Sensor can simply be defined as a device that receives and respond to stimulus or signal, you can also say, it is a device that receives a stimulus and respond with an electric signal.
This definition is broad. In fact, it is so broad that it covers almost everything from a human eye to a trigger in a pistol.

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The stimulus (also called measurand) is the quantity, property, or condition that is sensed and converted into electrical signal. The purpose of a sensor is to respond to some kind of change in a physical stimulus and to convert it into an electrical signal which is compatible with electronic circuits. We may say that a sensor is a translator of a generally nonelectrical value into an electrical value.

When we say “electrical,” we mean a signal which can be channelled, amplified, and modified by electronic devices. The sensor’s output signal may be in the form of voltage, current, or charge. These may be further described in terms of amplitude, frequency, phase, or digital code. This set of characteristics is called the output signal format. Therefore, a sensor has input properties (of any kind) and electrical output properties.

Sensor classification schemes range from very simple to the complex. Depending on the classification purpose, different classification criteria may be selected. However, all sensors could either be active or passive. An active sensor requires an external excitation signal/power which is modified by the sensor to produce its output signal. Whereas, a passive sensor generates its output signal directly in response to the measurand (does not require any additional energy source.

The examples are a thermocouple, a photodiode, and a piezoelectric sensor. Most of passive sensors are direct sensors)
Eyes detect light energy, ears detect acoustic energy, a tongue and a nose detect certain chemicals, and skin detects pressures and temperatures.

The eyes, ears, tongue, nose, and skin receive these signals then send messages to the brain which outputs a response. For example, when you touch a hot plate, it is your brain that tells you it is hot, not your skin.

TRANSDUCER AND ACTUATOR

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Just like a sensor, a transducer is an energy converter. However, the term sensor should be distinguished from transducer. While a transducer is a converter of one type of energy into another, a sensor converts any type of energy into electrical. It is more than just a sensor; it is a sensor plus signal conditioning.

When the output of the transducer is converted to a readable format, the transducer is called a sensor. An example of a transducer is a loudspeaker which converts an electrical signal into a variable magnetic field and, subsequently, into acoustic waves. This is nothing to do with perception or sensing.

Transducers may be used as actuators in various systems. An actuator may be described as opposite to a sensor—it converts electrical signal into generally nonelectrical energy. If sensors provide the “eyes” of control, then actuators provide the “muscle” For example, an electric

motor is an actuator—it converts electric energy into mechanical energy. Also, actuators have been used for years in automobile airbags. These actuators actuate the airbag after a crash has been sensed.
On the basis of the above, a transducer can be classified into an input transducer (sensor) or an output transducer (actuator)

TRANSDUCER SPECIFICATIONS/CHARACTERISTICS

In reality, Transducers or measurement systems function with some form of imperfection. It is imperative to know the capability and limitation of a transducer or measurement system to properly assess its performance. There are a number of performance related parameters of a transducer or measurement system (such as what it measures (stimulus), what its specifications are, what physical phenomenon it is sensitive to, what conversion mechanism is employed, what material it is fabricated from, and what its field of application is).

These parameters, often called sensor specifications or characteristics, informs the user about deviations from the ideal behaviour of the sensors.

Also, the transfer function of a transducer/sensor system says quite a lot about the system in terms of its relationship between its output signal and its measurand. Since measurement systems are not perfect, an ideal and “reality” output to stimuli relationship would exist for every sensor.

This relationship is usually characterised by the transfer function. This function may be a simple linear or a nonlinear connection, (e.g., logarithmic, exponential, or power function)

Following are the various specifications of a sensor/transducer system

Range/Span

Range -lowest and highest values of the stimulus. e.g, a thermocouple for the measurement of temperature might be have a range of 20-220 °C

Span (input full scale, IFS) - the arithmetic difference between the highest and lowest values of the input that being sensed. It represents the highest possible input value that can be applied to the sensor without causing an unacceptably large inaccuracy. Thus, above thermocouple has a span of 200 C.

Output full scale (OFC) - difference between the upper and lower ranges of the output of the sensor. Let’s say the above thermocouple was designed to have its output as 3.0v-1.5v, its OFC is 1.5v.

