The document introduces Bode plots, which graphically represent the magnitude and phase of a system's transfer function over frequency. Bode plots are constructed by: 1) Determining the transfer function, 2) Rewriting it in standard form, 3) Plotting the contribution of each term. Terms contribute horizontal, positively sloped, or negatively sloped lines to the magnitude plot, and constant, increasing, or decreasing phase shifts to the phase plot. Examples demonstrate constructing Bode plots for different transfer functions by superimposing the effects of individual terms.
This document discusses Bode plots, which are used to analyze the stability of linear time-invariant control systems. Bode plots graphically represent a system's transfer function and consist of a magnitude plot and a phase plot versus frequency. The magnitude plot shows the gain in decibels and the phase plot shows the phase angle. Together these plots can determine the gain and phase margins of a system, which indicate its stability. Examples are provided to demonstrate how to construct Bode plots from transfer functions and analyze system stability.
The document discusses Bode plots, which are frequency domain techniques used to analyze linear time-invariant systems. It covers poles and zeros, transfer functions, the S-plane, mechanics for constructing Bode plots, examples of plotting Bode plots by hand and using MATLAB, and designing a system to meet a target Bode plot specification. Key steps include identifying poles and zeros, approximating plots between break frequencies, and using MATLAB tools like Bode and Simulink to validate designs.
The document discusses Bode plots, which are used to analyze the frequency response of systems. It provides details on:
- Types of Bode plots including magnitude and phase plots
- Factors that affect the plots, such as poles and zeros
- Characteristics of different factors, such as a constant term resulting in a flat magnitude plot and zero phase shift
- Key aspects like corner frequencies and slope changes in the plots
- How to determine a transfer function from a given Bode plot
The document provides examples and step-by-step explanations to illustrate how to interpret Bode plots and derive the transfer function they represent.
The document discusses first order active RC sections and their transfer functions. It can be summarized as follows:
1) First order active RC sections have a transfer function of the form K/(s+z1)/(s+p1), where z1 is the zero and p1 is the pole.
2) Depending on the locations of z1 and p1, the magnitude response can be low pass, high pass, or all pass. A minimum phase response has z1 and p1 in the left half plane, while a non-minimum phase response has zeros in the right half plane.
3) Bode plots can be constructed by considering the individual effects of the zero and pole on magnitude (20
1. The document discusses various 2D and 3D transformations including translation, scaling, rotation, reflection, and shearing.
2. It explains the basic transformation types including rigid-body, affine, and free-form and provides examples of each.
3. Key concepts covered include the homogeneous coordinate system, composition of transformations using matrices, and expressing transformations like translation, scaling, and rotation using matrices.
The document discusses frequency response methods for analyzing dynamic systems. It provides the following key points:
- Frequency response analysis considers the steady-state response of a system to sinusoidal inputs, rather than step or ramp inputs. The frequency response can be determined experimentally or from the system's transfer function.
- A system's frequency response is its output amplitude and phase shift relative to the input signal, as the input frequency varies. For linear systems, the output is a sinusoid at the same frequency as the input.
- Bode plots are commonly used to graphically represent the frequency response of a system. They show the magnitude and phase shift of the system as a function of frequency.
This presentation explains about the introduction of Bode Plot, advantages of bode plot and also steps to draw Bode plot (Magnitude plot and phase plot). It explains basic or key factors used for drawing Bode plot. It also explains how to determine Magnitude, phase and slope for basic factors. It also explains how to determine stability by using Bode Plot and also how to determine Gain Crossover Frequency and Phase Crossover Frequency, Gain Margin and Phase Margin. It also explains drawing Bode plot with an example and also determines stability by using Bode Plot and also determines Gain Crossover Frequency and Phase Crossover Frequency, Gain Margin and Phase Margin.
The document presents information on frequency response systems and Bode plots. It defines frequency response as a measure of the output spectrum of a system in response to a stimulus. A Bode plot is a graphical representation of a system's frequency response in terms of gain and phase shift. It shows the logarithm of the magnitude and phase angle as functions of frequency. The document discusses different system types (0, 1, 2) and how to identify them based on the slope of the log magnitude curve at different frequencies. It also explains the impact of different transfer function components like constants, poles, and zeros on the shape of Bode plots.
This document discusses Bode plots, which are used to analyze the stability of linear time-invariant control systems. Bode plots graphically represent a system's transfer function and consist of a magnitude plot and a phase plot versus frequency. The magnitude plot shows the gain in decibels and the phase plot shows the phase angle. Together these plots can determine the gain and phase margins of a system, which indicate its stability. Examples are provided to demonstrate how to construct Bode plots from transfer functions and analyze system stability.
The document discusses Bode plots, which are frequency domain techniques used to analyze linear time-invariant systems. It covers poles and zeros, transfer functions, the S-plane, mechanics for constructing Bode plots, examples of plotting Bode plots by hand and using MATLAB, and designing a system to meet a target Bode plot specification. Key steps include identifying poles and zeros, approximating plots between break frequencies, and using MATLAB tools like Bode and Simulink to validate designs.
The document discusses Bode plots, which are used to analyze the frequency response of systems. It provides details on:
- Types of Bode plots including magnitude and phase plots
- Factors that affect the plots, such as poles and zeros
- Characteristics of different factors, such as a constant term resulting in a flat magnitude plot and zero phase shift
- Key aspects like corner frequencies and slope changes in the plots
- How to determine a transfer function from a given Bode plot
The document provides examples and step-by-step explanations to illustrate how to interpret Bode plots and derive the transfer function they represent.
The document discusses first order active RC sections and their transfer functions. It can be summarized as follows:
1) First order active RC sections have a transfer function of the form K/(s+z1)/(s+p1), where z1 is the zero and p1 is the pole.
2) Depending on the locations of z1 and p1, the magnitude response can be low pass, high pass, or all pass. A minimum phase response has z1 and p1 in the left half plane, while a non-minimum phase response has zeros in the right half plane.
3) Bode plots can be constructed by considering the individual effects of the zero and pole on magnitude (20
1. The document discusses various 2D and 3D transformations including translation, scaling, rotation, reflection, and shearing.
2. It explains the basic transformation types including rigid-body, affine, and free-form and provides examples of each.
3. Key concepts covered include the homogeneous coordinate system, composition of transformations using matrices, and expressing transformations like translation, scaling, and rotation using matrices.
The document discusses frequency response methods for analyzing dynamic systems. It provides the following key points:
- Frequency response analysis considers the steady-state response of a system to sinusoidal inputs, rather than step or ramp inputs. The frequency response can be determined experimentally or from the system's transfer function.
