Electronics Measurements and Instrumentation eBook & Notes - Download as PDF File .pdf), Text File .txt) or read online. Electrical and Electronics. Measurements and Instrumentation. Prithwiraj Purkait. Professor. Department of Electrical Engineering and. Dean, School of. Electronics Measurements And Instrumentation. Front Cover. ipprofehaphvol.tk, A.V. Bakshi. Technical Publications, - pages. 10 Reviews.

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Electronic Measurements and Instrumentation provides a comprehensive blend of the theoretical and practical aspects of electronic measurements and. ELECTRICAL and ELECTRONIC MEASUREMENTS and INSTRUMENTATION. 86 Pages · · MB · 5, Downloads ·English. electronic. download Electronic Measurements and Instrumentation by Rajendra Prasad PDF Electrical Power System. ₹ ₹ Rent this Ebook. 33% Off. Ebook.

The actual physical assembly may not appear to be so but it can be broken down into a representative diagram of connected blocks.

In the Humidity sensor it is activated by an input physical parameter and provides an output signal to the next block that processes the signal into a more appropriate state. A key generic entity is, therefore, the relationship between the input and output of the block.

As was pointed out earlier, all signals have a time characteristic, so we must consider the behavior of a block in terms of both the static and dynamic states. The behavior of the static regime alone and the combined static and dynamic regime can be found through use of an appropriate mathematical model of each block.

The mathematical description of system responses is easy to set up and use if the elements all act as linear systems and where addition of signals can be carried out in a linear additive manner. If nonlinearity exists in elements, then it becomes considerably more difcult perhaps even quite impractical to provide an easy to follow mathemat- ical explanation. Fortunately, general description of instrument systems responses can be usually be adequately covered using the linear treatment.

The equation forG can be written as two parts multiplied together. One expresses the static behavior of the block, that is, the value it has after all transient time varying effects have settled to their nal state. The other part tells us how that value responds when the block is in its dynamic state. The static part is known as the transfer characteristic and is often all that is needed to be known for block description.

The static and dynamic response of the cascade of blocks is simply the multiplication of all individual blocks. As each block has its own part for the static and dynamic behavior, the cascade equations can be rearranged to separate the static from the dynamic parts and then by multiplying the static set and the dynamic set we get the overall response in the static and dynamic states.

This is shown by the sequence of Equations. Instruments are formed from a connection of blocks. Each block can be represented by a conceptual and mathematical model. This example is of one type of humidity sensor.

It is now necessary to consider a major problem of instrument performance called instrument drift. This is caused by variations taking place in the parts of the instrumentation over time.

Prime sources occur as chemical structural changes and changing mechanical stresses. Drift is a complex phenomenon for which the observed effects are that the sensitivity and offset values vary. It also can alter the accuracy of the instrument differently at the various amplitudes of the signal present. Detailed description of drift is not at all easy but it is possible to work satisfactorily with simplied values that give the average of a set of observations, this usually being quoted in a conservative manner.

The rst graph a in Figure shows typical steady drift of a measuring. Figure b shows how an electronic amplier might settle down after being turned on. Drift is also caused by variations in environmental parameters such as temperature, pressure, and humidity that operate on the components.

These are known as inuence parameters. An example is the change of the resistance of an electrical resistor, this resistor forming the critical part of an electronic amplier that sets its gain as its operating temperature changes. Unfortunately, the observed effects of inuence parameter induced drift often are the same as for time varying drift.

Appropriate testing of blocks such as electronic ampliers does allow the two to be separated to some extent. For example, altering only the temperature of the amplier over a short period will quickly show its temperature dependence. Drift due to inuence parameters is graphed in much the same way as for time drift.

Figure shows the drift of an amplier as temperature varies. Note that it depends signicantly on the temperature Drift in the performance of an instrument takes many forms: Dynamic Characteristics of Instrument Systems: Dealing with Dynamic States: Measurement outcomes are rarely static over time. They will possess a dynamic component that must be understood for correct interpretation of the results. For example, a trace made on an ink pen chart recorder will be subject to the speed at which the pen can follow the input signal changes.

Drift in the performance of an instrument takes many forms: Error of nonlinearity can be expressed in four different ways: If the transfer relationship for a block follows linear laws of performance, then a generic mathematical method of dynamic description can be used. Unfortunately, simple mathematical methods have not been found that can describe all types of instrument responses in a simplistic and uniform manner.

