Texas Instruments | AN-236 An Introduction to the Sampling Theorem (Rev. C) | Application notes | Texas Instruments AN-236 An Introduction to the Sampling Theorem (Rev. C) Application notes

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AN-236 An Introduction to the Sampling Theorem
.....................................................................................................................................................
ABSTRACT
With rapid advancement in data acquistion technology (i.e. analog-to-digital and digital-to-analog
converters) and the explosive introduction of micro-computers, selected complex linear and nonlinear
functions currently implemented with analog circuitry are being alternately implemented with sample data
systems.
Contents
An Introduction to the Sampling Theorem ............................................................................... 3
An Intuitive Development .................................................................................................. 3
The Sampling Theorem .................................................................................................... 5
Some Observations and Definitions ...................................................................................... 8
The Sampling Theorem and Its Hardware ............................................................................... 9
5.1
IMPLICATIONS .................................................................................................... 9
6
A Final Note ................................................................................................................ 17
7
Acknowledgements ........................................................................................................ 17
8
ARTICLE REFERENCES ................................................................................................ 17
Appendix A
Basic Filter Concepts ............................................................................................ 19
1
2
3
4
5
List of Figures
1
When sampling, many signals may be found to have the same set of data points. These are called
aliases of each other. ....................................................................................................... 4
2
Shown in the shaded area is an ideal, low pass, anti-aliasing filter response. Signals passed through the
filter are bandlimited to frequencies no greater than the cutoff frequency, fc. In accordance with the
sampling theorem, to recover the bandlimited signal exactly the sampling rate must be chosen to be
greater than 2fc. ............................................................................................................. 4
3
Fourier transform of a sampled signal. ................................................................................... 7
4
Recovery of a signal f(t) from sampled data information............................................................... 7
5
Spectral folding or aliasing caused by: (a) under sampling (b) exaggerated under sampling. ................... 8
6
Aliased spectral envelope (a) and (b) of a and b respectively. ....................................................... 9
7
Generalized single channel sample data system. ...................................................................... 9
8
Typical filter magnitude and phase versus frequency response..................................................... 11
9
10
11
12
13
14
15
16
17
18
19
...............................................................................................................................
..............................................................................
Quantization error..........................................................................................................
Amplitude uncertainty as a function of (a) a nonvarying aperture and(b) aperture time uncertainty. ..........
The Fourier transform of the rectangular pulse (a) is shown in (b). ................................................
Sampling Pulse (a), its Magnitude (b) and Phase Response (c). ...................................................
Pulse width and how it effects the sin X/X envelop spectrum (normalized amplitudes)..........................
(a) Processed signal data points (b) output of D/A converter (c) output of smoothing filter. ....................
Common Low Pass Filter Response ....................................................................................
Common High Pass Filter Response ...................................................................................
Common Band-pass Filter Response...................................................................................
(c) equals the convolution of (a) with (b).
12
12
14
14
15
15
16
17
19
19
20
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20
2
Common Band-Reject Filter Response ................................................................................. 20
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1
An Introduction to the Sampling Theorem
With rapid advancement in data acquistion technology (i.e. analog-to-digital and digital-to-analog
converters) and the explosive introduction of micro-computers, selected complex linear and nonlinear
functions currently implemented with analog circuitry are being alternately implemented with sample data
systems.
Though more costly than their analog counterpart, these sampled data systems feature programmability.
Additionally, many of the algorithms employed are a result of developments made in the area of signal
processing and are in some cases capable of functions unrealizable by current analog techniques.
With increased usage a proportional demand has evolved to understand the theoretical basis required in
interfacing these sampled data-systems to the analog world.
theorem in a manner, hopefully, easily understood by those making their first attempt at signal processing.
Additionally discussed are some of the unobvious hardware effects that one might encounter when
applying the sampled theorem.
With this. . . let us begin.
2
An Intuitive Development
The sampling theorem by C.E. Shannon in 1949 places restrictions on the frequency content of the time
function signal, f(t), and can be simply stated as follows:
—
In order to recover the signal function f(t) exactly, it is necessary to sample f(t) at a rate greater
than twice its highest frequency component.
