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The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
This presentation explains about the introduction of Nyquist Stability criterion. It clearly shows advantages and disadvantages of Nyquist Stability criterion and also explains importance of Nyquist Stability criterion and steps required to sketch the Nyquist plot. It explains about the steps required to sketch Nyquist plot clearly. It also explains about sketching Nyquist plot and determines the stability by using Nyquist Stability criterion with an example.
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A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM FOR MAXIMUM OF 50 DEG PHASE ANGLE
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The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
This presentation explains about the introduction of Nyquist Stability criterion. It clearly shows advantages and disadvantages of Nyquist Stability criterion and also explains importance of Nyquist Stability criterion and steps required to sketch the Nyquist plot. It explains about the steps required to sketch Nyquist plot clearly. It also explains about sketching Nyquist plot and determines the stability by using Nyquist Stability criterion with an example.
CONTROL SYSTEMS PPT ON A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM ...sanjay kumar pediredla
A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM FOR MAXIMUM OF 50 DEG PHASE ANGLE
THIS PPT IS SO USEFUL FOR THE ENGINEERING STUDENTS FOR CONTROL SYSTEMS STUDENTS AND THIS PPT ALSO CONTAINS A MATLAB CODING FOR THE LEAD COMPENSATOR AND THE RESULTS ARE ALSO PLOTTED IN THAT PPT AND THE PROBLEM CAN ALSO BE SOLVED BY USING THE DATA IN PPT AND IT IS SO USEFUL PPT
This presentation explains about the introduction of Polar Plot, advantages and disadvantages of polar plot and also steps to draw polar plot. and also explains about how to draw polar plot with an examples. It also explains how to draw polar plot with numerous examples and stability analysis by using polar plot.
Part of Lecture Series on Automatic Control Systems delivered by me to Final year Diploma in Engg. Students. Equally useful for higher level. Easy language and step by step procedure for drawing Bode Plots. Three illustrative examples are included.
This presentation explains about the introduction of Polar Plot, advantages and disadvantages of polar plot and also steps to draw polar plot. and also explains about how to draw polar plot with an examples. It also explains how to draw polar plot with numerous examples and stability analysis by using polar plot.
Part of Lecture Series on Automatic Control Systems delivered by me to Final year Diploma in Engg. Students. Equally useful for higher level. Easy language and step by step procedure for drawing Bode Plots. Three illustrative examples are included.
A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts
Signals and Systems is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, defense electronics, consumer electronics, and consumer products.
Digital Signal Processing[ECEG-3171]-Ch1_L05Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Sampling is a Simple method to convert analog signal into discrete Signal by using any one of its three methods
if the sampling frequency is twice or greater than twice then sampled signal can be convert back into analog signal easily......
Signal and System, CT Signal DT Signal, Signal Processing(amplitude and time ...Waqas Afzal
Signal and System(definitions)
Continuous-Time Signal
Discrete-Time Signal
Signal Processing
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Classification of Signals
Basic Signal Operations(amplitude and time scaling)
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Have the message received by managers and peers along with a test email for review
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In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
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Speakers:
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Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
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SAP heatmap example with demo
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
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Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
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Assuring Contact Center Experiences for Your Customers With ThousandEyes
Lecture9
1. Lecture 10: Sampling Discrete-Time Systems
4 Sampling & Discrete-time systems (2 lectures):
Sampling theorem, discrete Fourier transform
Specific objectives for today:
• Sampling of a continuous-time signal
• Reconstruction of the signals from its samples
• Sampling theorem & Nyquist rate
• Reconstruction of a signal, using zero-order holds
2. Lecture 10: Resources
Core reading
SaS, O&W, C7
Background reading
MIT Lectures 9 & 13
In these two lectures, we’re going to develop the DT
Fourier transform as a sampled version of the CT
Fourier transform.
Note that this is opposite to time-domain convolution
where we developed CT convolution as the limiting
case of DT convolution
3. Sampling is the transformation of a continuous signal
into a discrete signal
Widely applied in digital analysis systems
1. Sample the continuous time signal
2. Design and process discrete time signal
3. Convert back to continuous time
What is Discrete Time Sampling?
x(t),
x[n]
t=nT
Discrete
Time sampler
Discrete
time system
Signal
reconstruction
x(t) x[n] y[n] y(t)
T is the sampling
period
4. Why is Sampling Important?
For many systems (e.g. Matlab, …) designing and
processing discrete-time systems is more efficient and
more general compared to performing continuous-time
system design.
How does Simulink perform continuous-time system
simulation?
