Learn Signal Processing 2024 – Best Signal Processing Courses & Best Signal Processing Tutorials & Best Signal Processing Books

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Best Signal Processing Courses 2021

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

Digital Signal Processing (DSP) From Ground Up in Python

$129.99 in stock

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as of October 8, 2020 4:12 pm

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...

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 manner. The aim of this course is to present practical techniques while avoiding the obstacles of abstract mathematical theories. To achieve this goal, DSP techniques are explained in plain language, and not simply proven to be true by mathematical derivations.

While keeping it simple, this course is available in different programming languages and hardware architectures so that students can put the techniques into 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 to develop the kernel convolutional algorithm in python, develop 17 different 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 Chebyshev type I filters in python, develop Chebyshev type II filters in python, perform spectral analysis on ECG signals in python, develop Butterworth filters in python, develop match filters in python, simulate LTI (Linear Time Invariant) in python, even give a talk on DSP and much more. Please see the full course schedule.

You will learn:

Developing the convolution kernel algorithm in Python
Design and develop 17 different window filters in Python
Develop the Discrete Fourier Transformation (DFT) algorithm in Python
Design and develop type I Chebyshev filters in Python
Design and develop Chebyshev type II filters in Python
Developed the IDFT (Inverse Discrete Fourier Transform) 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

Signal processing problems, solved in MATLAB and in Python

$34.99 in stock

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as of October 8, 2020 4:12 pm

Why you need to learn digital signal processing. Nature is mysterious, beautiful, and complex. Trying to understand nature is deeply rewarding, but also deeply challenging. One of the big challenges in studying nature is data analysis. Nature likes to mix many sources of signals and many sources...

Why you should learn digital signal processing.

Nature is mysterious, beautiful and complex. Trying to understand nature is deeply rewarding, but also deeply stimulating. One of the great challenges of studying nature is data analysis. Nature likes to mix many signal sources and many noise sources in the same recordings, which makes your job difficult.

Therefore, one of the most important goals of time series analysis and signal processing is to denoise: to separate the signals and noise that are mixed in the same data channels.

The big idea of DSP (digital signal processing) is to uncover the mysteries hidden in time series data, and this course will teach you the most commonly used discovery strategies.

What is the special feature of this course?

The main focus of this course is the implementation of signal processing techniques in MATLAB and Python. Some theories and equations are presented, but I imagine you are reading this because you want to implement DSP techniques on real signals, and not just go back to the abstract theory.

The course includes over 10,000 lines of MATLAB and Python code, along with sample datasets, which you can use to learn and adapt to your own courses or applications.

In this course, you will also learn to simulate signals in order to test and learn more about your signal processing and analysis methods.

You will also learn how to work with loud or corrupt signals.

Are there any prerequisites?

You need some programming experience. I browse the videos in MATLAB, and you can also follow along using Octave (a free, cross-platform program that emulates MATLAB). I provide the corresponding Python code if you prefer Python. You can use any other language, but you will have to do the translation yourself.

I recommend taking my Fourier Transform course before or alongside this course. However, this is not a requirement and you can pass this course without taking the Fourier Transformation course.

Master the Fourier transform and its applications

$29.99 in stock

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as of October 8, 2020 4:12 pm

The Fourier transform is one of the most important operations in signal processing and modern technology, and therefore in modern humancivilization. But how does it work, andwhy does it work? What you will learn in this course: You will learn the theoretical and computationalbases of the Fourier...

The Fourier transform is one of the most important operations in signal processing and modern technology, and therefore in modern human civilization. But how does it work and why does it work?

What you will learn in this course:

You will learn the theoretical and computational foundations of the Fourier transform, with an emphasis on how the Fourier transform is used in modern applications of signal processing, data analysis, and image filtering. The course covers not only the basics, but also advanced topics including the effects of non-stationarities, spectral resolution, normalization, filtering. All videos come with MATLAB and Python code for you to learn and adapt!

This course focuses on implementations of the Fourier transform on computers and digital signal processing (1D) and image processing (2D) applications. I don’t go into the details of setting up and solving integration issues to get analytical solutions. Thus, this course is more on the computer / data science / engineering side of things, rather than the pure math / differential equations / infinite series side.