Practically speaking for example, to sample an analog signal having a maximum frequency of 2Kc
requires sampling at greater than 4Kc to preserve and recover the waveform exactly.
The consequences of sampling a signal at a rate below its highest frequency component results in a
phenomenon known as aliasing. This concept results in a frequency mistakenly taking on the identity of an
entirely different frequency when recovered. In an attempt to clarify this, envision the ideal sampler of
Figure 1(a), with a sample period of T shown in Figure 1(b), sampling the waveform f(t) as pictured in
Figure 1(c). The sampled data points of f'(t) are shown in Figure 1(d) and can be defined as the sample
set of the continuous function f(t). Note in Figure 1(e) that another frequency component, a'(t), can be
found that has the same sample set of data points as f'(t) in Figure 1(d). Because of this it is difficult to
determine which frequency a'(t), is truly being observed. This effect is similar to that observed in western
movies when watching the spoked wheels of a rapidly moving stagecoach rotate backwards at a slow
rate. The effect is a result of each individual frame of film resembling a discrete strobed sampling
operation flashing at a rate slightly faster than that of the rotating wheel. Each observed sample point or
frame catches the spoked wheel slightly displaced from its previous position giving the effective
appearance of a wheel rotating backwards. Again, aliasing is evidenced and in this example it becomes
difficult to determine which is the true rotational frequency being observed.
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An Intuitive Development
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Figure 1. When sampling, many signals may be found to have the same set of data points. These are
called aliases of each other.
Figure 2. Shown in the shaded area is an ideal, low pass, anti-aliasing filter response. Signals passed
through the filter are bandlimited to frequencies no greater than the cutoff frequency, fc. In accordance
with the sampling theorem, to recover the bandlimited signal exactly the sampling rate must be chosen
to be greater than 2fc.
On the surface it is easily said that anti-aliasing designs can be achieved by sampling at a rate greater
than twice the maximum frequency found within the signal to be sampled. In the real world, however, most
signals contain the entire spectrum of frequency components; from the desired to those present in white
noise. To recover such information accurately the system would require an unrealizably high sample rate.
This difficulty can be easily overcome by preconditioning the input signal, the means of which would be a
band-limiting or frequency filtering function performed prior to the sample data input. The prefilter, typically
called anti-aliasing filter guarantees, for example in the low pass filter case, that the sampled data system
receives analog signals having a spectral content no greater than those frequencies allowed by the filter.
As illustrated in Figure 2, it thus becomes a simple matter to sample at greater than twice the maximum
frequency content of a given signal.
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A parallel analog of band-limiting can be made to the world of perception when considering the spectrum
of white light. It can be realized that the study of violet light wavelengths generated from a white light
source would be vastly simplified if initial band-limiting were performed through the use of a prism or white
light filter.
3
The Sampling Theorem
To solidify some of the intuitive thoughts presented in the previous section, the sampling theorem will be
presented applying the rigor of mathematics supported by an illustrative proof. This should hopefully leave
the reader with a comfortable understanding of the sampling theorem.
Theorem: — If the Fourier transform F(ω) of a signal function f(t) is zero for all frequencies above |ω| ≥
ωc, then f(t) can be uniquely determined from its sampled values
fn = f(nT)
(1)
These values are a sequence of equidistant sample points spaced
apart. (f)t is thus given by
(2)
Proof: Using the inverse Fourier transform formula:
(3)
the band limited function, f(t), takes the form, Figure 3a,
(4)
(5)
(6)
See Figure 3c and Figure 3e.
