This document contains MATLAB code and output for generating and analyzing various types of discrete-time signals including unit sample sequences, exponential sequences, sinusoidal sequences, random sequences, and complex signals like amplitude modulation. The document contains MATLAB programs to generate these signals and questions about modifying the programs and analyzing properties of the generated signals.
MATLAB is a programming tool that simplifies programming compared to languages like C and C#. The document introduces basic MATLAB functions like help, inputting matrices and vectors, matrix operations, loops, conditional statements, and graphs. It also covers symbolic math, Laplace transforms, Fourier transforms, and other domains and transforms. The overall document serves as an introduction to essential MATLAB programming concepts and capabilities.
The document discusses the analysis and control of a system using MATLAB. It includes 6 tasks:
1) Analyze the system response and control signal for two input steps using MATLAB.
2) Measure performance indices for different controller gains.
3) Determine the steady state error for various inputs and controller gains.
4) Calculate and graph the system response to a ramp input.
5) Calculate and graph the system response to a step disturbance.
6) Determine the range of proportional controller gains that result in overdamped, critically damped, and underdamped responses to a step input.
Tarea1 francisco moya_mena_a74449_grupo02FrankMoya3
This document describes a hydraulic process for producing alcohol gel that involves controlling the liquid level in a cylindrical tank. The process model and linearized model are provided. The student is asked to:
1) Calculate sensor and control valve gains.
2) Simulate the process model and control system in Simulink.
3) Plot the static characteristic curve and identify the operating point.
4) Compare responses of the real and linearized systems to step changes in control signal and disturbance.
5) Comment on the validity of the linear model when the operating point changes.
This document provides instructions for a lab assignment on modern and classical control theory. It lists 6 problems to solve involving mathematical modeling, partial fraction decomposition, Laplace transforms, Bode plots, and controller design. Students are instructed to name and submit MATLAB and Simulink files for problems requiring simulation and to include generated reports and separate figure attachments.
The document outlines MATLAB, describing it as a high-level programming language where everything is represented as an array. It discusses MATLAB's interface and use as a programming language, along with key features like arrays, basic operations, built-in functions, loops and conditions, graphics, images, and how to access MATLAB help. The overall focus is on introducing the basic concepts and capabilities of the MATLAB programming environment and language.
This document provides an introduction to modeling and simulating nonlinear systems using Simulink. It begins with instructions on starting Simulink and accessing online tutorials. Examples are then presented on building Simulink models of nonlinear systems and specifying simulation parameters. Methods covered include linearizing nonlinear models around equilibrium points, identifying linear models of nonlinear systems from simulation data, and verifying the accuracy of the approximate linear models.
Ece 415 control systems, fall 2021 computer project 1 ronak56
This document provides instructions for a control systems computer project. It includes two parts:
Part I has students implement proportional control of a DC motor model in MATLAB. This includes designing open-loop and closed-loop controllers and analyzing step and disturbance responses.
Part II involves further controller design problems, including designing a controller to increase the system type and minimize settling time, analyzing response to a ramp input, and discussing performance limitations.
MATLAB is a programming tool that simplifies programming compared to languages like C and C#. The document introduces basic MATLAB functions like help, inputting matrices and vectors, matrix operations, loops, conditional statements, and graphs. It also covers symbolic math, Laplace transforms, Fourier transforms, and other domains and transforms. The overall document serves as an introduction to essential MATLAB programming concepts and capabilities.
The document discusses the analysis and control of a system using MATLAB. It includes 6 tasks:
1) Analyze the system response and control signal for two input steps using MATLAB.
2) Measure performance indices for different controller gains.
3) Determine the steady state error for various inputs and controller gains.
4) Calculate and graph the system response to a ramp input.
5) Calculate and graph the system response to a step disturbance.
6) Determine the range of proportional controller gains that result in overdamped, critically damped, and underdamped responses to a step input.
Tarea1 francisco moya_mena_a74449_grupo02FrankMoya3
This document describes a hydraulic process for producing alcohol gel that involves controlling the liquid level in a cylindrical tank. The process model and linearized model are provided. The student is asked to:
1) Calculate sensor and control valve gains.
2) Simulate the process model and control system in Simulink.
3) Plot the static characteristic curve and identify the operating point.
4) Compare responses of the real and linearized systems to step changes in control signal and disturbance.
5) Comment on the validity of the linear model when the operating point changes.
This document provides instructions for a lab assignment on modern and classical control theory. It lists 6 problems to solve involving mathematical modeling, partial fraction decomposition, Laplace transforms, Bode plots, and controller design. Students are instructed to name and submit MATLAB and Simulink files for problems requiring simulation and to include generated reports and separate figure attachments.
The document outlines MATLAB, describing it as a high-level programming language where everything is represented as an array. It discusses MATLAB's interface and use as a programming language, along with key features like arrays, basic operations, built-in functions, loops and conditions, graphics, images, and how to access MATLAB help. The overall focus is on introducing the basic concepts and capabilities of the MATLAB programming environment and language.
This document provides an introduction to modeling and simulating nonlinear systems using Simulink. It begins with instructions on starting Simulink and accessing online tutorials. Examples are then presented on building Simulink models of nonlinear systems and specifying simulation parameters. Methods covered include linearizing nonlinear models around equilibrium points, identifying linear models of nonlinear systems from simulation data, and verifying the accuracy of the approximate linear models.