- A system's frequency response is its output amplitude and phase shift relative to the input signal, as the input frequency varies. For linear systems, the output is a sinusoid at the same frequency as the input.
- Bode plots are commonly used to graphically represent the frequency response of a system. They show the magnitude and phase shift of the system as a function of frequency.
This presentation explains about the introduction of Bode Plot, advantages of bode plot and also steps to draw Bode plot (Magnitude plot and phase plot). It explains basic or key factors used for drawing Bode plot. It also explains how to determine Magnitude, phase and slope for basic factors. It also explains how to determine stability by using Bode Plot and also how to determine Gain Crossover Frequency and Phase Crossover Frequency, Gain Margin and Phase Margin. It also explains drawing Bode plot with an example and also determines stability by using Bode Plot and also determines Gain Crossover Frequency and Phase Crossover Frequency, Gain Margin and Phase Margin.
The document presents information on frequency response systems and Bode plots. It defines frequency response as a measure of the output spectrum of a system in response to a stimulus. A Bode plot is a graphical representation of a system's frequency response in terms of gain and phase shift. It shows the logarithm of the magnitude and phase angle as functions of frequency. The document discusses different system types (0, 1, 2) and how to identify them based on the slope of the log magnitude curve at different frequencies. It also explains the impact of different transfer function components like constants, poles, and zeros on the shape of Bode plots.
I am Martina J. I am a Signals and Systems Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, from the University of Maryland. I have been helping students with their assignments for the past 9 years. I solve assignments related to Signals and Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems assignments.
Bode plots show the magnitude and phase response of a system as functions of frequency. They can be approximated as a sequence of straight lines called asymptotes. For a transfer function G(s) = (s + a), the Bode plot has:
1) A low-frequency asymptote of 20 log(a) where the magnitude is constant.
2) At the break frequency a, the phase reaches -45 degrees.
3) A high-frequency asymptote where the magnitude decreases at -20dB/decade and the phase reaches -90 degrees.
Normalizing the transfer function allows the break frequency to appear at 1 rad/sec on the plot. Bode plots
I am Grey Nolan. Currently associated with matlabassignmentexperts.com as an assignment helper. After completing my master's from the University of British Columbia, I was in search for an opportunity that expands my area of knowledge hence I decided to help students with their Signals and Systems assignments. I have written several assignments till date to help students overcome numerous difficulties they face in Signals and Systems Assignments.
Diffusion Schrödinger bridges for score-based generative modelingJeremyHeng10
Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).
Modern Control - Lec 05 - Analysis and Design of Control Systems using Freque...Amr E. Mohamed
The document discusses frequency response analysis and Bode plots. It begins with an introduction to frequency response and how the steady state response of a linear time-invariant system to a sinusoidal input is another sinusoid at the same frequency with a different magnitude and phase. The complex ratio of the output to input is called the frequency response. It then discusses Bode plots which show the magnitude and phase of the frequency response on logarithmic scales. Key features of components in open-loop transfer functions and how they affect the Bode plot shapes are explained. An example demonstrates drawing the Bode plots for a sample transfer function.
Diffusion Schrödinger bridges for score-based generative modelingJeremyHeng10
Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).
The document discusses issues with pinning and facetting in lattice Boltzmann simulations of multiphase flows. It presents a lattice Boltzmann model for propagating sharp interfaces using a phase field approach. Sharpening the phase field interface causes it to become pinned to the lattice or develop facets. Introducing randomness via a random projection method or random threshold prevents pinning and delays facetting, allowing the interface to propagate at the correct speed even for very sharp boundaries.
EE301 Lesson 15 Phasors Complex Numbers and Impedance (2).pptRyanAnderson41811
This document covers phasors, complex numbers, and their application to representing alternating current (AC) signals. It defines phasors as rotating vectors used to represent sinusoids, and complex numbers as numbers with real and imaginary parts that allow representing phasors. The document explains how to convert between polar and rectangular complex number forms, and how to perform operations like addition, subtraction, multiplication and division on complex numbers. It then discusses using phasors to model AC voltages and currents by transforming them into the frequency domain using complex numbers. Finally, it covers topics like phase difference between waveforms and using phasors to understand phase relationships between AC signals.
The document discusses logical design and analysis of combinational circuits using logic gates. It covers topics such as logic gates, synchronous vs asynchronous circuits, circuit analysis, implementing switching functions using data selectors, priority encoders, decoders, multiplexers, demultiplexers and other basic digital components. Examples are provided to illustrate circuit design and analysis techniques for combinational logic circuits.
Problem Solving by Computer Finite Element MethodPeter Herbert
This document discusses using finite element methods and the cotangent Laplacian to solve partial differential equations numerically. It begins by explaining how to generate simplicial meshes by dividing a region into basic pieces. It then introduces the cotangent Laplacian, which approximates the Laplacian operator, and how it is calculated based on angles in triangles. Finally, it demonstrates applying the cotangent Laplacian to solve sample Dirichlet and Neumann boundary value problems and compares the approximate solutions to exact solutions, showing convergence as the mesh is refined.
I am Bing Jr. I am a Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab Deakin University, Australia. I have been helping students with their assignments for the past 9 years. I solve assignments related to Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Signal Processing Assignments.
ALC-Prac-10A.pdfMAT2ALC Practice Class 10A Turing Machines.docxnettletondevon
ALC-Prac-10A.pdf
MAT2ALC Practice Class 10A Turing Machines (TM)
1. Below is a doubling machine (see Example 9.2.2 in NGV). It has input
alphabet {0} and tape alphabet {0,a,2} and implements the string
function f : 0∗ → 0∗ defined by f(0n) = 02n.
q0 q1
q2q3
0 7→ (a,L)
2 7→ (0,R)
a 7→ (0,R)
2 7→ (2,L)
(0 7→ (0,L)
(0 7→ (0,R)
(a) Write down the transition table for this Turing machine.
(b) (i) Complete the following configuration notation description
(q0, 00) → (q1,2a0) → (q2, 0a0) → . . .
of the processing of the word 00.
(ii) How many “moves” were required to do the computation in (i)?
(Count the arrows in the configuration notation.)
(c) Suppose the machine is processing the word 0n for some n ≥ 1.
(i) What is on the tape when the machine first reaches state q2?
(Don’t forget to underline of the read/write cell.)
(ii) What is on the tape the second time the machine reaches q2?