Expressing F(ω) as a Fourier series in the interval −ωc ≤ ω ≤ ωc we have
(7)
Where,
(8)
Further manipulating Equation 8
(9)
Cn can be written as
(10)
Substituting Equation 10 into Equation 7 gives the periodic Fourier Transform
(11)
of Figure 3f. Using Poisson's sum formula F(ω) can be stated more clearly as
(12)
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Interestingly for the interval −ωc ≤ ω ≤ ωc the periodic function Fp(ω) and Figure 3f. equals F(ω) and
Figure 3b. respectively. Analogously if Fp(ω) were multiplied by a rectangular pulse defined,
H(ω) = 1
−ωc ≤ ω ≤ ω
(13)
H(ω) = 0
|ω| ≥ ωC
(14)
and
then as pictured in Figure 4b, d, and f,
(15)
Solving for f(t) the inverse Fourier transform Equation 3 is applied to Equation 15
(16)
(17)
(1)
where
(18)
and fs is the sampling frequency
giving
(19)
Equation 19 is equivalent to Equation 2 as is illustrated in Figure 4e and Figure 3a respectively.
As observed in Figure 3 and Figure 4, each step of the sampling theorem proof was also illustrated with its
Fourier transform pair. This was done to present alternate illustrative proofs.
Recalling the convolution theorem, the convolution of F(ω), Figure 3b, with a set of equidistant impulses,
Figure 3d, yields the same periodic frequency function Fp(ω), Figure 3f, as did the Fourier transform of fn,
Figure 3e, the product of f(t), Figure 3a, and its equidistant sample impulses, Figure 3c.
In the same light the original time function f(t), Figure 4e, could have been recovered from its sampled
waveform by convolving fn, Figure 4a, with h(t), Figure 4c, rather than multiplying Fp(ω), Figure 4b, by the
rectangular function H(ω), Figure 4d, to get F(ω), Figure 4f, and finally inverse transforming to achieve f(t),
Figure 4e, as done in the mathematic proof.
(1)
6
Poisson's sum formula
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(2)
Figure 3. Fourier transform of a sampled signal.
Figure 4. Recovery of a signal f(t) from sampled data information.
(2)
The convolution theorem allows one to mathematically convolve in the time domain by simply multiplying in the frequency domain. That
is, if f(t) has the Fourier transform F(ω), and x(t) has the Fourier transform X(ω), then the convolution f(t)*x(t) has the Fourier transform
F(ω)•X(ω).f(t) * x(t) ↔ F(ω) • X(ω)f(t) • x(t) ↔ F(ω) * X(ω)
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Some Observations and Definitions
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Some Observations and Definitions
If Figure 3f or Figure 4b are re-examined it can be noted that the original spectrum Fp(ω), |ω| ≤ ωc, and its
images Fp(ω), |ω| ≥ ωc, are non-overlapping. On the other hand Figure 5 illustrates spectral folding,
overlapping or aliasing of the spectrum images into the original signal spectrum. This aliasing effect is, in
fact, a result of undersampling and further causes the information of the original signal to be
indistinguishable from its images (i.e. Figure 1e). From Figure 6 one can readily see that the signal is thus
considered non-recoverable.
The frequency |fc| of Figure 3f and Figure 4b is exactly one half the sampling frequency, fc=fs/2, and is
defined as the Nyquist frequency (after Harry Nyquist of Bell Laboratories). It is also often called the
aliasing frequency or folding frequency for the reasons discussed above. From this we can say that in
order to prevent aliasing in a sampled-data system the sampling frequency should be chosen to be
greater than twice the highest frequency component fc of the signal being sampled.
By definition
fs ≥ 2fc
(20)
Note, however, that no mention has been made to sample at precisely the Nyquist rate since in actual
practice it is impossible to sample at fs = 2fc unless one can guarantee there are absolutely no signal
components above fc. This can only be achieved by filtering the signal prior to sampling with a filter having
infinite rolloff. . . a physical impossibility, see Figure 2.
Figure 5. Spectral folding or aliasing caused by:
(a) under sampling
(b) exaggerated under sampling.
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Figure 6. Aliased spectral envelope (a) and (b) of
Figure 5a and Figure 5b respectively.
Figure 7. Generalized single channel sample data system.