If the behavior is nonlinear, then description. The behavior of nonlinear systems can, however, be studied as segments of linear behavior joined end to end. Here, digital computers are effectively used to model systems of any kind provided the user is prepared to spend time setting up an adequate model. Now the mathematics used to describe linear dynamic systems can be introduced. This gives valuable insight into the expected behavior of instrumentation, and it is usually found that the response can be approximated as linear.

The modeled response at the output of a blockGresult is obtained by multiplying the mathematical expression for the input signalGinput by the transfer function of the block under investigationGresponse, as shown in Equation 3. We begin with the former set: Forcing Functions Let us rst develop an understanding of the various types of input signal used to perform tests.

The most commonly used signals are shown in Figure 3. These each possess different valuable test features. For example, the sine-wave is the basis of analysis of all complex wave-shapes because they can be formed as a combination of various sinewaves, each having individual responses that add to give all other wave- shapes.

The step function has intuitively obvious uses because input transients of this kind are commonly encountered. The ramp test function is used to present a more realistic input for those systems where it is not possible to obtain instantaneous step input changes, such as attempting to move a large mass by a limited size of force.

Forcing functions are also chosen because they can be easily described by a simple mathematical expression, thus making mathematical analysis relatively straightforward. Characteristic Equation Development The behavior of a block that exhibits linear behavior is mathematically represented in the general form of expression given as Equation Here, the coefcientsa2,a1, anda0 are constants dependent on the particular block of interest.

The left- hand side of the equation is known as the characteristic equation. It is specic to the internal properties of the block and is not altered by the way the block is used.

The specic combination of forcing function input and block characteristic equation collectively decides the combined output response. Connections around the block, such as feedback from the output to the input, can alter the overall behavior signicantly: Solution of the combined behavior is obtained using Laplace transform methods to obtain the output responses in the time or the complex frequency domain.

These mathematical methods might not be familiar to the reader, but this is not a serious difculty for the cases most encountered in practice are.

EMI - Electronics measurements & instrumentation ebook & pdf notes

Unit of measurement: For example, length is a physical quantity. The metre is a unit of length that represents a definite predetermined length. When we say 10 metres or 10 m , we actually mean 10 times the definite predetermined length called "metre". The definition, agreement, and practical use of units of measurement have played a crucial role in human endeavour from early ages up to this day. Disparate systems of units used to be very common.

Now there is a global standard, the International System of Units SI , the modern form of the metric system. In trade, weights and measures is often a subject of governmental regulation, to ensure fairness and transparency. Metrology is the science for developing nationally and internationally accepted units of weights and measures. In physics and metrology, units are standards for measurement of physical quantities that need clear definitions to be useful.

Reproducibility of experimental results is central to the scientific method. A standard system of units facilitates this.

Scientific systems of units are a refinement of the concept of weights and measures developed long ago for commercial purposes. Science, medicine, and engineering often use larger and smaller units of measurement than those used in everyday life and indicate them more precisely. The judicious selection of the units of measurement can aid researchers in problem solving see, for example, dimensional analysis. In the social sciences, there are no standard units of measurement and the theory and practice of measurement is studied in psychometrics and the theory of conjoint measurement.

Error Analysis: Introduction The knowledge we have of the physical world is obtained by doing experiments and making measurements. It is important to understand how to express such data and how to analyze and draw meaningful conclusions from it. In doing this it is crucial to understand that all measurements of physical quantities are subject to uncertainties. It is never possible to measure anything exactly.

It is good, of course, to make the error as small as possible but it is always there. And in order to draw valid conclusions the error must be indicated and dealt with properly. Take the measurement of a person's height as an example. Assuming that her height has been determined to be 5' 8", how accurate is our result? Well, the height of a person depends on how straight she stands, whether she just got up most people are slightly taller when getting up from a long rest in horizontal position , whether she has her shoes on, and how long her hair is and how it is made up.

These inaccuracies could all be called errors of definition. A quantity such as height is not exactly defined without specifying many other circumstances.

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Even if you could precisely specify the "circumstances," your result would still have an error associated with it. The scale you are using is of limited accuracy; when you read the scale, you may have to estimate a fraction between the marks on the scale, etc.

If the result of a measurement is to have meaning it cannot consist of the measured value alone. An indication of how accurate the result is must be included also.