(iii) What is on the tape the next time the machine reaches q2?
(iv) What is on the tape the last time the machine reaches q2?
(v) What is on the tape the last time the machine reaches q0?
(vi) Which state is the machine in when processing halts?
(d) Use configuration notation to discover what happens if this machine
is started with only blanks on the tape, so the initial configuration
looks something like (q0,222).
2. In this question we design a TM with input alphabet Σ = {0, 1} and
alphabet Γ = {0, 1,2} that adds 1 to a binary number x written in the
usual way on the tape. We will develop a machine by tackling one task
at a time.
(a) On paper, add 1 to the binary numbers 110, 101, 1001 and 111.
Does this suggest a simple algorithm for adding 1 to x?
(b) When adding 1 to x we start at the right most digit. Design a TM
with two states that moves to the last digit of x and halts in an
accepting state. (Move right until a blank is found and then move
left once.)
(c) (i) What is the effect of adding 1 to a binary number with last
digit 0?
(ii) Extend the machine of (a) to one that adds 1 if x ends with 0
and (for now) rejects x if it ends with 1.
(d) (i) When we add 1 to a number with final digit 1, what happens
to the final digit?
(ii) Extend the machine to one that gives the correct final digit 1
when x ends with 1 and moves to a new “carry” state C.
(e) (i) By adding 1 to the binary number 1011, decide how the ma-
chine should behave when it is in state C.
(ii) Add transitions to the machine that implement this.
(f) (i) Does the machine work correctly if all digits in x are 1?
(ii) Add a transition to the machine that rectifies this.
3. The strategy of the TM of Question 1 is to place a marker a at the left
of the input word and move it to the left as new 0’s are added at the left
of the tape contents. The following machine attempts to modify this
strategy by again placing a marker a at the left of the input and moving
it to the right as new 0’s are added on the right of the tape contents.
q0 q1 q2 q3
0 7→ (a,R) 2 7→ (0,L) 2 7→ (2,L.
This document provides an overview of Laplace transforms. Key points include:
- Laplace transforms convert differential equations from the time domain to the algebraic s-domain, making them easier to solve. The process involves taking the Laplace transform of each term in the differential equation.
- Common Laplace transforms of functions are presented. Properties such as linearity, differentiation, integration, and convolution are also covered.
- Partial fraction expansion is used to break complex fractions in the s-domain into simpler forms with individual terms that can be inverted using tables of transforms.
- Solving differential equations using Laplace transforms follows a standard process of taking the Laplace transform of each term, rewriting the equation in the s-domain, solving
Conversion of transfer function to canonical state variable modelsJyoti Singh
Realization of transfer function into state variable models is needed even if the control system design based on frequency-domain design method.
In these cases the need arises for the purpose of transient response simulation.
But there is not much software for the numerical inversion of Laplace transform.
So one ways is to convert transfer function of the system to state variable description and numerically integrating the resulting differential equations rather than attempting to compute the inverse Laplace transform by numerical method.
A Simple Communication System Design Lab #3 with MATLAB SimulinkJaewook. Kang
This document outlines the schedule and topics for a series of labs on communication system design using MATLAB Simulink. The upcoming Lab #3 will cover phase splitting, which extracts the real and imaginary components from a complex baseband signal, and up/down conversion, which shifts signals between baseband and intermediate frequencies. The lab is scheduled for April 1st from 1-4pm and will be instructed by Jaewook Kang. Previous and future labs will cover topics like OFDM, S-function design, channel modeling, and subsystem implementation.
The document appears to be a sample paper for the EC-GATE-2013 exam, containing 23 multiple choice questions related to electrical engineering concepts.
The questions cover topics such as logic gates, vector calculus, impulse response of systems, pn junction diodes, oxidation rates in IC technology, approximations of trigonometric functions, divergence of vector fields, Bode plots, op-amps, resistor networks, microprocessor programs, digital modulation, sampling theory, MOSFET characteristics, properties of stable linear time-invariant systems, matrix eigenvalues, polynomial roots, circuit analysis using Laplace transforms, and AC circuit analysis.
Each question is followed by a short explanation of the answer. The document serves as a practice test to
This document discusses Fourier series and integrals. It begins by explaining Fourier series using sines, cosines, and exponentials to represent periodic functions. Square waves are given as examples that can be expressed as infinite combinations of sines. Any periodic function can be expressed as a Fourier series. Fourier series are then derived for specific examples, including a square wave, repeating ramp, and up-down train of delta functions. Cosine series are also discussed. The document concludes by deriving the Fourier series for the delta function.
Varibale frequency response lecturer 2 - audio+Jawad Khan
This document discusses Bode plots, which are used to analyze the frequency response of variable-frequency networks. Bode plots consist of two graphs: a magnitude plot using a logarithmic scale on the horizontal axis to show gain or attenuation over frequency, and a phase plot to show phase shift over frequency. Zeros cause positive gain slopes on the magnitude plot, while poles cause negative slopes. Phase is determined from the tangent inverse of the transfer function. Bode plots provide an efficient way to understand a network's behavior at different frequencies.
I am Simon M. I am a Signals and Systems Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, from The University, of Houston, USA. I have been helping students with their assignments for the past 15 years. I solve assignments related to Signals and Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems Assignment.
text book Programmable-Logic-Controllers plc.pdfMahamad Jawhar
This document provides an overview and introduction to programmable logic controllers (PLCs). It discusses the basic parts and components of a PLC including the input/output section, central processing unit, memory, and programming devices. It also describes the basic principles of how PLCs operate by scanning inputs, executing a user-created program, and updating outputs. The document is intended to familiarize readers with the basic concepts and components of PLCs.
Kurdistan Regional Government Iraq Ministry of Electricity generates electricity primarily from fossil fuels such as natural gas. Renewable energy sources such as hydro, solar, wind, and geothermal currently account for a smaller portion of electricity generation but their use is growing. Geothermal energy harnesses heat from within the earth through technologies such as hydrothermal, geopressure, and hot dry rock systems to generate electricity without directly burning fuels. Geothermal resources in the Kurdistan region show potential for development to increase renewable energy supply and reduce dependence on fossil fuels.
I am Martina J. I am a Signals and Systems Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, from the University of Maryland. I have been helping students with their assignments for the past 9 years. I solve assignments related to Signals and Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems assignments.
Bode plots show the magnitude and phase response of a system as functions of frequency. They can be approximated as a sequence of straight lines called asymptotes. For a transfer function G(s) = (s + a), the Bode plot has:
1) A low-frequency asymptote of 20 log(a) where the magnitude is constant.