5
The Sampling Theorem and Its Hardware
5.1
IMPLICATIONS
Though there are numerous sophisticated techniques of implementation, it is appropriate to re-emphasize
design of a sampled-data system. The method with which to achieve this goal will be to introduce a few of
the common perils encountered when implementing such a system. We begin by considering the
generalized block diagram of Figure 7.
As shown in Figure 7, prior to any signal processing manipulation the analog input signal must be
preconditioned to prevent aliasing and thereafter digitized to logic signals usable by the logic function
block. The antialiasing and digitizing functions are performed by an input filter and analog-to-digital
converter respectively. Once digitized the signal can then be altered or processed and upon completion,
reconstructed back to a continuous analog signal via a digital-to-analog converter followed by a smoothing
filter.
To this point no mention has been made concerning the sample and hold circuit block depicted in
Figure 7. In general the analog-to-digital converter can operate as a stand alone unit. In many high speed
operations however, the converter speed is insufficient and thus requires the assistance of a sample and
hold circuit. This will be discussed in detail further in the article.
5.1.1
The Antialiasing Input Filter
As indicated earlier in the text, the antialiasing filter should band-limit the input signal's spectrum to
frequencies no greater than the Nyquist frequency. In the real world however, filters are non-ideal and
have typical attenuation or band-limiting and phase characteristics as shown in Figure 8 . It must also be
realized that true band-limiting of a specific frequency spectrum is not possible. In the sample data system
band-limiting is achieved by attenuating those frequencies greater than the Nyquist frequency to a level
undetectable or invisible to the system analog-to-digital (A/D) converter. This level would typically be less
than the rms quantization noise level defined by the specific converter being used.
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(3) (4)
As an example of how an antialiasing filter would be applied, assume a sample data system having
within it an 8-bit A/D converter. Eight bits translates to 2n=28=256 levels of resolution. If a 2.56 volt
reference were used each quantization level, q, would represent the equivalent of 2.56 volts/256=10
millivolts. Realizing this the antialiasing filter would be designed such that frequencies in the stopband
were attenuated to less than the rms quantization noise level of
and thus appearing invisible to the
system. More specifically
(21)
It can be seen, for example in the Butterworth filter case (characterized as having a maximally flat passband) of Figure 9a that any order of filter may be used to achieve the −59 dB attenuation level, however,
the higher the order, the faster the roll off rate and the closer the filter magnitude response will approach
the ideal.
Referring back to Figure 8 it is observed that those frequencies greater than ωa are not recognized by the
A/D converter and thus the sampling frequency of the sample data system would be defined as ωs ≥ 2ωa.
Additionally, the frequencies present within the filtered input signal would be those less than ωa. Note
however, that the portion of the signal frequencies least distorted are those between ω=O and ωp and
those within the transition band are distorted to a substantial degree, though it was originally desired to
limit the signal to frequencies less than the cutoff ωp, because of the non-ideal frequency response the
true Nyquist frequency occurred at ωa. We see then that the sampled-data system could at most be
accurate for those frequencies within the antialiasing filter passband.
From the above example, the design of an antialiasing filter appears to be quite straight forward. Recall
however, that all waveforms are composed of the sums and differences of various frequency components
and as a result, if the response of the filter passband were not flat for the desired signal frequency
spectrum, the recovered signal would be an inaccurate summation of all frequency components altered by
their relative attenuations in the pass-band.
Additionally the antialiasing filter design should not neglect the effects of delay. As illustrated in Figure 8
and Figure 9b, delay time corresponds to a specific phase shift at a particular frequency. Similar to the flat
pass-band consideration, if the phase shift of the filter is not exactly proportional to the frequency, the
output of the filter will be a waveform in which the summation of all frequency components has been
altered by shifts in their relative phase. Figure 9b further indicates that contrary to the roll off rate, the
higher the filter order the more non-ideal the delay becomes (increased delay) and the result is a distorted
output signal.