Indeed, typically more effort is required to determine the error or uncertainty in a measurement than to perform the measurement itself. Thus, the result of any physical measurement has two essential components: For example, a measurement of the width of a table would yield a result such as Significant Figures: The significant figures of a measured or calculated quantity are the meaningful digits in it.

There are conventions which you should learn and follow for how to express numbers so as to properly indicate their significant figures. Any digit that is not zero is significant. Thus has three significant figures and 1.

Zeros between non zero digits are significant. Thus has four significant figures. Zeros to the left of the first non zero digit are not significant. Thus 0. This is more easily seen if it is written as 3. For numbers with decimal points, zeros to the right of a non zero digit are significant. Thus 2. For this reason it is important to keep the trailing zeros to indicate the actual number of significant figures. For numbers without decimal points, trailing zeros may or may not be significant.

Thus, indicates only one significant figure. To indicate that the trailing zeros are significant a decimal point must be added. For example, Exact numbers have an infinite number of significant digits. For example, if there are two oranges on a table, then the number of oranges is 2. Defined numbers are also like this.

For example, the number of centimeters per inch 2. There are also specific rules for how to consistently express the uncertainty associated with a number. In general, the last significant figure in any result should be of the same order of magnitude i. Also, the uncertainty should be rounded to one or two significant figures. Always work out the uncertainty after finding the number of significant figures for the actual measurement.

For example, 9. But in the end, the answer must be expressed with only the proper number of significant figures. After addition or subtraction, the result is significant only to the place determined by the largest last significant place in the original numbers. After multiplication or division, the number of significant figures in the result is determined by the original number with the smallest number of significant figures.

For example, 2. Refer to any good introductory chemistry textbook for an explanation of the methodology for working out significant figures. The Idea of Error: The concept of error needs to be well understood. What is and what is not meant by "error"? A measurement may be made of a quantity which has an accepted value which can be looked up in a handbook e. The difference between the measurement and the accepted value is not what is meant by error. Such accepted values are not "right" answers.

They are just measurements made by other people which have errors associated with them as well. Nor does error mean "blunder. Obviously, it cannot be determined exactly how far off a measurement is; if this could be done, it would be possible to just give a more accurate, corrected value. Error, then, has to do with uncertainty in measurements that nothing can be done about. If a measurement is repeated, the values obtained will differ and none of the results can be preferred over the others.

Although it is not possible to do anything about such error, it can be characterized. For instance, the repeated measurements may cluster tightly together or they may spread widely. This pattern can be analyzed systematically. Classification of Error: Generally, errors can be divided into two broad and rough but useful classes: Systematic errors are errors which tend to shift all measurements in a systematic way so their mean value is displaced.

This may be due to such things as incorrect calibration of equipment, consistently improper use of equipment or failure to properly account for some effect. In a sense, a systematic error is rather like a blunder and large systematic errors can and must be eliminated in a good experiment.

But small systematic errors will always be present. For instance, no instrument can ever be calibrated perfectly. Other sources of systematic errors are external effects which can change the results of the experiment, but for which the corrections are not well known.

In science, the reasons why several independent confirmations of experimental results are often required especially using different techniques is because different apparatus at different places may be affected by different systematic effects.

Aside from making mistakes such as thinking one is using the x10 scale, and actually using the x scale , the reason why experiments sometimes yield results which may be far outside the quoted errors is because of systematic effects which were not accounted for.

Random errors are errors which fluctuate from one measurement to the next. They yield results distributed about some mean value.

They can occur for a variety of reasons. They may occur due to lack of sensitivity. For a sufficiently a small change an instrument may not be able to respond to it or to indicate it or the observer may not be able to discern it.

Measurement and Instrumentation

They may occur due to noise. There may be extraneous disturbances which cannot be taken into account. They may be due to imprecise definition. They may also occur due to statistical processes such as the roll of dice.

Random errors displace measurements in an arbitrary direction whereas systematic errors displace measurements in a single direction. Some systematic error can be substantially eliminated or properly taken into account. Random errors are unavoidable and must be lived with. Many times you will find results quoted with two errors. The first error quoted is usually the random error, and the second is called the systematic error.