Ece 415 control systems, fall 2021 computer project 1 ronak56
This document provides instructions for a control systems computer project. It includes two parts:
Part I has students implement proportional control of a DC motor model in MATLAB. This includes designing open-loop and closed-loop controllers and analyzing step and disturbance responses.
Part II involves further controller design problems, including designing a controller to increase the system type and minimize settling time, analyzing response to a ramp input, and discussing performance limitations.
This document contains instructions for a final exam with multiple choice and written response questions about object-oriented programming, state machines, linear systems, circuits, and state estimation. The exam covers topics taught in the 6.01 course including defining classes to represent mathematical functions, implementing state machines, analyzing system stability from poles, designing circuits to produce buffered voltage levels, and applying Bayes' rule to update beliefs based on experimental observations.
This document provides an overview of MATLAB including:
1. How to perform operations interactively or using script files
2. Entering commands and expressions using the command window
3. Examples of arithmetic, precedence rules, and assignment
4. Common mathematical functions and operations on arrays and matrices
5. Saving, loading, and managing variables and files in MATLAB sessions
Simulink is a GUI block diagram environment for modeling and simulating dynamic systems. It contains a library of continuous, discrete, and other elements that can be dragged onto a model window and connected to build simulations. Models can be run with different parameters and component values using the sim command. Simulation output data is accessible in the MATLAB workspace and can be plotted to analyze system behavior.
Digital Signal Processing Lab Manual ECE studentsUR11EC098
This document describes a MATLAB program to perform operations on discrete-time signals. It discusses amplitude manipulation operations like amplification, attenuation, and amplitude reversal. Time manipulation operations covered include time shifting and time reversal. It also describes adding and multiplying two discrete signals. The program takes user input, performs the selected operations, and plots the output waveforms to verify results.
This MATLAB code generates a circular path by revolving a point around a center point 8 times over a 40 second period. It plots the x-y coordinates of the path in one subplot and the y-coordinates over time in another subplot. The code clears variables, sets font sizes, generates time and angle vectors, calculates x and y coordinates from the radius and angle, and plots the results with labels and axis properties. The overall aim is to generate and visualize a revolving circular path.
Octave - Prototyping Machine Learning AlgorithmsCraig Trim
Octave is a high-level language suitable for prototyping learning algorithms.
Octave is primarily intended for numerical computations and provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The syntax is matrix-based and provides various functions for matrix operations. This tool has been in active development for over 20 years.
This document discusses executing a Simulink model externally from MATLAB via the MATLAB external interface. It provides examples of executing a Simulink model from the MATLAB command line, from a C program, from FORTRAN, and from the Modular Modeling System (MMS). The examples demonstrate preserving Simulink state information when executing a model over an extended simulation by passing the final states of one interval as the initial states of the next. This allows interfacing a Simulink control model with detailed systems models in codes like TRACE or MMS.
The document is a lab report submitted by Mr. Yuvraj Singh for his Control System Lab course. It includes an index listing 8 experiments performed, along with the objectives, requirements, theory, programs, outputs, and results for Experiment 1 on introducing MATLAB software and Experiment 2 on obtaining a transfer function from zero-pole-gain parameters in MATLAB. The experiments involve modeling and analyzing first and second order control systems using MATLAB.
This document provides an introduction to Matlab and Simulink for control applications. It discusses what Matlab and Simulink are, their main components and functions. It then gives examples of using Matlab to model control systems using transfer functions, Laplace transforms and PID controllers. Specific examples include modeling and controlling a mass-spring-damper system and designing a cruise control system for a car.
To plot graphs in MATLAB, you must:
1. Define the range of values for the x-axis variable
2. Define the function as y = f(x)
3. Use the plot command plot(x,y) to generate the graph
MATLAB allows adding titles, labels, grid lines, adjusting axis scales, and plotting multiple functions on the same graph. Subplot allows generating multiple plots in the same figure.
This document provides an overview of plotting functions in MATLAB. It discusses how to generate basic and 3D plots, customize plots using options like color, style and labels, and control the plot appearance using functions such as axis, title, legend. Examples are given to illustrate plotting a simple function, holding multiple plots, using subplots, and generating surface plots. The document also covers plotting in 3D using functions like surf, plot3 and manipulating axes properties.
Here is a line-by-line interpretation of the code cramers_rule.m:
1. Declare the function with inputs A (matrix) and b (vector), and output x (solution vector)
2-3. Check that A is square, exit if not
4. Calculate determinant of A
5-6. Check if determinant is non-zero, exit if zero
7. Initialize x to store solution
8-10. Outer loop iterates through columns j
11-13. Inner logic handles edge cases to extract correct column of A for submatrix
14. Calculate determinant of submatrix to get numerator of Cramer's rule
15. Divide and
This document summarizes a lab report on controlling a state-space system using multiple control methods. The lab explores controlling the pitch rate, speed change, and angle of attack outputs of a simulated aircraft system. Analyzing the system transfer functions showed the outputs have the same unstable eigenvalues. A state-space controller was designed using unrelated eigenvalues, failing to stabilize the system under impulse, step, and sine wave inputs. To stabilize the system, the original eigenvalues must be made negative.
This document provides a cheat sheet for common Matlab commands and functions. It summarizes basics like saving/loading variables, constructing matrices, defining variables, arithmetic operations, solving equations, plotting, and transposes/dot products. Key commands include save, load, rand, zeros, ones, eye, diag, sin, plot, eig, and dot for constructing matrices, numerical operations, solving equations, plotting, and linear algebra.
