Information Encoding: Time-domain and Frequency-Domain Encoding in Arduino

A free video tutorial from Israel Gbati
Embedded Firmware Engineer
Rating: 4.2 out of 5Instructor rating
39 courses
89,849 students
Information encoding :  Time-domain and frequency-domain encoding

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Digital Signal Processing(DSP) From Ground Up™ using Arduino

Practical DSP on Arduino : FFT, Filter Design, Convolution, IIR, FIR, Hamming Window, Linear Systems,

07:27:27 of on-demand video • Updated November 2021

Develop and test the Convolution Kernel algorithm on Arduino
Develop and test the Discrete Fourier Transform (DFT) algorithm on Arduino
Develop and test the Inverse Discrete Fourier Transform (IDFT) on Arduino
Develop and test the Fast Fourier Transform (FFT) algorithm on Arduino
Perform spectral analysis on ECG signals using Arduino
Design and develop Windowed-Sinc filters on Arduino
Design and develop Finite Impulse Response (FIR) filters on Arduino
Design and develop Infinite Impulse Response (IIR) filters on Arduino
Use ARM CMSIS-DSP library on Arduino
Develop the FFT-Convolution algorithm on Arduino
Develop the First Difference algorithm on Arduino
Develop the Running Sum algorithm on Arduino
Develop the Moving Average filter algorithm on Arduino
Develop the Recursive Moving Average filter algorithm on Arduino
Develop signal statistical algorithms on Arduino
Build passive Low-pass and High-pass filters
Build Modified Sallen-Key filters
Build Bessel, Chebyshev and Butterworth filters
Understand all about Linear Systems and their characteristics
Suppress noise in signals using Arduino
Give a lecture on Digital Signal Processing (DSP)
English [Auto]
Hello I'll come back. So before we conclude this section on on a field test let's talk a bit about information encoded. There are many ways for information to be included in an analog way from the two most common ways our time to me that could in any frequency domain coded in frequency domain and code and the information is contained in that sinusoidal waves that combined just for the signal. An excellent example of frequency domain encoded signal is for you when we hear sound that perceived sound depends on the frequencies present and not on the particular shape of the waveform. Consequently digitization of these signals usually involves using an anti-aliasing filter such as a filter with a very sharp rule of like the Chevy Sheth or the Butterworth filter although these filters have a box that responds and call that information. Not affected by the steppers response. Of course these filters have excellent frequency response time domain and call it uses the shape of the waveform to store information and a good example of this is easy to you or EKG acquired by the doctor. The shape of the easy to signal provides the information the doctors are looking for. So this is a kind of see you in the next lesson.