If only one error is quoted, then the errors from all sources are added together. In quadrature as described in the section on propagation of errors. A good example of "random error" is the statistical error associated with sampling or counting. For example, consider radioactive decay which occurs randomly at a some average rate. If a sample has, on average, radioactive decays per second then the expected number of decays in 5 seconds would be Behavior like this, where the error, , 1 is called a Poisson statistical process.

Typically if one does not know that,.

Mean Value Suppose an experiment were repeated many, say N, times to get, , N measurements of the same quantity, x. If the errors were random then the errors in these results would differ in sign and magnitude. So if the average or mean value of our measurements were calculated,.

This is the best that can be done to deal with random errors: It should be pointed out that this estimate for a given N will differ from the limit as the true mean value; though, of course, for larger N it will be closer to the limit. In the case of the previous example: Doing this should give a result with less error than any of the individual measurements.

But it is obviously expensive, time consuming and tedious. So, eventually one must compromise and decide that the job is done. Nevertheless, repeating the experiment is the only way to gain confidence in and knowledge of its accuracy. In the process an estimate of the deviation of the measurements from the mean value can be obtained. Measuring Error There are several different ways the distribution of the measured values of a repeated experiment such as discussed above can be specified.

Maximum Error The maximum and minimum values of the data set, In these terms, the quantity,. And virtually no measurements should ever fall outside. Probable Error The probable error, measured values.

Standard Deviation For the data to have a Gaussian distribution means that the probability of obtaining the result x is, , 5. Because of the law of large numbers this assumption will tend to be valid for random errors.

And so it is common practice to quote error in terms of the standard deviation of a Gaussian distribution fit to the observed data distribution.

This is the way you should quote error in your reports. It is just as wrong to indicate an error which is too large as one which is too small. Certainly saying that a person's height is 5' 8.

Standard Deviation The mean is the most probable value of a Gaussian distribution. In terms of the mean, the standard deviation of any distribution is,. The best estimate of the true standard deviation is,. The true mean value of x is not being used to calculate the variance, but only the average of the measurements as the best estimate of it.

Thus, as calculated is always a little bit smaller than , the quantity really wanted. In the theory of probability that is, using the assumption that the data has a Gaussian distribution , it can be shown that this underestimate is corrected by using N-1 instead of N. However, we are also interested in the error of the mean, which is smaller than sx if there were several measurements.

An exact calculation yields,. The number to report for this series of N measurements of x is where. This means that out of experiments of this type, on the average, 32 experiments will obtain a value which is outside the standard errors. Suppose the number of cosmic ray particles passing through some detecting device every hour is measured nine times and the results are those in the following table.

Random counting processes like this example obey a Poisson distribution for which So one would expect the value of to be This is somewhat less than the value of 14 obtained above; indicating either the process is not quite random or, what is more likely, more measurements are needed. The same error analysis can be used for any set of repeated measurements whether they arise from random processes or not.

For example in the Atwood's machine experiment to measure g you are asked to measure time five times for a given distance of fall s. The mean value of the time is, , 9 and the standard error of the mean is,. For the distance measurement you will have to estimate [[Delta]]s, the precision with which you can measure the drop distance probably of the order of mm.

Propagation of Errors: Frequently, the result of an experiment will not be measured directly. Rather, it will be calculated from several measured physical quantities each of which has a mean value and an error. What is the resulting error in the final result of such an experiment? A first thought might be that the error in Z would be just the sum of the errors in A and B.

After all, 11 and. This could only happen if the errors in the two variables were perfectly correlated, i. If the variables are independent then sometimes the error in one variable will happen to cancel out some of the error in the other and so, on the average, the error in Z will be less than the sum of the errors in its parts. A reasonable way to try to take this into account is to treat the perturbations in Z produced by perturbations in its parts as if they were "perpendicular" and added according to the Pythagorean theorem,.

This idea can be used to derive a general rule.

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If A is perturbed by then Z will be perturbed by. Similarly the perturbation in Z due to a perturbation in B is,. Combining these by the Pythagorean theorem yields. Errors combine in the same way for both addition and subtraction.

You should be able to verify that the result is the same for division as it is for multiplication. For example,. It should be noted that since the above applies only when the two measured quantities are independent of each other it does not apply when, for example, one physical quantity is measured and what is required is its square.

If a variable Z depends on one or two variables A and B which have independent errors and then the rule for calculating the error in Z is tabulated in following table for a variety of simple relationships.