The signals are sampled and the systems are approximately
integrated in discrete time
Mainly due to the dramatic development of digital technology
resulting in inexpensive, lightweight, programmable and
reproducible discrete-time systems. Widely used for
communication
5. Sampling a Continuous-Time Signal
Clearly for a finite sample period T, it is not possible to represent
every uncountable, infinite-dimensional continuous-time signal
with a countable, infinite-dimensional discrete-time signal.
In general, an infinite number of CT signals can generate a DT
signal.
However, if the signal is band (frequency) limited, and the
samples are sufficiently close, it is possible to uniquely
reconstruct the original CT signal from the sampled signal
x1(t),
x2(t),
x3(t),
x[n]
t=nT
6. Definition of Impulse Train Sampling
We need to have a convenient way in
which to represent the sampling of a
CT signal at regular intervals
A common/useful way to do this is
through the use of a periodic impulse
train signal, p(t), multiplied by the CT
signal
T is the sampling period
ωs=2π/T is the sampling frequency
This is known as impulse train
sampling. Note xp(t) is still a
continuous time signal
∑
∞
−∞=
−=
=
n
p
nTttp
tptxtx
)()(
)()()(
δ T
7. Analysing Impulse Train Sampling (i)
What effect does this sampling have on the frequency decomposition
(Fourier transform) of the CT impulse train signal xp(t)?
By definition:
The signal p(t) is periodic and the coefficients of the Fourier
Series are given by:
Therefore, the Fourier transform is given by
One property of the Fourier transform we did not consider is the
multiplicative property which says if xp(t) = x(t)p(t), then
∑∑
∞
−∞=
∞
−∞=
−=−=
nn
p nTtnTxnTttxtx )()()()()( δδ
∫
∞
∞−
−= θθωθω π djPjXjX p ))(()()( 2
1
∑
∞
−∞=
−=
k
sT kjP )()( 2
ωωδω π
kdteta T
T
T
tjk
Tk ∀== ∫−
−
,)( 1
2/
2/
1 0ω
δ
8. Analysing Impulse Train Sampling (ii)
Substituting for P(jω)
Therefore Xp(jω) is a periodic function of ω, consisting of a
superposition of shifted replicas of X(jω), scaled by 1/T.
∑
∑ ∫
∫ ∑
∞
−∞=
∞
−∞=
∞
∞−
∞
∞−
∞
−∞=
−=
−−=
−−=
k sT
k sT
k sTp
kjX
dkjX
dkjXjX
))((
))(()(
))(()()(
1
1
1
ωω
θθωωδθ
θθωωδθω
|X(jω)|=0: |ω|>1 ωs=3
9. Reconstruction of the CT Signal
When the sampling frequency
ωs is less than twice the
band-limited frequency ωM,
there is no overlaps the
spectrum X(jω)
If this is true, the original
signal x(t) can be
recovered from the impulse
sampled xp(t), by passing it
through a low pass filter
H(jω) with gain T and cutoff
frequency between ωM and
ωs-ωM.
1
1/T
T
1
ωM=1
ωs=3
10. Sampling Theorem
Let x(t) be a band (frequency)-limited signal
X(jω) = 0 for |ω|>ωM.
Then x(t) is uniquely determined by its samples {x(nT)}
when the sampling frequency satisfies:
where ωs=2π/T.
2ωM is known as the Nyquist rate, as it represents the
largest frequency that can be reproduced with the sample
time
The result makes sense because a frequency-limited signal
has a limited amount of information that can be fully
captured with the sampled sequence {x(nT)}
Ms ωω 2>
X(jω)
ωM ω
ωs
-ωM
11. Zero Order Hold Sampling
A zero order hold is a common method
for bridging CT-DT signals
A zero order hold samples the current
signal and holds that value until the
next sample
In most systems, it is difficult to
generate and transmit narrow, large-
amplitude pulses (impulse train
sampling) xp(t)
We can often use a variety of
filtering/interpolation techniques to
reconstruct the original time-domain
signal, however often the zero-order
hold signal x0(t) is sufficiently
accurate
xp(t)
x0(t)
x(t)
t
t
t
12. Lecture 10: Summary
The sample time for converting a continuous time signal into
a sampled, discrete time signal is determined by the
Nyquist rate, amongst other things.
The signal must satisfy the relationship:
If the signal is to be preserved exactly. Information in
frequencies higher than this will be lost when the signal is
sampled.
A continuous time signal is often sampled and communicated
using a zero order hold
Often this is enough to be considered as the re-constructed
continuous time signal, but sometimes approximate
methods for re-constructing the signal are used
Ms ωω 2>