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In this course digital signal processing topics will be explained both theoretically and using MATLAB programming. The sampling opeation will be explained both in time domain and frequency domain. Upsampling and downsampling operations will be explained in details. Reconstruction of analog signals from digital signals is another topic to be covered in this course. Discrete Fourier transform is covered in details. The design of analog and digital IIR filters is covered in this course.
In this lecture, the outline of the course is provided.
In this lecture, sampling operation on a continuous time signal is explained, and the meaning of sampling frequency is elaborated.
In this lecture, sampling operation in time domain is explained using MATLAB programming
Using MATLAB we illustrate the meaning of sampling frequency
In this lecture, we derive some time domain formulas for the sampling operation.
In this lecture, frequency domain formulas for sampling operation are derived.
Fourier transform calculation of continuous time signals are explained by MATLAB
In this lecture, Fourier transform of DIGITAL signals will be calculated using MATLAB programming
In this lecture, we draw the graph of the Fourier transform of the product signal xs(t)=xc(t) x s(t) where xc(t) is the continuous time signal to be samples and s(t) is the impulse train
The graph of the Fourier transform of the product signal is drawn for overlapping cases
In this lecture, we will derive the mathematical expressions for the reconstruction of an analog signal from its samples.
In this lecture, reconstruction of an analog signal from its samples is illustrated by an example
In this lecture, we explain how to write a MATLAB code for the reconstruction of an analog signal from its samples. We illustrate the concept with an example.
In this lecture, we illustrate the effect of sampling frequency on the reconstructed signal using a MATLAB code. The MATLAB code is run using different sampling frequencies, and the reconstructed signals are compared.
In this lecture, we will explain how to approximate the reconstruction filter and obtain its linear version, then illustrate its use by an example.
In this lecture, we will explain the downsampling operation performed for digital signals in time domain.
In this lecture, we explain the downsampling operation in time domain using MATLAB
In this lecture, we inspect the spectrum of the downsampled signals in MATLAB
In this lecture, we will derive the mathematical expression for the Fourier transform of the downsampled signal.
In this lecture, we verify the Fourier transform of the downsampled signal mathematically.
In this lecture, we explain how to draw the spectrum of downsampled signals by examples.
In this lecture, we explain the aliasing in downsampled signal, and derive a criteria for the downsampling factor.
In this lecture, we interpret the sampling period of the downsampled signal.
In this lecture, we explain decimator filter used before downsampling operation.
In this lecture, we explain the decimation filter using MATLAB
In this lecture, we explain the upsampling of digital signals in time domain.
In this lecture, we inspect the Fourier transform of upsampled signal.
In this lecture, we explain the interpolation of digital signals.
In this lecture, we explain the approximated interpolation filter, and provide an example for the interpolation of a digital signal.
Sample-and-old operation and quantization process are explained.
This lecture is a continuation of the previous lecture, and in this lecture we explain practical D/C converters.
We explain time-shifting, time-scaling, and rotation operations for non-periodic and periodic digital signals.
We continue explaining the manipulation of periodic signals involving combined shifting and scaling operations.
In this lecture, we refresh our knowledge about the Fourier transforms of the periodic and non-periodic continuous and digital signals.
In this lecture, we explain the topics Periodic convolution and sampling of Fourier transform
In this lecture, we show how to calculate the DFT and inverse DFT coefficients of non-periodic digital signals. We derive necessary formulas, and solve numerical examples for clear illustration.
The previous lecture is continued
In this lecture, we explain the aliasing problem in time domain, and talk about some properties of discrete Fourier series coefficients
In this lecture, we explain how to calculate the circular convolution of two non-periodic digital signal.
In this lecture, we review some fundamental concepts like LTI systems, Z-transform, Laplace transform, stability, causality, etc.
In this lecture, we explain the transformation of a continuous time system to a discrete time system.
In this lecture, we provide information about practical analog filters, and explain how to design a low-pass Butterworth analog filter. We also provide a numerical example for low-pass Butterworth analog filter design
In this lecture, we explain how to design a digital IIR lowpass filter.
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Course is well-structured, covering key fundamental topics.
Practical examples using MATLAB are highly praised.
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