Digital Signal Processing (DSP) From Ground Up™ in Python

Digital Signal Processing (DSP) From Ground Up& in Python, available at $64.99, has an average rating of 4.3, with 142 lectures, based on 871 reviews, and has 5470 subscribers.

You will learn about Develop the Convolution Kernel algorithm in Python Design and develop 17 different window filters in Python Develop the Discrete Fourier Transform (DFT) algorithm in Python Design and develop Type I Chebyshev filters in Python Design and develop Type II Chebyshev filters in Python Develop the Inverse Discrete Fourier Transform (IDFT) algorithm in Pyhton Develop the Fast Fourier Transform (FFT) algorithm in Python Perform spectral analysis on ECG signals in Python Design and develop Windowed-Sinc filters in Python Design and develop Finite Impulse Response (FIR) filters in Python Design and develop Infinite Impulse Response (IIR) filters in Python Develop the First Difference algorithm in Python Develop the Running Sum algorithm in Python Develop the Moving Average filter algorithm in Python Develop the Recursive Moving Average filter algorithm in Python Design and develop Butterworth filters in Python Design and develop Match filters in Python Design and develop Bessel filters in Python Simulate Linear Time Invariant (LTI) Systems in Python Perform linear and cubic interpolation in Python This course is ideal for individuals who are People working in the field of signal processing or University students taking classes in signal processing or Python developers who wish to expand their skills or People who want to understand signal processing practically and apply it to their respective fields. It is particularly useful for People working in the field of signal processing or University students taking classes in signal processing or Python developers who wish to expand their skills or People who want to understand signal processing practically and apply it to their respective fields.

Enroll now: Digital Signal Processing (DSP) From Ground Up& in Python

Summary

Title: Digital Signal Processing (DSP) From Ground Up& in Python

Price: $64.99

Average Rating: 4.3

Number of Lectures: 142

Number of Published Lectures: 141

Number of Curriculum Items: 142

Number of Published Curriculum Objects: 141

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

Develop the Convolution Kernel algorithm in Python

Design and develop 17 different window filters in Python

Develop the Discrete Fourier Transform (DFT) algorithm in Python

Design and develop Type I Chebyshev filters in Python

Design and develop Type II Chebyshev filters in Python

Develop the Inverse Discrete Fourier Transform (IDFT) algorithm in Pyhton

Develop the Fast Fourier Transform (FFT) algorithm in Python

Perform spectral analysis on ECG signals in Python

Design and develop Windowed-Sinc filters in Python

Design and develop Finite Impulse Response (FIR) filters in Python

Design and develop Infinite Impulse Response (IIR) filters in Python

Develop the First Difference algorithm in Python

Develop the Running Sum algorithm in Python

Develop the Moving Average filter algorithm in Python

Develop the Recursive Moving Average filter algorithm in Python

Design and develop Butterworth filters in Python

Design and develop Match filters in Python

Design and develop Bessel filters in Python

Simulate Linear Time Invariant (LTI) Systems in Python

Perform linear and cubic interpolation in Python

Who Should Attend

People working in the field of signal processing

University students taking classes in signal processing

Python developers who wish to expand their skills

People who want to understand signal processing practically and apply it to their respective fields.

Target Audiences

People working in the field of signal processing

University students taking classes in signal processing

Python developers who wish to expand their skills

People who want to understand signal processing practically and apply it to their respective fields.

With a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Digital Signal Processing (DSP) in an engaging and easy to follow way. The goal of this course is to present practical techniques while avoiding? obstacles of abstract mathematical theories. To achieve this goal, the DSP techniques are explained in plain language, not simply proven to be true through mathematical derivations.

Still keeping it simple, this course comes in different programming languages and hardware architectures so that students can put the techniques to practice using a programming language or hardware architecture? of their choice. This version of the course uses the Python programming language.

By the end of this course you should be able develop the Convolution Kernelalgorithm in python,? develop 17different? types? of window? filters in python, develop the Discrete Fourier Transform (DFT) algorithm in python, develop the Inverse Discrete Fourier Transform (IDFT) algorithm in pyhton, design and develop Finite Impulse Response (FIR) filters in python, design and develop Infinite Impulse Response (IIR) filters in python, develop Type I Chebyshev filters in python, develop Type II Chebyshev filters in python, perform spectral analysis on ECGsignals in python,? develop Butterworthfilters in python, develop Match filters in python,simulate Linear Time Invariant (LTI) Systems in python, even give a lecture on DSP and so much more. Please take a look at the full course curriculum.

