Digital Signal Processing using Scilab from Udemy

Scilab: Basic Operations on Discrete-Time Signal, Delay, Advance, and Folding using Scilab, Plotting Signals, Scilab Installation, Scilab Introduction

Scilab Implementations of Elementary Discrete-time Signals: Unit Sample, Unit Step, Unit Ramp and Exponential

Scilab Implementations of Graphical Representation of Continuous Signal, Discrete-Time Signal, Even and Odd Signals, Verification of whether Signal is Even or not.

Verification of Sampling Theorem, Sampling of Analog Signal, Sampling of Signal at Fs less than 2Fmax, Sampling of Signal at Nyquist Rate Fs=2Fmax, Sampling of Signal at Fs greater than 2Fmax

Scilab Implementation of Convolution with Predefined Function, conv scilab function, zero padding, Verifying output with Matrix method

Scilab Implementation of Convolution without Predefined Function, Conditions for Zero Padding, Identifying Response of System using Convolution Summation

Correlation of Two Sequences, Cross Correlation, Autocorrelation, xcorr function, Correlation using predefined function, Scilab implementation of Correlation operation.

Scilab code for finding z-Transform, Scilab code for demonstration of convolution property of z-transform, Defining functions in Scilab.

Scilab demonstration of Pole-Zero Plot, Creating Polynomials in Scilab, Numerator Polynomial, Denominator Polynomial, System Function in Scilab

Identifying Fourier Transform using Scilab, Signal Representation in Fourier Domain

Scilab Demonstrations of Discrete Fourier Transform, Inverse Discrete Transform, Magnitude Response, Phase Response, Circular Convolution using DFT and IDFT
Scilab Console, Writing Programs using Scilab, Implementation of basic signal processing operations

What's inside

Syllabus

Discrete-Time Signal

Basic Operations on Discrete-Time Signal, Delay, Advance, and Folding using Scilab, Plotting Signals, Scilab Installation, Scilab Introduction

Scilab Implementations of Elementary Discrete-time Signals: Unit Sample, Unit Step, Unit Ramp and Exponential

Scilab Implementations of Graphical Representation of Continuous Signal, Discrete-Time Signal, Even and Odd Signals, Verification of whether Signal is Even or not.

Sampling Theorem

Verification of Sampling Theorem, Sampling of Analog Signal, Sampling of Signal at Fs less than 2Fmax, Sampling of Signal at Nyquist Rate Fs=2Fmax, Sampling of Signal at Fs greater than 2Fmax

Convolution of Discrete-Time Signals

Scilab Implementation of Convolution with Predefined Function, conv scilab function, zero padding, Verifying output with Matrix method

Scilab Implementation of Convolution without Predefined Function, Conditions for Zero Padding, Identifying Response of System using Convolution Summation

Correlation of Two Sequences

Correlation of Two Sequences, Cross Correlation, Autocorrelation, xcorr function, Correlation using predefined function, Scilab implementation of Correlation operation.

Scilab code for finding z-Transform, Scilab code for demonstration of convolution property of z-transform, Defining functions in Scilab.

Scilab demonstration of Pole-Zero Plot, Creating Polynomials in Scilab, Numerator Polynomial, Denominator Polynomial, System Function in Scilab

Identifying Fourier Transform using Scilab, Signal Representation in Fourier Domain

Scilab Demonstrations of Discrete Fourier Transform, Inverse Discrete Transform, Magnitude Response, Phase Response, Circular Convolution using DFT and IDFT

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Digital Signal Processing using Scilab with these activities:

Review Linear Algebra Fundamentals

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Strengthen your understanding of linear algebra concepts, which are foundational for understanding signal processing operations like convolution and transforms.

Browse courses on Linear Algebra

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Review matrix operations such as addition, subtraction, and multiplication.
Practice solving systems of linear equations.
Understand the concepts of eigenvalues and eigenvectors.

Review 'Digital Signal Processing: Principles, Algorithms, and Applications' by John G. Proakis and Dimitris G. Manolakis

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Consult a comprehensive DSP textbook for alternative explanations and deeper insights into the course material.

View Digital Signal Processing on Amazon

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Focus on chapters covering topics like DFT, z-transform, and convolution.
Compare the book's examples with the Scilab implementations learned in the course.
Use the book to clarify any confusing concepts.

Implement Basic Signal Operations in Scilab

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Reinforce your understanding of discrete-time signal operations by implementing them in Scilab. This will solidify your practical skills.