A final and complex consideration to understand is the effects of sampling. When a signal is sampled the
end effect is the multiplication of the signal by a unit sampling pulse train as recalled from Figure 3a, c and
e. The resultant waveform has a spectrum that is the convolution of the signal spectrum and the spectrum
of the unit sample pulse train, i.e. Figure 3b, d, and f. If the unit sample pulse has the classical sin X/X
spectrum of a rectangular pulse, see Figure 13, then the convolution of the pulse spectrum with the signal
spectrum would produce the non-ideal sampled signal spectrum shown in Figure 10a, b, and c.
It should be realized that because of the band-limiting or filtering and delay response of the Sin X/X
function combined with the effects of the non-ideal antialiasing filter (i.e. non-flat pass-band and phase
shift) certain of the sum and difference frequency components may fall within the desired signal spectrum
thereby creating aliasing errors, Figure 10c.
When designing antialiasing filters it will be found that the closer the filter response approaches the ideal
the more complex the filter becomes. Along with this an increase in delay and pass-band ripple combine
to distort and alias the input signal. In the final analysis the design will involve trade offs made between
filter complexity, sampling speed and thus system bandwidth.
(3)
(4)
10
In order not to disrupt the flow of the discussion a list of filter terms has been presented in Appendix A.
For an explanation of quantization refer to section IV. B. of this article.
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(5)
Figure 8. Typical filter magnitude and phase versus frequency response.
a) Attenuation characteristics of a normalized Butterworth filter as a function of degree n.
(5)
This will be explained more clearly in Section IV. of this article.
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b) Group delay performances of normalized Butterworth lowpass filters as a function of degree n.
Figure 9.
Figure 10. (c) equals the convolution of (a) with (b).
5.1.2
The Analog-to-Digital Converter
Following the antialiasing filter is the A/D converter which performs the operations of quantizing and
coding the input signal in some finite amount of time. Figure 11 shows the quantization process of
converting a continuous analog input signal into a set of discrete output levels. A quantization, q, is thus
defined as the smallest step used in the digital
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representation of fq(n) where f(n) is the sample set of an input signal f(t) and is expressed by a finite
number of bits giving the sequence fq(n). Digitally speaking q is the value of the least significant code bit.
The difference signal ε(n) shown in Figure 11 is called quantization noise or error and can be defined as
ε(n) = f(n) − fq(n). This error is an irreducible one and is a function of the quantizing process. Its error
amplitude is dependent on the number of quantization levels or quantizer resolution and as shown, the
maximum quantization error is |q/2|.
Generally ε(n) is treated as a random error when described in terms of its probability density function, that
is, all values of ε(n) between q/2 and −q/2 are equally probable, then for the average value ε(n)avg=0 and
for the rms value
As a side note it is appropriate at this point to emphasize that all analog signals have some form of noise
corruption. If for example an input signal has a finite signal-to-noise ratio of 40dB it would be superfluous
to select an A/D converter with a high number of bits. It may be realized that the use of a large number of
bits does not give the digitized signal a higher signal-to-noise ratio than that of the original analog input
signal. As a supportive argument one may say that though the quantization steps q are very small with
respect to the peak input signal the lower order bits of the A/D converter merely provide a more accurate
Returning to our discussion, we define the conversion time as the time taken by the A/D converter to
convert the analog input signal to its equivalent quantization or digital code. The conversion speed
required in any particular application depends upon the time variation of the signal to be converted and the
amount of resolution or bits, n, required. Though the antialiasing filter helps to control the input signal time
rate of change by band-limiting its frequency spectrum, a finite amount of time is still required to make a
measurement or conversion. This time is generally called the aperture time and as illustrated in Figure 12
produces amplitude measurement uncertainty errors. The maximum rate of change detectable by an A/D
converter can simply be stated as
(22)
If for example V full scale = 10.24 volts, T conversion time = 10 ms, and n = 10 or 1024 bits of resolution
then the maximum rate of change resolvable by the A/D converter would be 1 volt/sec. If the input signal
has a faster rate of change than 1 volt/sec, 1 LSB changes cannot be resolved within the sampling period.