The document provides examples of MATLAB code for solving various engineering problems. The code examples calculate body mass index from height and weight, altitude from air pressure, the radius of the largest sphere that can fit inside a cone, radioactive decay over time, the volume of a half cylinder fuel tank, and more. The summaries provide the key inputs, outputs, and calculations made by each MATLAB script.
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.
1. The document discusses discrete event simulation and its components. Discrete event simulation models systems as they change states at discrete points in time. It involves events, state variables, event lists, and statistical counters.
2. The programming of discrete event simulation involves components like the system state, simulation clock, event list, statistical counters, initialization routine, timing routine, event routines, and report generator. The timing routine determines the next event and advances the clock, while event routines update the system state.
3. An example Fortran program for simulating a single-server queueing system is presented. It involves subroutines for initialization, timing, arrival and departure events, and reporting. Key variables include event time,
1. Various common signals were generated using MATLAB, including unit impulse, unit step, ramp, sinc, sine, sawtooth, square, and triangular signals. Both continuous and discrete forms were produced.
2. Operations on the generated signals included plotting their amplitude over time or index, adding titles and labels to figures, and displaying the results in different subplot configurations for comparison.
3. Common periodic signals like sine and square waves were generated along with aperiodic signals such as ramp, impulse and step functions to demonstrate the creation of basic continuous and discrete time signals in MATLAB for analysis and simulation.
The document contains details about experiments performed in a Digital Signal Processing practical course. It includes the aims, apparatus required, theory, source code and results for experiments involving MATLAB programs to generate basic signals like impulse, step, ramp and exponential signals; sine and cosine signals; quantization; sampling theorem; linear convolution; autocorrelation; and cross-correlation. Programs were written in MATLAB to perform the various digital signal processing tasks and the output was verified.
The document contains the laboratory manual for the digital signal processing lab of Jayalakshmi Institute of Technology. It lists the experiments to be conducted using MATLAB and TMS320C5416. The experiments using MATLAB include generation of discrete time signals, verification of sampling theorem, calculation of FFT and IFFT, analysis of LTI systems, convolution, and design of FIR and IIR filters. The experiments using TMS320C5416 include linear and circular convolution, calculation of FFT, generation of signals, and implementation of FIR and IIR filters. Detailed procedures and programs are provided for each experiment.
PID Tuning using Ziegler Nicholas - MATLAB ApproachWaleed El-Badry
This is an unreleased lab for undergraduate Mechatronics students to know how to practice Ziegler Nicholas method to find the PID factors using MATLAB.
This document contains information about the Digital Signal Processing lab at Shadan College of Engineering & Technology. It includes:
1. A list of 12 experiments to be conducted in the lab, related to topics like generating signals, implementing filters, and analyzing system responses.
2. An introduction to MATLAB, describing its basic functions and capabilities for numerical computation and signal processing.
3. Programs and instructions for carrying out specific DSP experiments in MATLAB, including generating basic signals, computing the DFT/IDFT of sequences, and determining the impulse/frequency responses of systems defined by difference equations.
The document provides students with an overview of the lab activities and teaches them how to use MATLAB for digital signal
This document contains instructions for a final exam with multiple choice and written response questions about object-oriented programming, state machines, linear systems, circuits, and state estimation. The exam covers topics taught in the 6.01 course including defining classes to represent mathematical functions, implementing state machines, analyzing system stability from poles, designing circuits to produce buffered voltage levels, and applying Bayes' rule to update beliefs based on experimental observations.
This document provides an overview of MATLAB including:
1. How to perform operations interactively or using script files
2. Entering commands and expressions using the command window
3. Examples of arithmetic, precedence rules, and assignment
4. Common mathematical functions and operations on arrays and matrices
5. Saving, loading, and managing variables and files in MATLAB sessions
Simulink is a GUI block diagram environment for modeling and simulating dynamic systems. It contains a library of continuous, discrete, and other elements that can be dragged onto a model window and connected to build simulations. Models can be run with different parameters and component values using the sim command. Simulation output data is accessible in the MATLAB workspace and can be plotted to analyze system behavior.
Digital Signal Processing Lab Manual ECE studentsUR11EC098
This document describes a MATLAB program to perform operations on discrete-time signals. It discusses amplitude manipulation operations like amplification, attenuation, and amplitude reversal. Time manipulation operations covered include time shifting and time reversal. It also describes adding and multiplying two discrete signals. The program takes user input, performs the selected operations, and plots the output waveforms to verify results.
This MATLAB code generates a circular path by revolving a point around a center point 8 times over a 40 second period. It plots the x-y coordinates of the path in one subplot and the y-coordinates over time in another subplot. The code clears variables, sets font sizes, generates time and angle vectors, calculates x and y coordinates from the radius and angle, and plots the results with labels and axis properties. The overall aim is to generate and visualize a revolving circular path.
Octave - Prototyping Machine Learning AlgorithmsCraig Trim
Octave is a high-level language suitable for prototyping learning algorithms.
Octave is primarily intended for numerical computations and provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The syntax is matrix-based and provides various functions for matrix operations. This tool has been in active development for over 20 years.
This document discusses executing a Simulink model externally from MATLAB via the MATLAB external interface. It provides examples of executing a Simulink model from the MATLAB command line, from a C program, from FORTRAN, and from the Modular Modeling System (MMS). The examples demonstrate preserving Simulink state information when executing a model over an extended simulation by passing the final states of one interval as the initial states of the next. This allows interfacing a Simulink control model with detailed systems models in codes like TRACE or MMS.