These rules may be compounded for more complicated situations. The design of a voltmeter, ammeter or ohmmeter begins with a current-sensitive element. Though most modern meters have solid state digital readouts, the physics is more readily demonstrated with a moving coil current detector called a galvanometer.

Since the modifications of the current sensor are compact, it is practical to have all three functions in a single instrument with multiple ranges of sensitivity.

Schematically, a single range "multimeter" might be designed as illustrated. A voltmeter measures the change in voltage between two points in an electric circuit and therefore must be connected in parallel with the portion of the circuit on which the measurement is made. By contrast, an ammeter must be connected in series. In analogy with a water circuit, a voltmeter is like a meter designed to measure pressure difference. It is necessary for the voltmeter to have a very high resistance so that it does not have an appreciable affect on the current or voltage associated with the measured circuit.

Modern solid-state meters have digital readouts, but the principles of operation can be better appreciated by examining the older moving coil meters based on galvanometer sensors.

An ammeter is an instrument for measuring the electric current in amperes in a branch of an electric circuit. It must be placed in series with the measured branch, and must have very low resistance to avoid significant alteration of the current it is to measure. By contrast, an voltmeter must be connected in parallel. The analogy with an in-line flowmeter in a water circuit can help visualize why an ammeter must have a low resistance, and why connecting an ammeter in parallel can damage the meter.

The standard way to measure resistance in ohms is to supply a constant voltage to the resistance and measure the current through it.

That current is of course inversely proportional to the resistance according to Ohm's law, so that you have a non-linear scale. It is not an "Average" voltage and its mathematical relationship to peak voltage varies depending on the type of waveform. By definition, RMS Value, also called the effective or heating value of AC, is equivalent to a DC voltage that would provide the same amount of heat generation in a resistor as the AC voltage would if applied to that same resistor.

The heating value of the voltage available is equivalent to a volt DC source this is for example only and does not mean DC and AC are interchangeable. The typical multi-meter is not a True RMS reading meter. As a result it will only produce misleading voltage readings when trying to measure anything other than a DC signal or sine wave.

Several types of multi-meters exist, and the owner's manual or the manufacturer should tell you which type you have. Each handles AC signals differently, here are the three basic types. A rectifier type multi-meter indicates RMS values for sinewaves only. It does this by measuring average voltage and multiplying by 1. Trying to use this type of meter with any waveform other than a sine wave will result in erroneous RMS readings. Average reading digital volt meters are just that, they measure average voltage for an AC signal.

Using the equations in the next column for a sinewave, average voltage Vavg can be converted to Volts RMS Vrms , and doing this allows the meter to display an RMS reading for a sinewave. Bridge Measurements: A Maxwell bridge in long form, a Maxwell-Wien bridge is a type of Wheatstone bridge used to measure an unknown inductance usually of low Q value in terms of calibrated resistance and capacitance.

It is a real product bridge. With reference to the picture, in a typical application R1 and R4 are known fixed entities, and R2 and C2 are known variable entities.

R2 and C2 are adjusted until the bridge is balanced. R3 and L3 can then be calculated based on the values of the other components:. To avoid the difficulties associated with determining the precise value of a variable capacitance, sometimes a fixed-value capacitor will be installed and more than one resistor will be made variable. The additional complexity of using a Maxwell bridge over simpler bridge types is warranted in circumstances where either the mutual inductance between the load and the known bridge entities, or stray electromagnetic interference, distorts the measurement results.

The capacitive reactance in the bridge will exactly oppose the inductive reactance of the load when the bridge is balanced, allowing the load's resistance and reactance to be reliably determined. Wheatstone's bridge circuit diagram It is used to measure an unknown electrical resistance by balancing two legs of a bridge circuit, one leg of which includes the unknown component.

Its operation is similar to the original potentiometer. Rx is the unknown resistance to be measured; R1, R2 and R3 are resistors of known resistance and the resistance of R2 is adjustable. R2 is varied until this condition is reached. The direction of the current indicates whether R2 is too high or too low. Detecting zero current can be done to extremely high accuracy see galvanometer. Therefore, if R1, R2 and R3 are known to high precision, then Rx can be measured to high precision.

Very small changes in Rx disrupt the balance and are readily detected. This setup is frequently used in strain gauge and resistance thermometer measurements, as it is usually faster to read a voltage level off a meter than to adjust a resistance to zero the voltage.

