Introduction to Digital Signal Processing
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Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.
Circles Sines and Signals - Introduction
Excellent course with lots of interesting real world applications as examples. The course moves quite fast though, and assumes students have some basic knowledge of signals and systems. Excellent teaching of digital signal processing theory and techniques. Even form an engineering student who already has most on the material, is a very good review source.
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Remember, the IF results from the mixing process between RF signals and a variable frequency oscillator, shifting the modulated signal to a much lower frequency on the demodulation path to audio. While many of the same types of filters and effects can be implemented at this stage as at the IF stage, it is usually more effective to perform those functions at the IF filter stage and avoid passing undesired signals further along the receiver processing path. However, DSP can be readily applied to perform audio functions such as equalizing received audio, audio filtering, audio mixing, and speech processing such as compression or expansion.
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The algorithms used in DSP are complex and will not be described in detail in this article. Imagine we have a sample of a very complex RF waveform.
Introduction to Digital Signal Processing
That is, the DSP can compute all the different sine wave component frequencies comprising the complex signal form, as well as the amplitude of each frequency. See sidenote below. The Fourier Transform shows us the set of frequencies and the amplitude of each that sum together to create a more complex or irregularly shaped signal.
With the digital signal data organized into a frequency spectrum representation, the frequencies may be filtered in many different ways. For example, all frequencies above a desired cutoff value can be digitally eliminated amplitude value set to zero , and all frequencies below a different cutoff value can be similarly eliminated, thereby leaving only a narrowed desired band of frequencies, as depicted below.
The high and low cutoff frequencies define the filter width, or passband. This narrower band is DA converted back into analog form for further receiver processing, leaving the undesired signals behind. James marked it as to-read Oct 11, Trulli Octa marked it as to-read Oct 11, Luis Marinheira added it Dec 02, Filipe Sinclair added it Aug 17, There are no discussion topics on this book yet.
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