The document is a lab report submitted by Mr. Yuvraj Singh for his Control System Lab course. It includes an index listing 8 experiments performed, along with the objectives, requirements, theory, programs, outputs, and results for Experiment 1 on introducing MATLAB software and Experiment 2 on obtaining a transfer function from zero-pole-gain parameters in MATLAB. The experiments involve modeling and analyzing first and second order control systems using MATLAB.
This document provides an introduction to Matlab and Simulink for control applications. It discusses what Matlab and Simulink are, their main components and functions. It then gives examples of using Matlab to model control systems using transfer functions, Laplace transforms and PID controllers. Specific examples include modeling and controlling a mass-spring-damper system and designing a cruise control system for a car.
To plot graphs in MATLAB, you must:
1. Define the range of values for the x-axis variable
2. Define the function as y = f(x)
3. Use the plot command plot(x,y) to generate the graph
MATLAB allows adding titles, labels, grid lines, adjusting axis scales, and plotting multiple functions on the same graph. Subplot allows generating multiple plots in the same figure.
This document provides an overview of plotting functions in MATLAB. It discusses how to generate basic and 3D plots, customize plots using options like color, style and labels, and control the plot appearance using functions such as axis, title, legend. Examples are given to illustrate plotting a simple function, holding multiple plots, using subplots, and generating surface plots. The document also covers plotting in 3D using functions like surf, plot3 and manipulating axes properties.
Here is a line-by-line interpretation of the code cramers_rule.m:
1. Declare the function with inputs A (matrix) and b (vector), and output x (solution vector)
2-3. Check that A is square, exit if not
4. Calculate determinant of A
5-6. Check if determinant is non-zero, exit if zero
7. Initialize x to store solution
8-10. Outer loop iterates through columns j
11-13. Inner logic handles edge cases to extract correct column of A for submatrix
14. Calculate determinant of submatrix to get numerator of Cramer's rule
15. Divide and
This document summarizes a lab report on controlling a state-space system using multiple control methods. The lab explores controlling the pitch rate, speed change, and angle of attack outputs of a simulated aircraft system. Analyzing the system transfer functions showed the outputs have the same unstable eigenvalues. A state-space controller was designed using unrelated eigenvalues, failing to stabilize the system under impulse, step, and sine wave inputs. To stabilize the system, the original eigenvalues must be made negative.
This document provides a cheat sheet for common Matlab commands and functions. It summarizes basics like saving/loading variables, constructing matrices, defining variables, arithmetic operations, solving equations, plotting, and transposes/dot products. Key commands include save, load, rand, zeros, ones, eye, diag, sin, plot, eig, and dot for constructing matrices, numerical operations, solving equations, plotting, and linear algebra.
The document provides examples of MATLAB code for solving various engineering problems. The code examples calculate body mass index from height and weight, altitude from air pressure, the radius of the largest sphere that can fit inside a cone, radioactive decay over time, the volume of a half cylinder fuel tank, and more. The summaries provide the key inputs, outputs, and calculations made by each MATLAB script.
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.
1. The document discusses discrete event simulation and its components. Discrete event simulation models systems as they change states at discrete points in time. It involves events, state variables, event lists, and statistical counters.
2. The programming of discrete event simulation involves components like the system state, simulation clock, event list, statistical counters, initialization routine, timing routine, event routines, and report generator. The timing routine determines the next event and advances the clock, while event routines update the system state.
3. An example Fortran program for simulating a single-server queueing system is presented. It involves subroutines for initialization, timing, arrival and departure events, and reporting. Key variables include event time,
1. Various common signals were generated using MATLAB, including unit impulse, unit step, ramp, sinc, sine, sawtooth, square, and triangular signals. Both continuous and discrete forms were produced.
2. Operations on the generated signals included plotting their amplitude over time or index, adding titles and labels to figures, and displaying the results in different subplot configurations for comparison.
3. Common periodic signals like sine and square waves were generated along with aperiodic signals such as ramp, impulse and step functions to demonstrate the creation of basic continuous and discrete time signals in MATLAB for analysis and simulation.
The document contains details about experiments performed in a Digital Signal Processing practical course. It includes the aims, apparatus required, theory, source code and results for experiments involving MATLAB programs to generate basic signals like impulse, step, ramp and exponential signals; sine and cosine signals; quantization; sampling theorem; linear convolution; autocorrelation; and cross-correlation. Programs were written in MATLAB to perform the various digital signal processing tasks and the output was verified.
The document contains the laboratory manual for the digital signal processing lab of Jayalakshmi Institute of Technology. It lists the experiments to be conducted using MATLAB and TMS320C5416. The experiments using MATLAB include generation of discrete time signals, verification of sampling theorem, calculation of FFT and IFFT, analysis of LTI systems, convolution, and design of FIR and IIR filters. The experiments using TMS320C5416 include linear and circular convolution, calculation of FFT, generation of signals, and implementation of FIR and IIR filters. Detailed procedures and programs are provided for each experiment.
PID Tuning using Ziegler Nicholas - MATLAB ApproachWaleed El-Badry
This is an unreleased lab for undergraduate Mechatronics students to know how to practice Ziegler Nicholas method to find the PID factors using MATLAB.