The desired value of Rx is now known to be given as:. If all four resistor values and the supply voltage VS are known, the voltage across the bridge VG can be found by working out the voltage from each potential divider and subtracting one from the other. The equation for this is:. The Wheatstone bridge illustrates the concept of a difference measurement, which can be extremely accurate. Variations on the Wheatstone bridge can be used to measure capacitance, inductance, impedance and other quantities, such as the amount of combustible gases in a sample, with an explosimeter.

The Kelvin bridge was specially adapted from the Wheatstone bridge for measuring very low resistances. In many cases, the significance of measuring the unknown resistance is related to measuring the impact of some physical phenomenon - such as force, temperature, pressure, etc. Schering Bridge: A Schering Bridge is a bridge circuit used for measuring an unknown electrical capacitance and its dissipation factor. The dissipation factor of a capacitor is the the ratio of its resistance to its capacitive reactance.

The Schering Bridge is basically a four-arm alternating-current AC bridge circuit whose measurement depends on balancing the loads on its arms. Figure 1 below shows a diagram of the Schering Bridge. The Schering Bridge In the Schering Bridge above, the resistance values of resistors R1 and R2 are known, while the resistance value of resistor R3 is unknown. The capacitance values of C1 and C2 are also known, while the capacitance of C3 is the value being measured.

To measure R3 and C3, the values of C2 and R2 are fixed, while the values of R1 and C1 are adjusted until the current through the ammeter between points A and B becomes zero. This happens when the voltages at points A and B are equal, in which case the bridge is said to be 'balanced'.

In an AC circuit that has a capacitor, the capacitor contributes a capacitive reactance to the impedance. Thus, when the bridge is balanced: A Hay Bridge is an AC bridge circuit used for measuring an unknown inductance by balancing the loads of its four arms, one of which contains the unknown inductance.

One of the arms of a Hay Bridge has a capacitor of known characteristics, which is the principal component used for determining the unknown inductance value. Figure 1 below shows a diagram of the Hay Bridge. The Hay Bridge: As shown in Figure 1, one arm of the Hay bridge consists of a capacitor in series with a resistor C1 and R2 and another arm consists of an inductor L1 in series with a resistor L1 and R4.

The other two arms simply contain a resistor each R1 and R3. The values of R1and R3 are known, and R2 and C1 are both adjustable. The unknown values are those of L1 and R4. Like other bridge circuits, the measuring ability of a Hay Bridge depends on 'balancing' the circuit.

Balancing the circuit in Figure 1 means adjusting R2 and C1 until the current through the ammeter between points A and B becomes zero. This happens when the voltages at points A and B are equal.

Substituting R4, one comes up with the following equation: A Wien bridge oscillator is a type of electronic oscillator that generates sine waves. It can generate a large range of frequencies. The circuit is based on an electrical network originally developed by Max Wien in The bridge comprises four resistors and two capacitors. It can also be viewed as a positive feedback system combined with a bandpass filter. Wien did.

The modern circuit is derived from William Hewlett's Stanford University master's degree thesis. Hewlett, along with David Packard co-founded Hewlett-Packard. Their first product was the HP A, a precision sine wave oscillator based on the Wien bridge.

The A was one of the first instruments to produce such low distortion. Amplitude stabilization: The key to Hewlett's low distortion oscillator is effective amplitude stabilization.

The amplitude of electronic oscillators tends to increase until clipping or other gain limitation is reached.

This leads to high harmonic distortion, which is often undesirable. Hewlett used an incandescent bulb as a positive temperature coefficient PTC thermistor in the oscillator feedback path to limit the gain.

The resistance of light bulbs and similar heating elements increases as their temperature increases. If the oscillation frequency is significantly higher than the thermal time constant of the heating element, the radiated power is proportional to the oscillator power. Since heating elements are close to black body radiators, they follow the Stefan-Boltzmann law.

The radiated power is proportional to T4, so resistance increases at a greater rate than amplitude. If the gain is inversely proportional to the oscillation amplitude, the oscillator gain stage reaches a steady state and operates as a near ideal class A amplifier, achieving very low distortion at the frequency of interest.

At lower frequencies the time period of the oscillator approaches the thermal time constant of the thermistor element and the output distortion starts to rise significantly.