2) At the break frequency a, the phase reaches -45 degrees.
3) A high-frequency asymptote where the magnitude decreases at -20dB/decade and the phase reaches -90 degrees.
Normalizing the transfer function allows the break frequency to appear at 1 rad/sec on the plot. Bode plots
I am Grey Nolan. Currently associated with matlabassignmentexperts.com as an assignment helper. After completing my master's from the University of British Columbia, I was in search for an opportunity that expands my area of knowledge hence I decided to help students with their Signals and Systems assignments. I have written several assignments till date to help students overcome numerous difficulties they face in Signals and Systems Assignments.
Diffusion Schrödinger bridges for score-based generative modelingJeremyHeng10
Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).
Modern Control - Lec 05 - Analysis and Design of Control Systems using Freque...Amr E. Mohamed
The document discusses frequency response analysis and Bode plots. It begins with an introduction to frequency response and how the steady state response of a linear time-invariant system to a sinusoidal input is another sinusoid at the same frequency with a different magnitude and phase. The complex ratio of the output to input is called the frequency response. It then discusses Bode plots which show the magnitude and phase of the frequency response on logarithmic scales. Key features of components in open-loop transfer functions and how they affect the Bode plot shapes are explained. An example demonstrates drawing the Bode plots for a sample transfer function.
Diffusion Schrödinger bridges for score-based generative modelingJeremyHeng10
Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).
The document discusses issues with pinning and facetting in lattice Boltzmann simulations of multiphase flows. It presents a lattice Boltzmann model for propagating sharp interfaces using a phase field approach. Sharpening the phase field interface causes it to become pinned to the lattice or develop facets. Introducing randomness via a random projection method or random threshold prevents pinning and delays facetting, allowing the interface to propagate at the correct speed even for very sharp boundaries.
EE301 Lesson 15 Phasors Complex Numbers and Impedance (2).pptRyanAnderson41811
This document covers phasors, complex numbers, and their application to representing alternating current (AC) signals. It defines phasors as rotating vectors used to represent sinusoids, and complex numbers as numbers with real and imaginary parts that allow representing phasors. The document explains how to convert between polar and rectangular complex number forms, and how to perform operations like addition, subtraction, multiplication and division on complex numbers. It then discusses using phasors to model AC voltages and currents by transforming them into the frequency domain using complex numbers. Finally, it covers topics like phase difference between waveforms and using phasors to understand phase relationships between AC signals.
The document discusses logical design and analysis of combinational circuits using logic gates. It covers topics such as logic gates, synchronous vs asynchronous circuits, circuit analysis, implementing switching functions using data selectors, priority encoders, decoders, multiplexers, demultiplexers and other basic digital components. Examples are provided to illustrate circuit design and analysis techniques for combinational logic circuits.
Problem Solving by Computer Finite Element MethodPeter Herbert
This document discusses using finite element methods and the cotangent Laplacian to solve partial differential equations numerically. It begins by explaining how to generate simplicial meshes by dividing a region into basic pieces. It then introduces the cotangent Laplacian, which approximates the Laplacian operator, and how it is calculated based on angles in triangles. Finally, it demonstrates applying the cotangent Laplacian to solve sample Dirichlet and Neumann boundary value problems and compares the approximate solutions to exact solutions, showing convergence as the mesh is refined.
I am Bing Jr. I am a Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab Deakin University, Australia. I have been helping students with their assignments for the past 9 years. I solve assignments related to Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Signal Processing Assignments.
ALC-Prac-10A.pdfMAT2ALC Practice Class 10A Turing Machines.docxnettletondevon
ALC-Prac-10A.pdf
MAT2ALC Practice Class 10A Turing Machines (TM)
1. Below is a doubling machine (see Example 9.2.2 in NGV). It has input
alphabet {0} and tape alphabet {0,a,2} and implements the string
function f : 0∗ → 0∗ defined by f(0n) = 02n.
q0 q1
q2q3
0 7→ (a,L)
2 7→ (0,R)
a 7→ (0,R)
2 7→ (2,L)
(0 7→ (0,L)
(0 7→ (0,R)
(a) Write down the transition table for this Turing machine.
(b) (i) Complete the following configuration notation description
(q0, 00) → (q1,2a0) → (q2, 0a0) → . . .
of the processing of the word 00.
(ii) How many “moves” were required to do the computation in (i)?
(Count the arrows in the configuration notation.)
(c) Suppose the machine is processing the word 0n for some n ≥ 1.
(i) What is on the tape when the machine first reaches state q2?
(Don’t forget to underline of the read/write cell.)
(ii) What is on the tape the second time the machine reaches q2?
(iii) What is on the tape the next time the machine reaches q2?
(iv) What is on the tape the last time the machine reaches q2?
(v) What is on the tape the last time the machine reaches q0?
(vi) Which state is the machine in when processing halts?
(d) Use configuration notation to discover what happens if this machine
is started with only blanks on the tape, so the initial configuration
looks something like (q0,222).
2. In this question we design a TM with input alphabet Σ = {0, 1} and
alphabet Γ = {0, 1,2} that adds 1 to a binary number x written in the
usual way on the tape. We will develop a machine by tackling one task
at a time.
(a) On paper, add 1 to the binary numbers 110, 101, 1001 and 111.
Does this suggest a simple algorithm for adding 1 to x?
(b) When adding 1 to x we start at the right most digit. Design a TM
with two states that moves to the last digit of x and halts in an
accepting state. (Move right until a blank is found and then move
left once.)
(c) (i) What is the effect of adding 1 to a binary number with last
digit 0?
(ii) Extend the machine of (a) to one that adds 1 if x ends with 0
and (for now) rejects x if it ends with 1.
(d) (i) When we add 1 to a number with final digit 1, what happens
to the final digit?
(ii) Extend the machine to one that gives the correct final digit 1
when x ends with 1 and moves to a new “carry” state C.
(e) (i) By adding 1 to the binary number 1011, decide how the ma-
chine should behave when it is in state C.
(ii) Add transitions to the machine that implement this.
(f) (i) Does the machine work correctly if all digits in x are 1?
(ii) Add a transition to the machine that rectifies this.
3. The strategy of the TM of Question 1 is to place a marker a at the left
of the input word and move it to the left as new 0’s are added at the left
of the tape contents. The following machine attempts to modify this
strategy by again placing a marker a at the left of the input and moving
it to the right as new 0’s are added on the right of the tape contents.
q0 q1 q2 q3
0 7→ (a,R) 2 7→ (0,L) 2 7→ (2,L.
