Data Acquisition:
The term Data Acquisition in general refers to the gathering of data relating to signals, processes or experiments which provide information on real-world physical events. Usually, it is implied that this data can be manipulated by computers to obtain some key information about the events.
Data Conversion:
The term Data Conversion normally refers to the conversion of data from continuous analogue form to discrete digital form or vice-versa.
This is usually an intrinsic part of the Data Acquisition process and results in data being stored in binary coded form in some medium.
Analogue Signal:
An analogue signal is one which is a continuous function of some variable. It may have any value within a specified range. Very often the independent variable is time so that the signal is observed as a function of time. The essential feature is that the signal may have any of an infinite number of values within its range and therefore in theory has infinite resolution.
Digital Signal:
A digital signal is one which is quantised and can only have a limited number of values within a given range. It is normally thought of as being in numerical format and often in binary coded form. The essential feature is that the signal may have only one of a finite number of values within a range.
Noise:
By noise is meant a random signal the value of which we cannot predict with absolute certainty at any point in time and which does not normally contain useful information. It is an intrinsic part of nature and normally appears superimposed on useful signals which we want to measure to obtain information from them. It can only be characterised in statistical terms of probability.
1

Fig. 1.1 Speed vs Torque Characteristic of a Motor.
This is a continuous plot of one variable against another. The set of the corresponding values (co-ordinates) is infinite.
2
Fig. 1.2 Pressure Head vs Flow
Rate of Pump
This shows the maximum head of pressure available from a water pump for a given required volume flow rate. It can be used to determine the maximum depth of well the pump can be used with.
Fig. 1.3 Fuel Consumption vs
Engine Speed
This shows efficiency of the engine in terms of mileage per gallon as a function of speed.

Analogue signals which are functions of time:
Fig. 1.4 Amplitude Modulated
Radio Wave
Fig. 1.5 Sample
Speech Waveform
Fig. 1.6 Human
Electrocardiogram
(ECG)
P-Wave
Duration
P-Q
Interval
P-Q
Segment
Q-T Interval
QRS
Duration
S-T
Segment
T-Wave
Duration
3