Light bulbs have their disadvantages when used as gain control elements in Wien bridge oscillators, most notably a very high sensitivity to vibration due to the bulb's microphonic nature amplitude modulating the oscillator output, and a limitation in high frequency response due to the inductive nature of the coiled filament.

Modern Wien bridge oscillators have used other nonlinear elements, such as diodes, thermistors, field effect transistors, or photocells for amplitude stabilization in place of light bulbs.

Distortion as low. This is due to the low damping factor and long time constant of the crude control loop, and disturbances cause the output amplitude to exhibit a decaying sinusoidal response.

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APA 6th ed. Citations are based on reference standards. However, formatting rules can vary widely between applications and fields of interest or study. The specific requirements or preferences of your reviewing publisher, classroom teacher, institution or organization should be applied.

The E-mail Address es field is required. Please enter recipient e-mail address es. The E-mail Address es you entered is are not in a valid format. In addition, the electron beam is cut off blanked during flyback so that the retrace sweep is not observed. In general, the instrument is operated in the following manner. The signal to be displayed is amplified by the vertical amplifier and applied to the verical deflection plates of the CRT.

A portion of the signal in the vertical amplifier is applied to the sweep trigger as a triggering signal. The sweep trigger then generates a pulse coincident with a selected point in the cycle of the triggering signal.

This pulse turns on the sweep generator, initiating the sawtooth wave form. The sawtooth wave is amplified by the horizontal amplifier and applied to the horizontal deflection plates. Usually, additional provisions signal are made for appliying an external triggering signal or utilizing the 60 Hz line for triggering.

Also the sweep generator may be bypassed and an external signal applied directly to the horizontal amplifier. CRO Controls : The controls available on most oscilloscopes provide a wide range of operating conditions and thus make the instrument especially versatile. Since many of these controls are common to most oscilloscopes a brief description of them follows.

[PDF] A Course in Electronic Measurements and Instrumentation By A.K. Sawhney Book Free Download

Focus: Focus the spot or trace on the screen. Intensity: Regulates the brightness of the spot or trace. Sensitivity: Selects the sensitivity of the vertical amplifier in calibrated steps.

Variable Sensitivity: Provides a continuous range of sensitivities between the calibrated steps. Normally the sensitivity is calibrated only when the variable knob is in the fully clockwise position. Selecting dc couples the input directly to the amplifier; selecting ac send the signal through a capacitor before going to the amplifier thus blocking any constant component. Calibrated position is fully clockwise. Position: Controls horizontal position of trace on screen. Horizontal Variable: Controls the attenuation reduction of signal applied to horizontal aplifier through Ext.

Coupling: Selects whether triggering occurs at a specific dc or ac level. Source: Selects the source of the triggering signal. LINE - 60 cycle triger Level: Selects the voltage point on the triggering signal at which sweep is triggered.

It also allows automatic auto triggering of allows sweep to run free free run. The lower jack is grounded to the case. Horizontal Input: A pair of jacks for connecting an external signal to the horizontal amplifier. The lower terminal is graounted to the case of the oscilloscope. External Tigger Input: Input connector for external trigger signal. Out: Provides amplitude calibrated square waves of 25 and millivolts for use in calibrating the gain of the amplifiers.

Sensitivity is variable. Range of sweep is variable. Operating Instructions: Before plugging the oscilloscope into a wall receptacle, set the controls as follows: a Power switch at off b Intensity fully counter clockwise c Vertical centering in the center of range d Horizontal centering in the center of range e Vertical at 0.

Turn power on. Do not advance the Intensity Control. Allow the scope to warm up for approximately two minutes, then turn the Intensity Control until the beam is visible on the screen. Set the signal generator to a frequency of cycles per second. Connect the output from the gererator to the vertical input of the oscilloscope. Establish a steady trace of this input signal on the scope. Adjust play with all of the scope and signal generator controls until you become familiar with the functionof each.Appropriate testing of blocks such as electronic ampliers does allow the two to be separated to some extent.

Coupling: Selects whether triggering occurs at a specific dc or ac level. Welcome to EasyEngineering, One of the trusted educational blog.

This is essentially what is embodied in the Shannon-Nyquist sampling theorem. Ernest O. There is also a so-called aperture error which is due to a clock jitter and is revealed when digitizing a time-variant signal not a constant value. Nor does error mean "blunder. The book is divided into two parts that explain the topics Measurements and Instrumentation. The values can represent the ranges from 0 to i.

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