This document contains information about the Digital Signal Processing lab at Shadan College of Engineering & Technology. It includes:
1. A list of 12 experiments to be conducted in the lab, related to topics like generating signals, implementing filters, and analyzing system responses.
2. An introduction to MATLAB, describing its basic functions and capabilities for numerical computation and signal processing.
3. Programs and instructions for carrying out specific DSP experiments in MATLAB, including generating basic signals, computing the DFT/IDFT of sequences, and determining the impulse/frequency responses of systems defined by difference equations.
The document provides students with an overview of the lab activities and teaches them how to use MATLAB for digital signal
Digital signal Processing all matlab code with Lab report Alamgir Hossain
Digital signal processing(DSP) laboratory with matlab software....
Problem List :
1.To write a Matlab program to evaluate the impulse response of the system.
2.Computation of N point DFT of a given sequence and to plot magnitude and phase spectrum.
3.To Generate continuous time sinusoidal signal, discrete time cosine signal.
4.To find the DFT / IDFT of given signal.
5.Program for generation of Sine sequence.
6.Program for generation of Cosine sequence.
7. Program for the generation of UNIT impulse signal
8. Program for the generation of Exponential signal.
This document contains a lab manual for signals and systems experiments in the Department of Electronics and Communication Engineering at Shadan College of Engineering and Technology. It lists 12 experiments covering topics like frequency spectrum analysis of continuous and discrete signals, frequency response analysis using software and transfer functions, Fourier transforms, convolution, sampling, and filter design. It also provides an introduction to MATLAB, describing basic MATLAB windows, data types, commands, and functions for signals and systems applications.
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and SystemsAmr E. Mohamed
This document provides an overview of discrete-time signals and systems in MATLAB. It defines discrete signals as sequences represented by x(n) and how they can be implemented as vectors in MATLAB. It describes various types of sequences like unit sample, unit step, exponential, sinusoidal, and random. It also covers operations on sequences like addition, multiplication, scaling, shifting, folding, and correlations. Discrete time systems are defined as operators that transform an input sequence x(n) to an output sequence y(n). Key properties discussed are linearity, time-invariance, stability, causality, and the use of convolution to represent the output of a linear time-invariant system. Examples are provided to demonstrate various concepts.
The document describes experiments conducted in MATLAB to visualize and understand various continuous-time and discrete-time signals. In experiment 1, common continuous signals like unit step, ramp, impulse etc. are plotted. Experiment 2 involves plotting corresponding discrete-time signals. The document provides MATLAB code examples to generate and plot these standard signals.
This document contains an index of 10 experiments related to signal processing using MATLAB. It lists the aim, page numbers, and brief description of each experiment. The experiments include developing basic signals, performing operations on sequences like addition and multiplication, linear convolution, impulse response of systems, Z-transform, Fourier transform, and finding the magnitude and phase response of linear time-invariant systems. Sample MATLAB code and generated waveforms are provided for some of the experiments.
The program demonstrates linear and circular convolution of sequences using MATLAB. For linear convolution, the conv function is used to convolve two input sequences and plot the results. For circular convolution, the FFT of each sequence is taken, multiplied together and inverse FFT applied to obtain the output, which is also plotted. The program thus allows generation and visualization of linear and circular convolution.
Welcome to the Digital Signal Processing (DSP) Lab Manual. This manual is designed to be your comprehensive guide throughout your DSP laboratory sessions. Digital Signal Processing is a fundamental field in electrical engineering and computer science that deals with the manipulation of digital signals to achieve various objectives, such as filtering, transformation, and analysis. In this lab, you will have the opportunity to apply theoretical knowledge to practical, hands-on exercises that will deepen your understanding of DSP concepts.
This manual is structured to provide you with step-by-step instructions, explanations, and insights into the experiments you'll be performing. Each experiment is carefully designed to reinforce your understanding of fundamental DSP principles and help you develop the skills necessary for signal processing applications. Whether you are a student or an instructor, this manual is intended to facilitate a productive and enriching DSP lab experience.
Welcome to the Digital Signal Processing (DSP) Lab Manual. This manual is designed to be your comprehensive guide throughout your DSP laboratory sessions. Digital Signal Processing is a fundamental field in electrical engineering and computer science that deals with the manipulation of digital signals to achieve various objectives, such as filtering, transformation, and analysis. In this lab, you will have the opportunity to apply theoretical knowledge to practical, hands-on exercises that will deepen your understanding of DSP concepts.
This manual is structured to provide you with step-by-step instructions, explanations, and insights into the experiments you'll be performing. Each experiment is carefully designed to reinforce your understanding of fundamental DSP principles and help you develop the skills necessary for signal processing applications. Whether you are a student or an instructor, this manual is intended to facilitate a productive and enriching DSP lab experience.
The document describes MATLAB software and its uses for signal processing. MATLAB is a matrix-based program for scientific and engineering computation. It provides built-in functions for technical computation, graphics, and animation. The Signal Processing Toolbox contains functions for filtering, Fourier transforms, convolution, and filter design. The document lists some important MATLAB commands and frequently used signal processing functions, along with their syntax and purpose. It also describes the basic windows of the MATLAB interface and provides examples of generating common continuous and discrete time signals using MATLAB code.
The document contains solutions to 16 solved problems involving MATLAB code and calculations. Some key details include:
1) Solving expressions without MATLAB and checking answers with MATLAB code.