This document provides an overview of Laplace transforms. Key points include:
- Laplace transforms convert differential equations from the time domain to the algebraic s-domain, making them easier to solve. The process involves taking the Laplace transform of each term in the differential equation.
- Common Laplace transforms of functions are presented. Properties such as linearity, differentiation, integration, and convolution are also covered.
- Partial fraction expansion is used to break complex fractions in the s-domain into simpler forms with individual terms that can be inverted using tables of transforms.
- Solving differential equations using Laplace transforms follows a standard process of taking the Laplace transform of each term, rewriting the equation in the s-domain, solving
Conversion of transfer function to canonical state variable modelsJyoti Singh
Realization of transfer function into state variable models is needed even if the control system design based on frequency-domain design method.
In these cases the need arises for the purpose of transient response simulation.
But there is not much software for the numerical inversion of Laplace transform.
So one ways is to convert transfer function of the system to state variable description and numerically integrating the resulting differential equations rather than attempting to compute the inverse Laplace transform by numerical method.
A Simple Communication System Design Lab #3 with MATLAB SimulinkJaewook. Kang
This document outlines the schedule and topics for a series of labs on communication system design using MATLAB Simulink. The upcoming Lab #3 will cover phase splitting, which extracts the real and imaginary components from a complex baseband signal, and up/down conversion, which shifts signals between baseband and intermediate frequencies. The lab is scheduled for April 1st from 1-4pm and will be instructed by Jaewook Kang. Previous and future labs will cover topics like OFDM, S-function design, channel modeling, and subsystem implementation.
The document appears to be a sample paper for the EC-GATE-2013 exam, containing 23 multiple choice questions related to electrical engineering concepts.
The questions cover topics such as logic gates, vector calculus, impulse response of systems, pn junction diodes, oxidation rates in IC technology, approximations of trigonometric functions, divergence of vector fields, Bode plots, op-amps, resistor networks, microprocessor programs, digital modulation, sampling theory, MOSFET characteristics, properties of stable linear time-invariant systems, matrix eigenvalues, polynomial roots, circuit analysis using Laplace transforms, and AC circuit analysis.
Each question is followed by a short explanation of the answer. The document serves as a practice test to
This document discusses Fourier series and integrals. It begins by explaining Fourier series using sines, cosines, and exponentials to represent periodic functions. Square waves are given as examples that can be expressed as infinite combinations of sines. Any periodic function can be expressed as a Fourier series. Fourier series are then derived for specific examples, including a square wave, repeating ramp, and up-down train of delta functions. Cosine series are also discussed. The document concludes by deriving the Fourier series for the delta function.
Varibale frequency response lecturer 2 - audio+Jawad Khan
This document discusses Bode plots, which are used to analyze the frequency response of variable-frequency networks. Bode plots consist of two graphs: a magnitude plot using a logarithmic scale on the horizontal axis to show gain or attenuation over frequency, and a phase plot to show phase shift over frequency. Zeros cause positive gain slopes on the magnitude plot, while poles cause negative slopes. Phase is determined from the tangent inverse of the transfer function. Bode plots provide an efficient way to understand a network's behavior at different frequencies.
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text book Programmable-Logic-Controllers plc.pdfMahamad Jawhar
This document provides an overview and introduction to programmable logic controllers (PLCs). It discusses the basic parts and components of a PLC including the input/output section, central processing unit, memory, and programming devices. It also describes the basic principles of how PLCs operate by scanning inputs, executing a user-created program, and updating outputs. The document is intended to familiarize readers with the basic concepts and components of PLCs.
Kurdistan Regional Government Iraq Ministry of Electricity generates electricity primarily from fossil fuels such as natural gas. Renewable energy sources such as hydro, solar, wind, and geothermal currently account for a smaller portion of electricity generation but their use is growing. Geothermal energy harnesses heat from within the earth through technologies such as hydrothermal, geopressure, and hot dry rock systems to generate electricity without directly burning fuels. Geothermal resources in the Kurdistan region show potential for development to increase renewable energy supply and reduce dependence on fossil fuels.
This document appears to be a resume for Zeyad Azeez Abdullah, a mechanical engineer. It includes his name, title, date, and sections on qualifications, experience, education, skills and interests. There are also diagrams and specifications for solar panels, batteries, and other energy related topics.
Muhammad Jawhar Anwar built a helicopter robot for his Fourth Stage A1 class at Salahaddin University-Erbil's Mechanical & Mechatronics department. The robot uses a DC motor to rotate its rotor and has an LED light, buzzer, and logic programming to move straight, rotate clockwise and counterclockwise, turn the orange LED on while moving backward, and turn the green LED on.
Power Plant Engineering - (Malestrom) (1).pdfMahamad Jawhar
This document is the preface to a book on power plant engineering. It discusses the motivation for writing the book, which was to benefit students and researchers by covering key topics in power generation in a clear and concise manner. The preface notes that the book aims to satisfy engineering scholars and researchers by addressing conventional power plant topics at an international level. It also acknowledges those who encouraged and supported the authors in writing this pioneering textbook.
This document provides an index and specifications for various optical measuring instruments, including profile projectors and microscopes. It lists several models of profile projectors from the PJ-A3000 and PJ-H30 series, along with their specifications, optional accessories, and available fixtures. It also lists several models of measuring microscopes from the MF, MF-U, and MSM-400 series, along with their specifications and optional accessories. The document provides detailed information on the specifications, features, and options for these optical measuring instruments.
This document discusses refrigeration and its various applications and methods. It begins by defining refrigeration as the process of achieving and maintaining a temperature below surroundings through the removal of heat. The main types of refrigeration are then listed as domestic, commercial, industrial, marine, air conditioning, and food preservation. Various natural and early mechanical refrigeration techniques are described, such as the use of icehouses and evaporative cooling. The ideal vapor compression refrigeration cycle is explained through its four processes. Absorption refrigeration using ammonia-water and lithium bromide-water systems is also summarized. Compressor types including reciprocating, rotary, and centrifugal are defined. Key refrigeration system components and their functions are outlined.
This document provides an introduction to programmable logic controllers (PLCs). It defines a PLC as a specialized computer that monitors processes and controls machinery. The document outlines the history of PLCs in automating factory processes. It also describes the components of a basic PLC system and lists advantages such as flexibility, ease of programming, and reliability compared to traditional relay-based controls.