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Write Scilab code to generate unit sample, unit step, and exponential signals.
Implement delay, advance, and folding operations on discrete-time signals.
Plot the resulting signals to visually verify the operations.

Four other activities

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Read 'Signals and Systems' by Alan V. Oppenheim and Alan S. Willsky

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Supplement your learning with a comprehensive textbook on signals and systems to gain a deeper theoretical understanding.

View Signals and Systems (Second Edition) on Amazon

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Read the chapters related to discrete-time signals and systems.
Work through the examples and exercises in the book.
Compare the book's approach to the course materials.

Create a Scilab Tutorial on Convolution

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Deepen your understanding of convolution by creating a tutorial that explains the concept and demonstrates its implementation in Scilab. Teaching others is a great way to learn.

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Explain the mathematical definition of convolution.
Provide Scilab code examples for convolution with and without the predefined function.
Include visualizations to illustrate the convolution process.

Develop a Signal Processing Application in Scilab

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Apply your knowledge by developing a signal processing application in Scilab. This will provide hands-on experience and solidify your understanding of the concepts.

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Choose a signal processing task, such as audio filtering or image processing.
Implement the necessary algorithms in Scilab.
Test and evaluate the performance of your application.

Create a Scilab Function Library

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Consolidate your learning by creating a library of reusable Scilab functions for common signal processing tasks. This will improve your coding efficiency and understanding.

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Collect all the Scilab code examples from the course.
Organize the code into reusable functions with clear documentation.
Test the functions thoroughly and create example usage scenarios.

Career center

Learners who complete Digital Signal Processing using Scilab will develop knowledge and skills that may be useful to these careers:

Signal Processing Engineer

A Signal Processing Engineer designs, develops, and tests signal processing systems and algorithms. The role involves manipulating and analyzing signals to extract meaningful information or improve signal quality. This course helps build a foundation in the fundamental concepts of digital signal processing, including discrete time signals, sampling theorem and Fourier transforms. Furthermore, it covers Scilab implementations of these concepts, such as convolution and correlation, which are essential tools for a signal processing engineer. Familiarity with Scilab and the ability to implement signal processing algorithms will prove extremely helpful. Using Scilab, you will gain hands on experience with discrete Fourier transforms, inverse discrete transforms, and circular convolution.

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Wireless Communications Engineer

A Wireless Communications Engineer designs and develops wireless communication systems and technologies. In this role, you'll need to understand how signals are transmitted, received, and processed wirelessly. This course helps since it covers the fundamentals of discrete-time signals, the sampling theorem, and Fourier transforms, all of which are vital in wireless communications. The Scilab implementations of convolution and correlation provide practical experience in signal analysis, while understanding the z-transform helps analyze the stability and behavior of wireless systems. This is a technical role that rewards those who are mathematically inclined.

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Telecommunications Engineer

A Telecommunications Engineer designs and maintains telecommunications systems, including radio, television, telephone, and data communication networks. In this role, you'll need to understand how signals are transmitted, received, and processed. This course helps by covering the fundamentals of discrete-time signals, the sampling theorem, and Fourier transforms, all of which are vital in telecommunications. The Scilab implementations of convolution and correlation provide practical experience in signal analysis, while understanding the z-transform helps to analyze the stability and behavior of telecommunications systems. The course teaches how to perform circular convolution using the discrete Fourier transform and its inverse.

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Acoustic Consultant

Acoustic Consultants analyze and solve noise and vibration problems in various environments, such as buildings, transportation systems, and industrial facilities. This role requires a strong understanding of sound propagation, noise control techniques, and signal processing methods. This course provides helps build a solid foundation in signal processing concepts that are directly applicable to acoustics, particularly in the context of audio signals. The use of Scilab to perform Fourier transforms and analyze signals in the frequency domain will be quite relevant for analysing acoustic data. The course's coverage of the Discrete Fourier Transform, its inverse, and magnitude and phase responses is particularly relevant.

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Image Processing Engineer

Image Processing Engineers develop algorithms and systems for processing and analyzing images. This role requires a strong understanding of signal processing techniques applied to two-dimensional data. This course will prove useful, as image processing relies heavily on concepts like discrete Fourier transforms and convolution, both of which are covered in the course. The Scilab implementations of these concepts provides a hands-on approach to understanding how these algorithms work in practice. Understanding the sampling theorem and its verification using Scilab are especially important as they determine the quality of image reconstruction and processing.