2) Using vectors and relational operators to evaluate expressions and create new vectors.
3) Writing programs to calculate averages, sums, modify vectors based on conditions, and estimate values like pi.
4) Programs generate random vectors and matrices, perform operations element-by-element using loops, and calculate sequences like Fibonacci numbers.
Dsp 1recordprophess-140720055832-phpapp01Sagar Gore
This document describes a MATLAB program to study basic operations on discrete-time signals, including amplitude manipulation through scaling, attenuation, reversal, and offsetting, as well as time manipulation through shifting and reflection. The program prompts the user for input signals and operation parameters, performs the selected operations using MATLAB functions, and plots the output signals for comparison.
ELEG 320L – Signals & Systems Laboratory Dr. Jibran Khan Yous.docxtoltonkendal
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
1
LAB 4: CONVOLUTION
Background & Concepts
Convolution is denoted by:
𝑦[𝑛] = 𝑥[𝑛] ∗ ℎ[𝑛]
Your book has described the "flip and shift" method for performing convolution. First, we
set up two signals 𝑥[𝑘] and ℎ[𝑘]:
Flip one of the signals, say ℎ[𝑘], to form ℎ[−𝑘]:
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
2
Shift ℎ[−𝑘] by n to form ℎ[𝑛 − 𝑘]. For each value of 𝑛, form 𝑦[𝑛] by multiplying and
summing all the element of the product of𝑥[𝑘]ℎ[𝑛 − 𝑘], −∞ < 𝑘 < ∞. The figure
below shows an example of the calculation of𝑦[1]. The top panel shows𝑥[𝑘]. The
middle panel showsℎ[1 − 𝑘]. The lower panel shows𝑥[𝑘]𝑦[1 − 𝑘]. Note that this is a
sequence on a 𝑘 axis. The sum of the lower sequence over all k gives 𝑦[1] = 2.
We repeat this shifting, multiplication and summing for all values of 𝑛 to get the
complete sequence 𝑦[𝑛]:
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
3
The conv Command
conv(x,h) performs a 1-D convolution of vectors 𝑥 and ℎ. The resulting vector 𝑦
has length length(𝑦) = length(𝑥) + length(ℎ) − 1. Imagine vector 𝑥 as being
stationary and the flipped version of ℎ is slid from left to right. Note that conv(x,h) =
conv(h,x). An example of the convolution of two signals and plotting the result is
below:
>> x = [0.5 0.5 0.5]; %define input signal x[n]
>> h = [3.0 2.0 1.0]; %unit-pulse response h[n]
>> y = conv(x,h); %compute output y[n] via convolution
>> n = 0:(length(y)-1); %for plotting y[n]
>> stem(n,y) % plot y[n]
>> grid;
>> xlabel('n');
>> ylabel('y[n]');
>> title('Output of System via Convolution');
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
4
Deconvolution
The command [q,r] = deconv(v,u), deconvolves vector u out of vector v, using long
division. The quotient is returned in vector q and the remainder in vector r such that
v = conv(u,q)+r. If u and v are vectors of polynomial coefficients, convolving them is
equivalent to multiplying the two polynomials, and deconvolution is polynomial
division. The result of dividing v by u is quotient q and remainder r. An examples is
below:
If
>> u = [1 2 3 4];
>> v = [10 20 30];
The convolution is:
>> c = conv(u,v)
c =
10 40 100 160 170 120
Use deconvolution to recover v.
>> [q,r] = deconv(c,u)
q =
10 20 30
r =
0 0 0 0 0 0
This gives a quotient equal to v and a zero remainder.
Structures
Structures in Matlab are just like structures in C. They are basically containers that
allow one
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
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
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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.
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- 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/)
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AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Laboratorio vibra
1. Name :
Section :
Laboratory Exercise 1
DISCRETE-TIME SIGNALS: TIME-DOMAIN REPRESENTATION
1.1 GENERATION OF SEQUENCES
Project 1.1 Unit sample and unit step sequences
A copy of Program P1_1 is given below.
% Program P1_1
% Generation of a Unit Sample Sequence
clf;
% Generate a vector from -10 to 20
n = -10:20;
% Generate the unit sample sequence
u = [zeros(1,10) 8 zeros(1,20)];
% Plot the unit sample sequence
% stem para funciones discretas
stem(n,u);
xlabel('Time index n');ylabel('Amplitude');
title('Unit Sample Sequence');
axis([-10 20 0 1.2]);
Answers :
Q1.1 The unit sample sequence u[n] generated by running Program P1_1 is shown
below:
Q1.2 The purpose of clf com mand is – The screen is cleaned.
The purpose of axis com mand is – this activates the axis “x” and “y”
The purpose of title com mand is –this puts title in the graphics.
1
2. The purpose of xlabel com mand is – this puts legend in the axis “x”.
The purpose of ylabel com mand is – this puts legend in the axis “x”.
Q1.3 The modified Program P1_1 to generate a delayed unit sample sequence ud[n]
with a delay of 11 samples is given below along with the sequence generated by
running this program .
% Program P1_1
% Generation of a Unit Sample Sequence
clf;
% Generate a vector from -10 to 20
n = -10:20;
% Generate the unit sample sequence
ud = [zeros(1,21) 1 zeros(1,9) ];
% Plot the unit sample sequence
% stem para funciones discretas
stem(n,ud);
xlabel('Time index n');ylabel('Amplitude');
title('Unit Sample Sequence');
axis([-10 20 0 1.2]);
Q1.4 The modified Program P1_1 to generate a unit step sequence s[n] is given below
along with the sequence generated by running this program .