This document discusses the design of a CNC plasma cutting machine. It begins with an introduction to plasma cutting, explaining that plasma cutting uses a high-temperature jet of plasma gas to cut electrically conductive materials like metals. It then provides a flow chart showing the basic components and process of a plasma cutter. The document goes on to discuss the technical details of designing a CNC plasma cutting machine, including the mechanical components, electrical systems, and software setup required to automate the plasma cutting process through computer numerical control.
This document appears to be a research project report from Salahaddin University's College of Engineering for the 2018-2019 year. The report was prepared by 4 students and supervised by a faculty member. It includes sections on the introduction, methodology, conclusion, references, and images or figures related to the research project.
This document appears to be a template for a student project submitted to the Mechanical Engineering Department at Salahaddin University-Erbil in Iraq. The template provides an outline for the typical sections included in an engineering student project paper, such as an abstract, acknowledgements, table of contents, references, etc. It also includes placeholders for the student to include the introduction, methodology, example analysis and design, results and discussion, and conclusion sections. The introduction section provides background on the project and defines the problem statement, objectives and outline. The methodology section details the analysis procedures. The example section applies the methodology to a relevant problem. The results, discussion and conclusion sections present and interpret the findings and link them back to the
The document describes a PLC program for controlling a water filling and discharging process in a tank. The program uses two level sensors, one for high level and one for low level, to control a feeding valve and discharge valve. When the low level sensor is triggered, the feeding valve turns on to fill the tank, and when the high level sensor is triggered, the discharge valve turns on to empty the tank. The program is designed to maintain the water level between the two sensor points.
This document summarizes a student project to control a two-directional traffic light using a PLC. It introduces the use of traffic lights to control vehicle and pedestrian traffic. Timers are used to provide time delays to control the light sequences. A ladder logic diagram is programmed with contacts that simulate relays to control the red and green lights for each of the three poles in sequences, with one pole green at a time for a set period before moving to the next pole. The process then repeats continuously to control traffic flow in both directions.
This document summarizes different types of cooling towers. It describes how cooling towers work by bringing air and water into contact to reduce the water's temperature through evaporation. It discusses the components of cooling towers, including fill materials, drift eliminators, nozzles, fans, and driveshafts. It also compares crossflow and counterflow cooling towers and how they differ in their water and air flow configurations. Finally, it outlines the differences between factory-assembled and field-erected cooling towers.
This document is a research project submitted in partial fulfillment of the requirements for a degree in Mechanical and Mechatronics Engineering. It contains an abstract, introduction, and 4 chapters that discuss cooling towers, their components, thermal performance testing, and electrical components. The introduction provides background on cooling towers and how they work to lower water temperature through evaporation and heat transfer to the atmosphere. It also discusses prior research on improving cooling tower performance. The abstract indicates the research examines different types of cooling towers, their application, efficiency, and working principles, and includes a simulation of flow fields around a cooling tower.
This document is a research project submitted for a degree in Mechanical and Mechatronics Engineering on cooling towers. It includes 4 chapters that discuss mechanical components, thermal performance testing, and electrical components of cooling towers. The introduction provides background on cooling towers and how they work to remove heat from water through evaporation. It also discusses types of cooling towers, including natural draft and mechanical draft, and covers psychrometrics and heat transfer principles.
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2. It outlines the procedures for dimensioning each type of cooling tower, including calculating heat and mass transfer, air and water flows, electric power needs, and water loss. Equations used for each type are presented.
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The document is a homework assignment submitted by Ahmed Naseh Latif, a student in the 4th stage of the Mechanic and Mechatronic Engineering department at Salahaddin University-Erbil. The assignment asks the student to list 10 applications of robots in real life with pictures. The student provides 10 applications: 1) automated transportation, 2) security/defense/surveillance, 3) robot cooking, 4) medicine, 5) education, 6) home maintenance, 7) dangerous jobs, 8) as a servant, 9) as a friend, and 10) crime fighting. The student includes references from books and articles on the history and uses of robotics.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
1. Introduction to Bode Plot
• 2 plots – both have logarithm of frequency on x-axis
o y-axis magnitude of transfer function, H(s), in dB
o y-axis phase angle
The plot can be used to interpret how the input affects the output in both magnitude and phase over
frequency.
Where do the Bode diagram lines comes from?
1) Determine the Transfer Function of the system:
)
(
)
(
)
(
1
1
p
s
s
z
s
K
s
H
+
+
=
2) Rewrite it by factoring both the numerator and denominator into the standard form
)
1
(
)
1
(
)
(
1
1
1
1
+
+
=
p
s
sp
z
s
Kz
s
H
where the z s are called zeros and the p s are called poles.
3) Replace s with j? . Then find the Magnitude of the Transfer Function.
)
1
(
)
1
(
)
(
1
1
1
1
+
+
=
p
jw
jwp
z
jw
Kz
jw
H
If we take the log10 of this magnitude and multiply it by 20 it takes on the form of
20 log10 (H(jw)) =
+
+
)
1
(
)
1
(
log
20
1
1
1
1
10
p
jw
jwp
z
jw
Kz
=
)
1
(
log
20
log
20
log
20
)
1
(
log
20
log
20
log
20
1
10
10
1
10
1
10
1
10
10 +
−
−
−
+
+
+ z
jw
jw
p
z
jw
z
K
Each of these individual terms is very easy to show on a logarithmic plot. The entire Bode log magnitude plot is
the result of the superposition of all the straight line terms. This means with a little practice, we can quickly sket
the effect of each term and quickly find the overall effect. To do this we have to understand the effect of the
different types of terms.
These include: 1) Constant terms K
2) Poles and Zeros at the origin | j? |
3) Poles and Zeros not at the origin
1
1
p
j ω
+ or
1
1
z
j ω
+
4) Complex Poles and Zeros (addressed later)
2. Effect of Constant Terms:
Constant terms such as K contribute a straight horizontal line of magnitude 20 log10(K)
H = K
Effect of Individual Zeros and Poles at the origin:
A zero at the origin occurs when there is an s or j? multiplying the numerator. Each occurrence of this
causes a positively sloped line passing through ? = 1 with a rise of 20 db over a decade.
H = | ω
j |
A pole at the origin occurs when there are s or j? multiplying the denominator. Each occurrence of this
causes a negatively sloped line passing through ? = 1 with a drop of 20 db over a decade.