Course Curriculum

Chapter 1: Set Up

Lecture 1: Downloading Python

Lecture 2: Installing Python

Lecture 3: Using IDLE

Lecture 4: Installing Python packages

Lecture 5: Testing the packages

Chapter 2: Python Essentials

Lecture 1: Printing statements

Lecture 2: Variables

Lecture 3: Lists

Lecture 4: Operators

Lecture 5: Conditions

Lecture 6: For Loops

Lecture 7: While Loops

Lecture 8: Functions

Lecture 9: Dictionaries

Lecture 10: Classes and Objects

Chapter 3: Signal Statistics and Noise

Lecture 1: Signal Statistics and Noise

Lecture 2: Coding : Plotting signals with pyplot

Lecture 3: Coding : Importing signals and dealing with subplots

Lecture 4: Coding : Generating signals

Lecture 5: Mean and Standard Deviation

Lecture 6: Coding : Computing the Signal Mean

Lecture 7: Coding : Developing the Signal Mean algorithm

Lecture 8: Coding : Computing the Signal Variance

Lecture 9: Coding : Developing the Signal Variance algorithm

Lecture 10: Coding : Computing the Standard Deviation

Lecture 11: Coding : Developing the Signal Standard Deviation algorithm

Chapter 4: Quantization and The Sampling Theorem

Lecture 1: Nyquist Theorem ( Sampling Theorem )

Lecture 2: The Passive Low-Pass Filter

Lecture 3: The Passive High-Pass Filter

Lecture 4: The Active Filter

Lecture 5: The Bessel, Chebyshev and Butterworth filters

Chapter 5: Linear Systems and Superposition

Lecture 1: Introduction to Linear Systems

Lecture 2: Understanding Superposition

Lecture 3: Impulse and Step Decomposition

Chapter 6: Convolution

Lecture 1: Introduction to Convolution

Lecture 2: The Convolution Operation

Lecture 3: Examinging the Output of Convolution

Lecture 4: The Convolution Sum Equation

Lecture 5: A Closer look at the Delta function

Lecture 6: Coding : Examining the signals

Lecture 7: Coding : Computing the convolution of two signals

Lecture 8: Coding : Developing the Convolution algorithm

Lecture 9: Coding : Computing the De-convolution of two signals

Lecture 10: Coding : Correlation

Lecture 11: The Identity property of convolution

Lecture 12: The Running Sum and First Difference

Lecture 13: Coding : Computing the running sum of a signal

Lecture 14: Coding : Developing the Running Sum algorithm

Lecture 15: Coding : Computing the First Difference of a signal

Lecture 16: Coding : Developing the First Difference algorithm

Chapter 7: Fourier Transform

Lecture 1: Introduction to Fourier Analysis

Lecture 2: The DFT Engine

Lecture 3: Understanding Forward and Inverse DFT

Lecture 4: Coding : Developing the Discrete Fourier Transform (DFT) algorithm

Lecture 5: Coding : Developing the DFT magnitude algorithm

Lecture 6: Coding : Developing the Inverse Discrete Fourier Transform (IDFT) algorithm

Lecture 7: Coding : Computing the IDFT of an ECG signal

Lecture 8: Symmetry between Time domain and frequency domain -Duality

Lecture 9: Polar Notation

Lecture 10: Introduction to Spectral Analysis

Lecture 11: The Frequency Response

Chapter 8: Complex Numbers

Lecture 1: The Complex Number System

Lecture 2: Polar Representation of Complex Numbers

Lecture 3: Eulers Relation

Lecture 4: Representation of Sinusoids

Lecture 5: Representing Systems

Chapter 9: Complex Fourier Transform

Lecture 1: Introduction to Complex Fourier Transform

Lecture 2: Mathematical Equivalence

Lecture 3: The Complex DFT Equation

Lecture 4: Comparing Real DFT and Complex DFT

Chapter 10: Fast Fourier Transform (FFT)

Lecture 1: An Overview of how FFT works.

Lecture 2: Understanding the complexity of calculating DFT directly

Lecture 3: How the Decimation -in-Time FFT Algorithm works

Lecture 4: Coding : Computing the FFT of a signal

Chapter 11: Digital Filter Design

Lecture 1: Introduction to Digital Filters

Lecture 2: The Filter Kernel

Lecture 3: The Impulse,Step and Frequency response

Lecture 4: Understanding the Logarithmic scale and decibels

Lecture 5: Information representations of a signal

Lecture 6: Time domain parameters

Lecture 7: Frequency domain parameters

Lecture 8: Designing digital filters using the spectral inversion method

Lecture 9: Designing digital filters using the spectral reversal method

Lecture 10: Classification of digital filters

Chapter 12: Designing Finite Impulse Response (FIR) Filters

Lecture 1: The Moving Average Filter

Lecture 2: The Multiple Pass Moving Average Filter

Lecture 3: The Recursive Moving Average Filter

Lecture 4: Coding : Smoothing signals with the median filter

Instructors

Israel Gbati
Embedded Firmware Engineer

BHM Engineering Academy
21st Century Engineering Academy

Rating Distribution

1 stars: 17 votes

2 stars: 39 votes

3 stars: 136 votes

4 stars: 301 votes

5 stars: 378 votes

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