% Program P1_1
% Generation of a Unit Sample Sequence
clf;
% Generate a vector from -10 to 20
n = -10:20;
% Generate the unit sample sequence
s = [zeros(1,10) ones(1,21) ];
2
3. % Plot the unit sample sequence
% stem para funciones discretas
stem(n,s);
xlabel('Time index n');ylabel('Amplitude');
title('Unit Sample Sequence');
axis([-10 20 0 1.2]);
Q1.5 The modified Program P1_1 to generate a unit step sequence sd[n] with an ad-
vance of 7 samples is given below along with the sequence generated by running
this program .
< Insert program code here. Copy from m-file(s) and paste. >
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Project 1.2 Exponential signals
A copy of Programs P1_2 and P1_3 are given below .
< Insert program code here. Copy from m-file(s) and paste. >
Answers:
Q1.6 The complex- valued exponential sequence generated by running Program P1_2 is
shown below :
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Q1.7 The parameter controlling the rate of growth or decay of this sequence is -
The parameter controlling the amplitude of this sequence is -
Q1.8 The result of changing the parameter c to (1/12)+(pi/6)*i is -
3
4. Q1.9 The purpose of the operator real is -
The purpose of the operator imag is -
Q1.10 The purpose of the command subplot is -
Q1.11 The real-valued exponential sequence generated by running Program P1_3 is
shown below :
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Q1.12 The parameter controlling the rate of growth or decay of this sequence is -
The parameter controlling the amplitude of this sequence is -
Q1.13 The difference between the arithmetic operators ^ and .^ is -
Q1.14 The sequence generated by running Program P1_3 with the parameter a changed
to 0.9 and the parameter K changed to 20 is shown below :
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Q1.15 The length of this sequence is -
It is controlled by the following MATLAB command line :
It can be changed to generate sequences with different lengths as follows (give an
example command line and the corresponding length) :
Q1.16 The energies of the real-valued exponential sequences x[n]generated in Q1.11
and Q1.14 and computed using the command sum are -
Project 1.3 Sinusoidal sequences
A copy of Program P1_4 is given below .
< Insert program code here. Copy from m-file(s) and paste. >
Answers:
Q1.17 The sinusoidal sequence generated by running Program P1_4 is displayed below .
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Q1.18 The frequency of this sequence is -
It is controlled by the following MATLAB command line :
4
5. A sequence with new frequency _____ can be generated by the following command
line :
The parameter controlling the phase of this sequence is -
The parameter controlling the amplitude of this sequence is -
The period of this sequence is -
Q1.19 The length of this sequence is -
It is controlled by the following MATLAB command line :
A sequence with new length _____ can be generated by the following command
line :
Q1.20 The average power of the generated sinusoidal sequence is -
Q1.21 The purpose of axis com mand is -
The purpose of grid com mand is -
Q1.22 The modified Program P1_4 to generate a sinusoidal sequence of frequency 0.9 is
given below along with the sequence generated by running it .
< Insert program code here. Copy from m-file(s) and paste. >
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
A comparison of this new sequence with the one generated in Question Q1.17
shows -
A sinusoidal sequence of frequency 1.1 generated by modifying Program P1_4 is
shown below.
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
A comparison of this new sequence with the one generated in Question Q1.17
shows -
Q1.23 The sinusoidal sequence of length 50, frequency 0.08, amplitude 2.5, and phase
shift of 90 degrees generated by modifying Program P1_4 is displayed below .
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
The period of this sequence is -
Q1.24 By replacing the stem command in Program P1_4 with the plot command, the
plot obtained is as shown below :
5
6. < Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
The difference between the new plot and the one generated in Question Q1.17 is -
Q1.25 By replacing the stem command in Program P1_4 with the stairs command the
plot obtained is as shown below :
% Program P1_4
% Generation of a sinusoidal sequence
n = 0:40;
f = 0.1;
phase = 0;
A = 1.5;
arg = 2*pi*f*n - phase;
x = A*cos(arg);
clf; % Clear old graph
stairs(n,x); % Plot the generated sequence
axis([0 40 -2 2]);
grid;
title('Sinusoidal Sequence');
xlabel('Time index n');
ylabel('Amplitude');
axis;
The difference between the new plot and those generated in Questions Q1.17 and
Q1.24 is -
Project 1.4 Random signals
Answers:
Q1.26 The MATLAB program to generate and display a random signal of length 100 with
elements uniformly distributed in the interval [–2, 2] is given below along with the
plot of the random sequence generated by running the program :
< Insert program code here. Copy from m-file(s) and paste. >
6
7. < Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Q1.27 The MATLAB program to generate and display a Gaussian random signal of length
75 with elements normally distributed with zero mean and a variance of 3 is given
below along with the plot of the random sequence generated by running the
program :
< Insert program code here. Copy from m-file(s) and paste. >
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
Q1.28 The MATLAB program to generate and display five sample sequences of a random
sinusoidal signal of length 31
{X[n]} = {Acos(ωon + φ)}
where the amplitude A and the phase φ are statistically independent random
variables with uniform probability distribution in the range 0 ≤ A ≤ 4 for the
amplitude and in the range 0 ≤ φ ≤ 2π for the phase is given below. Also shown
are five sample sequences generated by running this program five different times .