Energy
(eV)
Fig. 1.7 Time on a Digital Clock
Time can only have a value that is an exact number of minutes. Values in between are not accounted for. This means that the time in only exactly correct at 1,440 instants in the day.
M (4)
L (8)
K (2)
Fig. 1.8 Atomic
Energy Levels
Electrons in atoms such as Silicon can only have energies at specific values, i.e. their energy levels are quantised.
Fig. 1.9 Height on a Ladder
The occupant can only stand at heights where there are rungs on the ladder. Height is quantised.
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Digital Signals as Functions of Time:
The signal may take on one of the finite number of quantised values, which may change with time in an irregular or regular manner.
Irregular variation
Heart
Rate
(bpm)
200 –
150 –
100 –
50 –
0 --
0 1 2 3 4 5 6
Time (s)
Regular variation
Sampled
Paramete r
0 1 2 3 4 5 6 7 8 9 Time (ms)
10 10
Fig. 1.10 Digital Signals in Time
Binary Digital Signals:
A binary signal is one which may only have one of two values or states. In modern computerised data acquisition the states are normally represented as HI and LO in logic terms, as 0 Volts and E
Volts in electrical terms or as ‘0’ or ‘1’ values in binary arithmetic.
Binary
State
HI
LO
0 1 0 0 1 0 1 1 0 1 0
Time
Fig. 1.12 A Binary Digital Signal
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A signal free of noise may have any value within a given range at any point in time. An example of a noise-free signal is shown in Fig. 1.12.
In theory, if no noise is present, the value of the signal at any time can be ascertained and stipulated to any degree of precision required.
Relative
Amplitude
(mV)
Time (ms)
Fig. 1.12 A Noise-Free Analogue Signal
Value at t = 250ms is V = 54. 3467234…………..mV
An example of a signal with noise added to it is shown in Fig. 1.13. A signal with noise added has the same function of time as the noisefree signal but with the value of the noise added to it at any point in time.
Relative
Amplitude
(mV)
Time (ms)
Fig. 1. 13 An Analogue Signal with Added Noise
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Most noise is random in nature and therefore its value cannot be predicted with certainty at any point in time. It can only be stated to be within certain limits which are determined statistically. When superimposed on a signal of interest, it introduces the same degree of uncertainty into the value of the signal. This means that there is now an associated error in establishing the value of the signal at any point in time. In the case of the noise contaminated signal in Fig. 1.13 above:
Value at t = 250 ms could be V = 54.3467234…………..mV or it may be or it may be
V = 54.3467458………...…mV
V = 54.3467186………...…mV
This limits both the accuracy and resolution with which the value of a signal or event can be determined. It is clear above that the level of certainty of the value of the signal is limited to V = 54.3467 mV.
Error:
By error is meant the overall degree of uncertainty in establishing or measuring the value of a signal, process or event. In general measurement errors arise from several sources such as: electrical interference, electronic semiconductor noise, non-linearity of transducers and manufacturing tolerances and inaccuracies of instrumentation. There is also human error but this not what is being considered here.
Accuracy:
This is a measure of the extremes of the overall error associated with a measurement. It is essentially an indication of the limits of the confidence we can have in the measurements we make. It is usually specified either in terms of the units in which the measurement is made, e.g. mV as above, or as a percentage (normally of full scale) and is assumed positive or negative unless otherwise specified.
Resolution:
This is a measure of the smallest interval with which the event or signal of interest is specified. In a digital or numerical readout this corresponds to the least significant digit (LSD) and is specified in the units in which the measurement is made.
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Normally, the resolution is equal to or less than the accuracy of measurement. That is, a variable may be measured with a greater accuracy or precision than is needed and can be specified with lower resolution than the accuracy allows.
For example a voltage may be measured as: V = 2.053 V + 1 mV but may be recorded as: V = 2.05 V
However, an alarming trend has developed in recent years, whereby instrument manufacturers are marketing units that have multi-digit displays which give a resolution of the parameter being indicated which is greater than the actual measurement accuracy. This gives a false sense of digital precision. For example many commercially available thermometers will display temperature to a resolution of
0.1
O C but only have a measurement accuracy of + 1 O C so that the temperature displayed must be interpreted as 27.2 + 1 O C which means the temperature may lie anywhere in the range 26 – 28 O C. Other worrying examples of such instruments are home blood pressure monitors and diabetic blood glucose monitors.
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In modern data storage digital pulses are used to represent the 1s and
0s of Binary Numbers. A digital pulse lasts for a specified period of time, often referred to as a clock cycle. The pulse will have a high logic level, normally at the level of the supply voltage, to represent a binary
1 and a low logic level, normally 0V, to represent a binary 0. Ideal binary pulses are shown in Fig. 1.14 and it can be seen that logic levels are clearly and uniquely defined and also that the transitions from one binary state to another are abrupt and in ideal terms are instantaneous. A binary digit or pulse is known commercially as a ‘Bit’
Binary State 0 1
5V
Voltage
0V
2.5 5.0 Time (ms)
Fig. 1.14 A Pure Digital Pulse
In practice, digital pulses become contaminated by noise in the same manner as analogue signals as shown in Fig. 1.15 below:
Fig. 1.15 A Digital Pulse Contaminated by Noise
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It can clearly be seen that the profile of the pulse is much less well defined than in the ideal case. The precise value of the pulse cannot be determined exactly at any point in time, just as is the case when noise is added to an analogue signal. Moreover, it can be seen that the transition between logic states is not instantaneous but takes a finite amount of time to occur. However, despite this the values of ‘1’ and ‘0’ can clearly be identified without any uncertainty. Therefore it can be said that a binary pulse can be contaminated with a significant amount of noise without any error in detecting its value, which is simply either a ‘1’ or a ‘0’. It is essentially a matter of determining the presence or absence of a noisy pulse. The quality of the ‘1-ness’ or ‘0-ness’ is immaterial.
Digital Logic:
In digital logic circuits the effects of noise accumulated on pulses transmitted between gates or logic blocks can be dealt with very effectively. This is accomplished by specifying acceptable ranges for HI and LO logic levels which allow for added noise. These ranges will be related to the supply voltage of a particular logic system as shown in
Fig. 1.16.
V supply
Acceptable Range for Logic HI Value
V
H min
V
L max
Acceptable Range for Logic LO Value
0V
Fig. 1.16 Practical Logic Voltage Ranges
It must be assured that the ‘1’ level of the pulse is greater than the critical minimum value of V
H min
i.e. falls within the acceptable range for a logic HI value and that the ‘0’ level is less than the critical maximum level of V
L max
i.e. falls within the acceptable range for a logic
LO value. Once this is the case the pulse can be detected with absolute certainty. This is accomplished by setting a suitable threshold voltage as shown in the figure below. When the input pulse goes above this level an output pulse of perfectly restored logic HI level is generated.
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When the input pulse goes below the threshold voltage an output pulse of perfectly restored logic LO level is produced as shown in Fig.
1.17. In this way the noise is effectively removed from the pulse and it has been ‘regenerated’. This is done every time a logic pulse passes through a logic gate. In this way the effects of noise can be overcome entirely for digital pulses and they can be transmitted, received, stored and recovered without loss of quality or data and hence without loss of information.
V
V
V supply
H min
THRESHOLD
V
L max
0V
Noisy
Input
Pulse
Time
V supply
0V
Regenerated
Output
Pulse
Time
Fig. 1.17 The Regeneration of a Logic Pulse
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