H =
ω
j
1
Effect of Individual Zeros and Poles Not at the Origin
Zeros and Poles not at the origin are indicated by the (1+j? /zi) and (1+j? /pi). The values
zi and pi in each of these expression is called a critical frequency (or break frequency). Below their critical
frequency these terms do not contribute to the log magnitude of the overall plot. Above the critical
frequency, they represent a ramp function of 20 db per decade. Zeros give a positive slope. Poles produce a
negative slope.
H =
i
i
p
j
z
j
ω
ω
+
+
1
1
20 log10(K)
?
0.1 1 10 100
(log scale)
20 log10(H)
?
0.1 1 10 100
(log10 scale)
20 log(H)
-20 db
dec.
dec.
+20 db
zi
pi
-20 db
?
0.1 1 10 100
(log scale)
20 log(H)
dec
?
0.1 1 10 100
(log scale)
20 log(H)
20 db
dec
3. • To complete the log magnitude vs. frequency plot of a Bode diagram, we superpositionall the lines
of the different terms on the same plot.
Example 1:
For the transfer function given, sketch the Bode log magnitude diagram which shows howthe log
magnitude of the system is affected by changing input frequency. (TF=transfer function)
1
2 100
TF
s
=
+
Step 1: Repose the equation in Bode plot form:
1
100
1
50
TF
s
=
+
recognized as
1
1
1
K
TF
s
p
=
+
with K = 0.01 and p1 = 50
For the constant, K: 20 log10(0.01) = -40
For the pole, with critical frequency, p1:
Example 2:
Your turn. Find the Bode log magnitude plot for the transfer function,
4
2
5 10
505 2500
x s
TF
s s
=
+ +
Start by simplifying the transfer function form:
50
-40 db
0db ? (log scale)
20 log10(MF)
4. Example 2 Solution:
Your turn. Find the Bode log magnitude plot for the transfer function,
4
2
5 10
505 2500
x s
TF
s s
=
+ +
Simplify transfer function form:
4
4
5 10
5 10 20
5*500
( 5)( 500) ( 1)( 1) ( 1)( 1)
5 500 5 500
x
s
x s s
TF
s s s s
s s
= = =
+ + + + + +
Recognize: K = 20 à 20 log10(20) = 26.02
1 zero at the origin
2 poles: at p1 = 5 and p2=500
Technique to get started:
1) Draw the line of each individual term on the graph
2) Follow the combined pole-zero at the origin line back to the left side of the graph.
3) Add the constant offset, 20 log10(K), to the value where the pole/zero at the origin line intersects the left
side of the graph.
4) Apply the effect of the poles/zeros not at the origin. working from left (low values) to right (higher
values) of the poles/zeros.
0 db
-40 db
100
80 db
-80 db
40 db
103
102
101
? (log scale)
5. Example 3: One more time. This one is harder. Find the Bode log magnitude plot for the transfer function,
200( 20)
(2 1)( 40)
s
TF
s s s
+
=
+ +
Simplify transfer function form:
0 db
-40 db
100
80 db
-80 db
40 db
103
102
101
20log10(TF)
? (log scale)
0 db
-40 db
100
80 db
-80 db
40 db
103
102
101
? (log scale)
6. Technique to get started:
1) Draw the line of each individual term on the graph
2) Follow the combined pole-zero at the origin line back to the left side of the graph.
3) Add the constant offset, 20 log10(K), to the value where the pole/zero at the origin line intersects the left
side of the graph.
4) Apply the effect of the poles/zeros not at the origin. working from left (low values) to right (higher
values) of the poles/zeros.
Example 3 Solution: Find the Bode log magnitude plot for the transfer function,
200( 20)
(2 1)( 40)
s
TF
s s s
+
=
+ +
Simplify transfer function form:
200*20
( 1) 100 ( 1)
200( 20) 40 20 20
(2 1)( 40) ( 1)( 1) ( 1)( 1)
0.5 40 0.5 40
s s
s
TF
s s s s
s s s s s
+ +
+
= = =
+ + + + + +
Recognize: K = 100 à 20 log10(100) = 40
1 pole at the origin
1 zero at z1 = 20
2 poles: at p1 = 0.5 and p2=40
0 db
-40 db
100
80 db
-80 db
40 db
103
102
101
20log10(TF)
40 db/dec
20 db/dec
20 db/dec
40 db/dec
? (log scale)
7. Technique to get started:
1) Draw the line of each individual term on the graph
2) Follow the combined pole-zero at the origin line back to the left side of the graph.
3) Add the constant offset, 20 log10(K), to the value where the pole/zero at the origin line intersects the left
side of the graph.
4) Apply the effect of the poles/zeros not at the origin. working from left (low values) to right (higher
values) of the poles/zeros.
The plot of the log magnitude vs. input frequency is only half of the story.
We also need to be able to plot the phase angle vs. input frequency on a log scale as well to complete the
full Bode diagram..
For our original transfer function,
)
1
(
)
1
(
)
(
1
1
1
1
+
+
=
p
jw
jwp
z
jw
Kz
jw
H
the cumulative phase angle associated with this function are given by
)
1
(
)
1
(
)
(
1
1
1
1
+
∠
∠
∠
+
∠
∠
∠
=
∠
p
jw
p
jw
z
jw
z
K
jw
H
Then the cumulative phase angle as a function of the input frequency may be written as
+
−
−
−
+
+
+
∠
=
∠ )
1
(
)
(
)
1
(
)
(
1
1
1
1 p
jw
p
jw
z
jw
z
K
jw
H
Once again, to show the phase plot of the Bode diagram, lines can be drawn for each of the different terms.
Then the total effect may be found by superposition.
Effect of Constants on Phase:
A positive constant, K>0, has no effect on phase. A negative constant, K<0, will set up a phase shift of
±180o
. (Remember real vs imaginary plots – a negative real number is at ±180o
relative to the origin)
Effect of Zeros at the origin on Phase Angle:
Zeros at the origin, s, cause a constant +90 degree shift for each zero.
∠ TF
Effect of Poles at the origin on Phase Angle:
Poles at the origin, s -1
, cause a constant -90 degree shift for each pole.
∠ TF
? (log)
+90 deg
?
-90 deg
8. Effect of Zeros not at the origin on Phase Angle:
Zeros not at the origin, like
1
1
z
j ω
+ , have no phase shift for frequencies much lower than zi, have a +
45 deg shift at z1, and have a +90 deg shift for frequencies much higherthan z1.
∠ H
.
To draw the lines for this type of term, the transition from 0o
to +90o
is drawn over 2 decades, starting at
0.1z1 and ending at 10z1.