< Insert program code here. Copy from m-file(s) and paste. >
< Insert MATLAB figure(s) here. Copy from figure window(s) and
paste. >
1.2 SIMPLE OPERATIONS ON SEQUENCES
Project 1.5 Signal Smoothing
A copy of Program P1_5 is given below .
% Program P1_5
% Signal Smoothing by Averaging
clf;
R = 51;
d = 0.8*(rand(R,1) - 0.5); % Generate random noise
m = 0:R-1;
s = 2*m.*(0.9.^m); % Generate uncorrupted signal
x = s + d'; % Generate noise corrupted signal
subplot(2,1,1);
plot(m,d','r-',m,s,'g--',m,x,'b-.');
xlabel('Time index n');ylabel('Amplitude');
legend('d[n] ','s[n] ','x[n] ');
x1 = [0 0 x];x2 = [0 x 0];x3 = [x 0 0];
y = (x1 + x2 + x3)/3;
subplot(2,1,2);
plot(m,y(2:R+1),'r-',m,s,'g--');
legend( 'y[n] ','s[n] ');
xlabel('Time index n');ylabel('Amplitude');
Answers:
7
8. Q1.29 The signals generated by running Program P1_5 are displayed below :
Q1.30 The uncorrupted signal s[n]is – el product de un crecimiento lineal
The additive noise d[n]is – una secuencia aleatoria distribuida en medio de - 0.4 y
+0.4.
Q1.31 The statement x = s + d CAN / CANNOT be used to generate the noise corrupted
signal because – la letra d es un vector columna, s es un vector fila, para sumar estos vectores se
debe sacar la inversa de uno de ellos.
Q1.32 The relations between the signals x1, x2, and x3, and the signal x are – que tres
señales estan extendidos a la version “x” con una muestra anexada a izquierda y
derecha. x1 es un retardo de x, la señal x2 es igual a x y x3 es una señal adelantada en
el tiempo.
Q1.33 The purpose of the legend command is – dar informacion con color para determiner
el tipo de señal que se observa.
Project 1.6 Generation of Complex Signals
A copy of Program P1_6 is given below .
% Program P1_6
% Generation of amplitude modulated sequence
clf;
n = 0:100;
m = 0.4;fH = 0.1; fL = 0.01;
xH = sin(2*pi*fH*n);
xL = sin(2*pi*fL*n);
y = (1+m*xL).*xH;
stem(n,y);grid;
xlabel('Time index n');ylabel('Amplitude');
Answers:
8
9. Q1.34 The amplitude modulated signals y[n] generated by running Program P1_6 for
various values of the frequencies of the carrier signal xH[n] and the modulating
signal xL[n], and various values of the modulation index m are shown below :
m = 0.4;fH = 0.1; fL = 0.01;
Q1.35 The difference between the arithmetic operators * and .* is – el product de dos
matrices o vectores que tienen las mismas dimensiones.
A copy of Program P1_7 is given below .
% Program P1_7
% Generation of a swept frequency sinusoidal sequence
n = 0:100;
a = pi/2/100;
b = 0;
arg = a*n.*n + b*n;
x = cos(arg);
clf;
stem(n, x);
axis([0,100,-1.5,1.5]);
title('Swept-Frequency Sinusoidal Signal');
xlabel('Time index n');
ylabel('Amplitude');
grid; axis;
Answers:
Q1.36 The swept- frequency sinusoidal sequence x[n] generated by running Program
P1_7 is displayed below .
9
10. Q1.37 The minimum and maximum frequencies of this signal are - el minimo es cuando
n=0, el maximo es cuando m=100
Q1.38 The Program 1_7 modified to generate a swept sinusoidal signal with a minimum
frequency of 0.1 and a maximum frequency of 0.3 is given below :
% Program P1_7
% Generation of a swept frequency sinusoidal sequence
n = 0:100;
a = pi/500;
b = pi/5;
arg = a*n.*n + b*n;
x = cos(arg);
clf;
stem(n, x);
axis([0,100,-1.5,1.5]);
title('Swept-Frequency Sinusoidal Signal');
xlabel('Time index n');
ylabel('Amplitude');
grid; axis;
1.3 WORKSPACE INFORMATION
Q1.39 The information displayed in the command window as a result of the who
command is – una lista de nombres de las variables definidas.
Q1.40 The information displayed in the command window as a result of the whos
command is - un numero detrminado de bytes de almacenamiento para el lugar de
trabajo.
1.4 OTHER TYPES OF SIGNALS (Optional)
Project 1.8 Squarewave and Sawtooth Signals
10
11. Answer:
Q1.41 MATLAB programs to generate the square- wave and the sawtooth wave se quences
of the type shown in Figures 1.1 and 1.2 are given below along with the sequences
generated by running these programs :
n = 0:30;
f = 0.1;
phase = 0; duty=60; A = 2.5;
arg = 2*pi*f*n + phase;
x = A*square(arg,duty);
clf;
stem(n,x);
axis([0 30 -3 3]); grid;
title('Onda cuadrada');
xlabel('n'); ylabel('Amplitud');
axis;
n = 0:30;
f = 0.1;
phase = 0;
duty=30;
A = 2.5;
arg = 2*pi*f*n + phase;
x = A*square(arg,duty);
clf;
stem(n,x);
axis([0 30 -3 3]); grid;
title('Onda cuadrada');
xlabel('n'); ylabel('Amplitud');
axis;
11