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Biomedical Engineer

A Biomedical Engineer applies engineering principles to solve medical and healthcare-related problems. This often involves designing medical devices, developing diagnostic tools, and processing biological signals. This course helps build skills in signal processing, which is crucial for analyzing biosignals such as electrocardiograms (ECG) and electroencephalograms (EEG). Understanding discrete-time signals, Fourier transforms, and filtering techniques will enable a Biomedical Engineer to extract meaningful information from noisy biosignals. This course teaches how to verify the sampling theorem and sample analog signals.

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Robotics Engineer

A Robotics Engineer designs, builds, and programs robots and robotic systems. This role requires knowledge of various engineering disciplines, including signal processing for sensor data analysis and control. This course may be useful because signal processing is fundamental to robotics. Understanding discrete-time signals, convolution, and correlation are important for processing sensor data and controlling robot movements. The Scilab implementations allow engineers to prototype and test signal processing algorithms for robotics applications.

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Audio Engineer

An Audio Engineer works with sound to enhance recordings, live performances, or broadcasts. Responsibilities may include recording, mixing, mastering, and sound design. This course may be useful for an audio engineer because audio signals are a specific type application of signal processing. Understanding discrete-time signals, Fourier transforms, and convolution, as covered in the course, helps build a deep awareness of how to manipulate and enhance audio. The Scilab implementations taught in the course directly apply to audio processing tasks, such as filtering and effects processing. An audio engineer will become skilled in working with the discrete Fourier transform and its inverse and the magnitude and phase responses.

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Automation Engineer

An Automation Engineer designs, develops, and implements automated systems to improve efficiency and productivity in various industries. This course may be helpful, as understanding signal processing techniques for sensor data analysis and control is beneficial for this role. Understanding discrete-time signals, convolution, and correlation helps in processing sensor data and controlling automated systems. The Scilab implementations allow engineers to prototype and test signal processing algorithms for automation applications. This engineer may wish to model sensor data or write code for programmable logic controllers.

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Control Systems Engineer

A Control Systems Engineer designs and implements systems that automatically regulate and control processes. These processes can range from simple temperature control to complex robotic systems. This course may be useful because control systems rely heavily on signal processing techniques for analysis and design. Understanding the z-transform, pole-zero plots, and Fourier transforms, as covered in the course, helps to analyze system stability and frequency response. The Scilab implementations allow a control systems engineer to simulate and verify control system designs.

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Research Scientist

A Research Scientist conducts scientific research and experiments to advance knowledge in a specific field. The role often involves designing experiments, collecting and analyzing data, and publishing findings. This course will prove useful to a Research Scientist, as this course may provide useful tools for data analysis and signal processing tasks, depending on the specific area of research. The Scilab implementations provide a hands-on approach to applying these techniques to real-world data. You will learn how to perform circular convolution using the discrete Fourier transform and its inverse. An advanced degree, such as a PhD, is typically required.

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Data Scientist

A Data Scientist analyzes large datasets to extract insights and inform decision-making. While it might not seem immediately apparent, signal processing techniques can be applied to many types of data, and therefore this course may be helpful. Understanding concepts like Fourier transforms and correlation helps in feature extraction and pattern recognition. The Scilab implementations provide a practical way to apply these techniques to real-world datasets. A data scientist may find that they could apply spectral analysis of discrete-time data streams to solve a data problem.

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Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning algorithms and models. This course may be useful as signal processing techniques can be used for feature extraction and data preprocessing in machine learning applications. Understanding concepts like Fourier transforms and correlation can help in identifying relevant features in datasets. The Scilab implementations provide a practical way to apply these techniques to real-world data. A Machine Learning Engineer may use this course to gain experience with spectral data. To succeed, you probably need an advanced degree, like a master's degree.

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Software Engineer

Software Engineers design, develop, and test software applications. They commonly are involved in coding, debugging, and maintaining software systems. Even though it may not be immediately relevant, a software engineer may find this course useful, as this course provides experience with Scilab. This experience can be valuable in the development of applications that involve signal processing. Software engineers who work on audio processing, image processing, or telecommunications software may gain practical experience from this course.

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Test Engineer

Test Engineers plan, design, and execute tests on systems and equipment to ensure they meet quality standards and specifications. This role may require the analysis of signal data to verify performance. This course may be useful because this course provides exposure to signal processing concepts and tools, which are often used in test and measurement equipment. Using Scilab, the test engineer will learn to implement correlation operations. Understanding discrete-time signals, Fourier transforms, and sampling theorem can help engineers analyze test data effectively.

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Digital Signal Processing using Scilab

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