of the pulse is proportional to the distance and is related to atmospheric density in a predictable way.
proximity sensor- A type of distance sensor but less sophisticated. Only detects a specific proximity. May be optical - combination of a photocell and LED or laser. Applications in cell phones, paper detector in photocopiers, auto power standby/shutdown mode in notebooks and other devices. May employ a magnet and a Hall effect device.
scanning laser- A narrow beam of laser light is scaned over the scene by a mirror. A photocell sensor located at an offset responds when the beam is reflected from an object to the sensor, whence the distance is calculated by triangulation.
focus. A large aperture lens may be focused by a servo system. The distance to an in-focus scene element may be determined by the lens setting.
binocular. Two images gathered on a known baseline are brought into coincidence by a system of mirrors and prisms. The adjustment is used to determine distance. Used in some cameras (called range-finder cameras) and on a larger scale in early battleship range-finder
coherent laser- interference between transmitted and reflective lightwaves are counted and the distance is calculated. Capable of extremely high precision.
acoustic: uses ultrasound time-of-flight echo return. Used in mid 20th century polaroid cameras and applied also to robotics. Even older systems like Fathometers (and fish finders) and other 'Tactical Active' SonarSound Navigation And Ranging) systems in naval applications which mostly use audible sound frequencies.
distance sensor (noncontacting) Several technologies can be applied to sense distance:
Non Initialized systems
Gray code strip or wheel- a number of photodetectors can sense a pattern, creating a binary number. The gray code is a mutated pattern that ensures that only one bit of information changes with each measured step, thus avoiding ambiguities.
Initialized systems
These require starting from a known distance and accumulate incremental changes in measurements.
Quadrature wheel- An disk-shaped optical mask is driven by a gear train. Two photocells detecting light passing through the mask can determine a partial revolution of the mask and the direction of that rotation.
whisker sensor- A type of touch sensor and proximity sensor.
Classification of measurement errors
A good sensor applies to the following rules:
the sensor should be sensitive to the measured property
the sensor should be insensitive to any other property
the sensor should not influence the measured property
In the ideal situation, the output signal of a sensor is exactly proportional to the value of the measured property. The gain is then defined as the ratio between output signal and measured property. For example, if a sensor measures temperature and has a voltage output, the gain is a constant with the unit V/K.
If the sensor is not ideal, several types of deviations can be observed:
The gain may in practice differ from the value specified. This is called a gain error.
Since the range of the output signal is always limited, the output signal will eventually clip when the measured property exceeds the limits. The full scale range defines the outmost values of the measured property where the sensor errors are within the specified range.
If the output signal is not zero when the measured property is zero, the sensor has an offset or bias. This is defined as the output of the sensor at zero input.
If the gain is not constant, this is called nonlinearity. Usually this is defined by the amount the output differs from ideal behaviour over the full range of the sensor, often noted as a percentage of the full range.
If the deviation is caused by a rapid change of the measured property over time, there is a dynamic error. Often, this behaviour is described with a bode plot showing gain error and phase shift as function of the frequency of a periodic input signal.
If the output signal slowly changes independent of the measured property, this is defined as drift.
Long term drift usually indicates a slow degradation of sensor properties over a long period of time.
Noise is a random deviation of the signal that varies in time.
Hysteresis is an error caused by the fact that the sensor not instantly follows the change of the property being measured, and therefore involves the history of the measured property.
If the sensor has a digital output, the signal is discrete and is essentially an approximation of the measured property. The approximation error is also called digitization error.
If the signal is monitored digitally, limitation of the sampling frequency also causes a dynamic error.
The sensor may to some extent be sensitive for other properties than the property being measured. For example, most sensors are influenced by the temperature of their environment.
All these deviations can be classified as systematic errors or random errors. Systematic errors can sometimes be compensated for by means of some kind of calibration strategy. Noise is a random error that can be reduced by signal processing, such as filtering, usually at the expense of the dynamic behaviour of the sensor.
Biological sensors
All living organisms contain biological sensors with functions similar to those of the mechanical devices described. Most of these are specialized cells that are sensitive to:
Overview of Sensors and Needs for Environmental Monitoring Clifford K. Ho, Alex Robinson, David R. Miller and Mary J. Davis Sensors 2005, 5, 4-37 1 (open access) article
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