In many instances a sample-and-hold circuit may be used to reduce the amplitude uncertainty error by
measuring the input signal with a smaller aperture time than the conversion time aperture of the A/D
converter. In this case the maximum rate of change resolvable by the sample-and-hold would be
(23)
Note also that the actual calculated rate of change may be limited by the slew rate specification fo the
sample-and-hold in the track mode. Additionally it is very important to clarify that this does not imply
violating the sampling theorem in lieu of the increased ability to more accurately sample signals having a
fast time rate of change.
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Figure 11. Quantization error.
ΔV: AMPLITUDE UNCERTAINTY ERROR
ta: APERTURE TIME
Δta: APERTURE TIME UNCERTAINTY
Figure 12. Amplitude uncertainty as a function of (a) a nonvarying aperture and(b) aperture time
uncertainty.
An ideal sample-and-hold effectively takes a sample in zero time and with perfect accuracy holds the
value of the sample indefinitely. This type of sampler is also known as a zero order hold circuit and its
effect on a sample data system warrants some discussion.
It is appropriate to recall the earlier discussion that the spectrum of a sampled signal is one in which the
resultant spectrum is the product obtain by convolving the input signal spectrum with the sin X/X spectrum
of the sampling waveform. Figure 13 illustrates the frequency spectrum plotted from the Fourier transform
(24)
of a rectangular pulse. The sin X/X form occurs frequently in modern communication theory and is
commonly called the sampling function.
The magnitude and phase of a typical zero order hold sampler spectrum
(25)
is shown in Figure 14 and Figure 15 illustrates the spectra of various sampler pulse-widths. The purpose
of presenting this illustrative information is to give insight at to what effects cause the aliasing described in
Figure 10. From Figure 15 it is realized that the main lobe of the sin X/X function varies inversely
proportional with the sampler pulse-width. In other words a wide pulse-width, or in this case the aperture
window, acts as a low pass filtering function and limits the amount of information resolvable by the sample
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data system. On the other hand a narrow sampler pulse-width or aperture window has a broader main
lobe or band-width and thus when convolved with the analog input signal produces the least amount of
distortion. Understandably then the effect of the sampler's spectral phase and main lobe width must be
considered when developing a sampling system so that no unexpected aliasing occurs from its
convolution with the input signal spectrum.
Figure 13. The Fourier transform of the rectangular
pulse (a) is shown in (b).
Figure 14. Sampling Pulse (a), its Magnitude (b) and Phase Response (c).
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Figure 15. Pulse width and how it effects the sin X/X
envelop spectrum (normalized amplitudes).
5.1.3
The Digital-to-Analog Converter and Smoothing Filter
After a signal has been digitally conditioned by the signal processing unit of Figure 7, a D/A converter is
used to convert the sampled binary information back in to an analog signal. The conversion is called a
zero order hold type where each output sample level is a function of its binary weight value and is held
until the next sample arrives, see Figure 16. As a result of the D/A converter step function response it is
apparent that a large amount of undesirable high frequency energy is present. To eliminate this the D/A
converter is usually followed by a smoothing filter, having a cutoff frequency no greater than half the
sampling frequency. As its name suggests the filter output produces a smoothed version of the D/A
converter output which in fact is a convolved function. More simply said, the spectrum of the resulting
signal is the product of a step function sin X/X spectrum and the band-limited analog filter spectrum.
Analogous to the input sampling problem, the smoothed output may have aliasing effects resulting from
the phase and attenuation relations of the signal recovery system (defined as the D/A converter and
smoothing filter combination).
As a final note, the attenuation due to the D/A converter sin X/X spectrum shape may in some cases be
compensated for in the signal processing unit by pre-processing using a digital filter with an inverse
response X/sin X prior to D/A conversion. This allows an overall flat magnitude signal response to be
smoothed by the final filter.
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A Final Note
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Figure 16. (a) Processed signal data points (b) output of D/A converter (c) output of smoothing filter.
6
A Final Note
This article began by presenting an intuitive development of the sampling theorem supported by a
mathematical and illustrative proof. Following the theoretical development were a few of the unobvious
and troublesome results that develop when trying to put the sampling theorem into practice. The purpose
of presenting these thought provoking perils was to perhaps give the beginning designer some insight or
guidelines for consideration when developing a sample data system's interface.