Effect of Poles not at the origin onPhase Angle:
Poles not at the origin, like
1
1
1
p
jω
+
, have no phase shift for frequencies much lower than pi, have a -
45 deg shift at p1, and have a -90 deg shift for frequencies much higherthan p1.
∠ TF
.
To draw the lines for this type of term, the transition from 0o
to -90o
is drawn over 2 decades, starting at
0.1p1 and ending at 10p1.
When drawing the phase angle shift for not-at-the-origin zeros and poles, first locate the criticalfrequency
of the zero or pole. Then start the transition 1 decade before, following a slope of
±45o
/decade. Continue the transition until reaching the frequency one decade past the critical frequency.
Now let’s complete the Bode Phase diagrams for the previous examples:
?
0.1z1 1z1 10z1 100z1
+90 deg
+45 deg
?
0.1p1 1p1 10p1 100p1
-90 deg
-45 deg
9. Example 1:
For the Transfer Function given, sketch the Bode diagram which shows how the phase of the system is
affected by changing input frequency.
1 (1/100)
2 100 ( 1)
50
TF
s
s
= =
+ +
20 log|TF|
TF
+90
-90
-40db
50
5 500 rad/s
?
?
0.5
10. Example 2:
Repeat for the transfer function,
20log|TF|
4
2
5 10 20
505 2500 ( 1)( 1)
5 500
x s s
TF
s s
s s
= =
+ + + +
0 db
-40 db
100
80 db
-80 db
40 db
103
102
101
? (log scale)
0o
-90o
100
180o
-180o
90o
103
102
101
? (log scale)
20 log10(MF)
Phase Angle
11. Example 2 Solution:
Repeat for the transfer function,
20log|TF|
4
2
5 10 20
505 2500 ( 1)( 1)
5 500
x s s
TF
s s
s s
= =
+ + + +
0 db
-40 db
100
80 db
-80 db
40 db
103
102
101
? (log scale)
0o
-90o
100
180o
-180o
90o
103
102
101
? (log scale)
20 log10(MF)
Phase Angle
12. Example 3: Find the Bode log magnitude and phase angle plot for the transfer function,
100 ( 1)
200( 20) 20
(2 1)( 40) ( 1)( 1)
0.5 40
s
s
TF
s s
s s s s
+
+
= =
+ + + +
0 db
-40 db
10-1
80 db
-80 db
40 db
102
101
100
? (log scale)
0o
-90o
10-1
180o
-180o
90o
102
101
100
? (log scale)
20 log10(MF)
Phase Angle
13. Example 3: Find the Bode log magnitude and phase angle plot for the transfer function,
100 ( 1)
200( 20) 20
(2 1)( 40) ( 1)( 1)
0.5 40
s
s
TF
s s
s s s s
+
+
= =
+ + + +
0 db
-40 db
10-1
80 db
-80 db
40 db
102
101
100
? (log scale)
0o
-90o
10-1
180o
-180o
90o
102
101
100
? (log scale)
20 log10(MF)
Phase Angle
14. Example 4:
Sketch the Bode plot (Magnitude and Phase Angle) for
3
100 10 ( 1)
( 10)( 1000)
s
TF
s s
× +
=
+ +
=
20log10|TF|
Angle of TF
15. Example 4:
Sketch the Bode plot (Magnitude and Phase Angle) for
3
100 10 ( 1)
( 10)( 1000)
s
TF
s s
× +
=
+ +
=
10( 1)
1
( 1)( 1)
10 1000
s
s s
+
+ +
Therefore: K = 10 so 20log10(10) = 20 db
One zero: z1 = 1
Two poles: p1 = 10 and p2 = 1000
20log10|TF|
Angle of TF
40
20
0 db
-20
-40
10-2
10-1
100
101
102
103
104
10-2
10-1
100
101
102
103
104
180
90
0 deg
-90
-180
16. Matlab can also be used to draw Bode plots:
Matlab (with the sketched Bode Plot superimposed on the actual plot)
3
100 10 ( 1)
( 10)( 1000)
s
TF
s s
× +
=
+ +
w=logspace(-1,5,100); %setup for x-axis
MagH=100000*sqrt(w.^2+1^2)./(sqrt(w.^2+10^2).*sqrt(w.^2+1000^2));
%transfer function
MagHdb=20*log10(MagH); %transfer functionconverted to dB
PhaseHRad=atan(w/1)-atan(w/10)-atan(w/1000); %phase done in radians
PhaseHDeg=PhaseHRad*180/pi; %phase done in degrees
subplot(2,1,1)
semilogx(w,MagHdb,':b',x,y,'-b') %semilog plot
xlabel('frequency [rad/s]'),ylabel('20 log10(|TF|) [db]'),grid %xaxis label
subplot(2,1,2)
semilogx(w,PhaseHDeg,':b',xAng,yAngDeg,'-b')
xlabel('frequency [rad/s]'),ylabel('Phase Angle [deg]'),grid
18. The largest error that occurs on the Magnitude plot is right at the critical frequency. It is on the order of 3
db.
10
-1
10
0
10
1
10
2
10
3
0
10
20
30
40
50
frequency [rad/s]
20
log10(|TF|)
[db]
10
-1
10
0
10
1
10
2
10
3
0
20
40
60
80
100
frequency [rad/s]
Phase
Angle
[deg]
The largest error that is shown on the Phase plot occurs at 0.1? critical and 10? critical (one decade above and
below the critical frequency). Error at these points is about 6 degrees.
It’s understood that sketching the Bode diagrams will contain some error but this is generally considered
acceptable practice.
19. To quickly sketch the graphs:
1. Determine the starting value: |H(0)|
2. Determine all critical frequencies (break frequencies). Start from the lowest value and draw the graphs
as follows:
Magnitude Phase (create slope 1
decade below to 1 decade
above ωcritical)
Pole is negative -20dB/dec -45o
Pole is positive -20dB/dec +45o
Zero is negative +20dB/dec +45o
Zero is positive +20dB/dec -45o
Add each value to the previous value.
Examples:
1. H(s) = |H(0)| = |0+1/0+10 = 1/10 |= |0.1| => -20dB
Critical frequencies: zero@ -1 and pole @ -10
Magnitude Plot Phase Plot
The dotted line is a more accurate representation.
2. H(s) = |H(0)| = |10*(-1)/(-3)(-10) |= |-1/3| = 1/3 => -10dB
Note that the angle of (-1/3 real value) is 180o
critical frequencies: zero @ 1, pole@3 and 10