Errors

This is one of the most important characteristics of a sensor. This is measured value –true value. E.g. a linear displacement sensor ideally should generate 1 mV per 1-mm displacement; but after experiment, the sensor gave a displacement reading of 26.4 mm, when the actual displacement had been 26 mm, then the error is 0.4 mm.
Errors are usually systematic or random. Systematic errors are usually initiated from the measuring instrument or process. Arise from,

• Interfering or modifying variables (e.g, temperature) - Drift (e.g, changes in chemical structure or mechanical stresses)

• The measurement process changes the measurand (e.g, loading errors) - The transmission process changes the signal (e.g, attenuation, reduction in amplitude of a wave or the strength of a signal) - Human observers (e.g, parallax errors)
  Systematic Errors can be corrected by compensation methods e.g filtering (removal of unwanted signal)
  While random errors are caused by unpredictable environmental conditions. E.g

• NOISE: a signal that carries no useful information. Sources include, environmental noise (e.g, background noise picked by a microphone), Transmission noise (e.g, 60Hz hum)
  Other errors could arise from material variations, imperfection of materials/design errors, workmanship, manufacturing tolerances, etc.

Accuracy/Inaccuracy

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It is the closeness of the agreement between the actual measurement result and a true value of the measurand. Usually expressed as a percentage of the input span.
In the above example, where the displacement sensor having an absolute error of 0.4mm, the inaccuracy is (0.4mm/26mm) * 100 = 1.54%. Thus we say that over a range of 26mm, the sensor’s inaccuracy is 1.54%.
If we repeat this experiment over and over again without any random error and every time we observe an error of 0.4mm, we may say that the sensor has a systematic inaccuracy of 0.4mm over a 26-mm span. A random component is usually present in reality, thus, the systematic inaccuracy/error may be represented as an average or mean value of multiple errors.

Inaccuracy rating, could also be expressed in terms of its output signal. A pressure sensor has a 100kPa input full scale and a 10v full-scale output. Its inaccuracy may be specified as ±0.5%, ±500 Pa, or ±0.05v.
For example, let us consider a stimulus having value x. ideally, we would expect this value to correspond to point z on the transfer function, resulting in the output value Y. Instead, the real function will respond at point Z, producing output value Y’. This output value corresponds to point z’ on the ideal transfer function, which, in turn, relates to a “would-be” input stimulus x’ whose value is smaller than x. Thus, in this example, imperfection in the sensor’s transfer function leads to a measurement error of−δ.

Calibration

Calibration means the determination of specific variables that describe the overall (entire circuit- sensor, signal conditioning/processing, and A/D converter) transfer function. For example, we need to measure temperature with an accuracy ±0.5◦C; however, an available sensor is rated as having an accuracy of ±1◦C. This can be done but the sensor needs to be calibrated; that is, its individual transfer function needs to be found during calibration. The mathematical model of the transfer function should be known before calibration.

Calibration is sometimes an operational requirement and the Calibration data is usually supplied by the manufacturer. Thus, the Calibration procedures are usually included with the design documents. Errors due to calibration must be evaluated and specified.

Sensitivity

Sensitivity of a sensor is defined as the ratio of change in output value of a sensor to the unity change in input value that causes the output change. For instance, the sensitivity of the displacement sensor in the error subsection above is = 1mV/mm. It is measured as the slope of the transfer function and it is also used to indicate sensitivity to other environment that is not measured.
For example, a motion detector for a security system should be sensitive to movement of humans and not responsive to movement of smaller animals, like dogs and cats.

Hysteresis

This the effect of direction of the input on the output. A hysteresis error the maximum deviation of the sensor’s output at a specified point of the input signal when it is approached from the opposite directions (Fig. 2.4). For example, a displacement sensor when the object moves from left to right at a certain point produces a voltage which differs by 20 mV from that when the object moves from right to left.

If the sensitivity of the sensor is 10mV/mm, the hysteresis error in terms of displacement units is 2mm. Common causes for hysteresis are friction, magnetization, structural changes in the materials, etc.

Resolution

This is the minimum input (of a physical parameter) that will create a detectable output. That is, the smallest change in the stimulus that can be sensed by the sensor. For example, a digital voltmeter with resolution of 0.1V is used to measure the output of a sensor.
The change in input (stimulus) that will provide a change of 0.1V on the voltmeter is the resolution of the sensor/voltmeter system.

Also, Resolution could also be expressed as a percent of Input full scale (IFS). E.g. an angular sensor has an IFS of 270 degree, and its 0.5 degree resolution would be specified as 0.181% IFS. i.e (0.5/270) * 100.
Note that a high resolution does not necessarily imply a high accuracy (a watch may resolve to the nearest second, while it may be a few minutes off)

Repeatability

A repeatability (reproducibility) is an error caused by the inability of a sensor to represent the same value under similar conditions. It is expressed as the maximum difference between output readings as determined by two calibrating cycles, unless otherwise specified. It is usually represented as % of the output full scale: = (Δ /FS)×100%.
Possible sources of the repeatability error may be design error, thermal noise, build up charge, elasticity of material, etc.