7
Acknowledgements
The author wishes to thank James Moyer and Barry Siegel for their encouragement and the time they
8
ARTICLE REFERENCES
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•
•
•
•
•
•
•
•
•
•
•
S.D. Stearns, Digital Signal Analysis, Hayden, 1975
S.A. Tretter, Introduction to Discrete-Time Signal Processing, Wiley, 1976
W.D. Stanley, Digital Signal Processing, Reston, 1975
A. Papoulis, The Fourier Integral and its Applications, Mc-Graw-Hill, 1962
E.A. Robinson, M. T. Silvia, Digital Signal Processing and Time Series Analysis, Holden-Day, 1978
C.E. Shannon, “Communication in the Presence of Noise,” Proceedings IRE, Vol. 37, pp. 10–21, Jan.
1949
M. Schwartz, L. Shaw, Signal Processing: Discrete Spectral Analysis, Detection and Estimation,
McGraw-Hill, 1975
L.R. Rabiner, B. Gold, Theory and Application of Digital Signal Processing, Prentice-Hall, 1975
W.H. Hayt, J.E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 3rd edition, 1978
E.O. Brigham, The Fast Fourier Transform, Prentice-Hall, 1974
J. Sherwin, Specifying A/D and D/A converters, National Semiconductor Corp., Application Note AN156
Analog-Digital Conversion Notes, Analog Devices Inc., 1974
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ARTICLE REFERENCES
•
18
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A.I. Zverev, Handbook of Filter Synthesis, Wiley, 1967
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Appendix A Basic Filter Concepts
A filter is a network used for separating signal waves on the basis of their frequency and is usually
composed of passive, reactive and active elements such as resistors, capacitors, inductors, and
amplifiers, or combinations thereof.
There are basically five types of filters used to pass or reject such signals and they are defined as follows:
1. A low-pass filter passes a band of frequencies called the passband, ranging from zero frequency or DC
to a certain cutoff frequency, ωc, and in addition has a maximum attenuation or ripple level of AMAX
within the passband. See Figure 17.
• Frequencies beyond the ωc may have an attenuation greater than AMAX but beyond a specific
frequency ωs defined as the stopband frequency, a minimum attenuation of AMIN must prevail. The
band of frequencies higher than ωs and maintaining attenuation greater than or equal to AMIN is
called the stopband. The transition region or transition band is that band of frequencies between
ωc and ωs.
(6)
Figure 17. Common Low Pass Filter Response
A high-pass filter allows frequencies above the passband frequency, ωc, to pass and rejects frequencies
below this point. AMAX must be maintained in the passband and frequencies equal to and below the
stopband frequency, ωs, must have a minimum attenuation of AMIN. See Figure 18.
Figure 18. Common High Pass Filter Response
A bandpass filter performs the function of passing a specific band of frequencies while rejecting those
frequencies above and below ωc2 and lower, ωc1 cutoff frequency limits. See Figure 19.
As in the previous two cases the passband is required to sustain an attenuation of AMAX, and the stopband
of frequencies above and below ωs2 and ωs2 respectively, must have a minimum attenuation of AMIN.
(6)
Recall that the radian frequency ω=2πf.
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Figure 19. Common Band-pass Filter Response
A band-reject filter or notch filter allows all but a specific band of frequencies to pass. As shown in
Figure 4, those frequencies between ωs1 and ωs2 are filtered out and those frequencies above and below
ωc2 and ωc1 respectively are passed. The attenuation requirements of the stopband AMIN and passband
AMAX must still hold.
Figure 20. Common Band-Reject Filter Response
An all-pass or phase shift filter allows all frequencies to pass without any appreciable attenuation. It further
introduces a predictable phase shift to all frequencies passed, though not restricting the entire range of
frequencies to a specific phase shift (i.e., a phase shift may be imposed upon a selected band of
frequencies and appear invisible to all others).
20
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