Reliability

This is a statistical measure of quality of a device. It indicates the ability of a sensor to perform a required function under stated conditions for a stated period. It specifies a failure or error, either temporary or permanent, exceeding the limits of a sensor’s performance under normal operating conditions. Ought to be provided by the manufacturer and it’s usually based on accelerated lifetime testing.

Dead band/time

The dead band or dead space of a transducer is the range of input values for which there is no output. The dead time of a sensor device is the time duration from the application of an input until the output begins to respond or change.

Cost, size, weight

Cost, weight, and overall sizes are geared to the required areas of applications. Cost may be a minor issue when the sensor’s reliability and accuracy are of paramount importance. For example, if a sensor is intended for life-support equipment and aircrafts, a high price tag may be well justified to assure high accuracy and reliability. However, for a very broad range of consumer applications, the price of a sensor is often the bedrock of a design.

Uncertainty

There are hardly any perfections in this world. All materials are not exactly as we think they are. Our knowledge of even the purest of the materials is always approximate; machines are not perfect and never produce perfectly identical parts according to drawings. Any measurement system consists of many components, including sensors.

Thus, no matter how accurate the measurement is, we simply never can be 100% sure of the measured value. The result of a measurement should be considered complete only when accompanied by a quantitative statement of its uncertainty.

An error can be compensated to a certain degree by correcting its systematic component. The result of such a correction can unknowably be very close to the unknown true value of the stimulus and, thus, it will have a very small error. Yet, in spite of a small error, the uncertainty of measurement may be very large so we cannot really trust that the error is indeed that small.

Common sources of uncertainty include human errors, environmental conditions (temperature, humidity, atmospheric pressure, power supply variations, etc.), measured errors (e.g. amplifier noise, sensor aging, thermal loss through connecting wires, dynamic error due to sensor’s inertia, temperature instability of object of measurement, etc) and ambient drifts (e.g. bridge resistors, digital resolution).

The word “uncertainty” by its very nature implies that the uncertainty of the result of a measurement is an estimate and generally does not have well-defined limits.

We will be drawing the curtains here, in the next class we will be talking about THE TYPES OF SENSOR AND TRANSDUCER.

REFERENCES

• Dunn, William C. “Introduction to instrumentation, sensors, and process control”

• Jacob Fraden, “Handbook of modern sensors: physics, designs, and applications / 3rd ed.”

• Kato, H., Kojima, M., Gattoh, M., Okumura,Y., and Morinaga, S. “Photoelectric inclination sensor and its application to the measurement of the shapes of 3-D objects.” IEEE Trans. Instrum. Meas. 40(6), 1021–1026, 1991.

• Barker, M. J. and Colclough, M. S. “A two-dimensional capacitive position transducer with rotation output.” Rev. Sci. Instrum., 68(8), 3238–3240, 1997.

• Peters, R.D. U.S. Patent 5,461,319, 1995. - De Silva, C. W. “Control Sensors and Actuators,” Prentice-Hall, Englewood Cliffs, NJ, 1989.

• Efedua J. Eziashi. “Electronic Instrumentation Principles”, 2004. - Clifford,M. A., “Accelerometers Jump into the Consumer Goods Market, ”Sensors Magazine, Vol. 21, No. 8, August 2004.

• Massa, D. P., “Choosing an Ultrasonic Sensor for Proximity or Distance Measurement,” Sensors Magazine, Vol. 16, No. 2, February 1999.

• Young, W. C., Roark’s Formulas for Stress and Strain, 6th ed., McGraw-Hill.

• Shigley, J., Mechanical Engineering Design, McGraw-Hill, 1963, pp. 284–289.

• Nagy, M. L., C. Spanius, and J. W. Siekkinen, “A User Friendly High Sensitivity Strain Gauge,” Sensors Magazine, Vol. 18, No. 6, June 2001.

• Sheingold, D. H., ed. Analog–Digital Conversion Handbook, 3rd ed. Prentice Hall, Englewood Cliffs, NJ, 1986. - Banerjee, B., “The 1 Msps Successive Approximation Register A/D Converter,” Sensors Magazine, Vol. 18, No. 12, December 2001.

• Tokheim, R. L., Digital Electronics Principles and Applications, 6th ed., Glencoe/ McGrawHill, 2003, pp. 77–113.

• Gerald Luecke, “Analog and digital circuits for electronic control system applications: using the TI MSP430 microcontroller”, 2004.

Thanks a lot for coming around, if you find this class interesting do well to resteem this post, all questions will be joyfully answered in the comment session.