Welcome to the world of digital signal processing (DSP), where the possibilities are endless, but Ah! The technical language in this field is a labyrinth of jargon that makes even the most seasoned hackers scratch their head. The Nyquist-Shannon Theoreom, FIR, IIR, Cooley-Tukey FFT, Hamming Window. What do these even mean?
Yet fear not! This guide is here to help you to navigate this maze of DSP jargon and emerge victorious on the other side. DSP has the power to change the game in many industries like music production, telecommunications, and medical instruments. But crossing the barrier of technical language and complex math can be daunting. That's why this guide is created - to make DSP accessible to all.
Enginners had day-dreamed about the possibility of using digital hardware for filtering signals, as early as World War II. But the technology at the time was prohibitively expensive and impractical. But along with the growth of digital computers, something happend in 1965 that changed everything.
In that year, James Cooley (of IBM) and John Tukey (of Princeton) published their revolutionary article, proposing an algorithm which promised unparalleled speed and efficiency. This algorithm later became known as Fast Fourier Transform (Cooley-Tukey FFT) Algorithm. Suddenly, the idea of digital filtering became not only feasible but downright attractive.
Although the FFT algorithm was a game-changer, the development of a comprehensive theory was already well underway at that time. Cooley and Tukey's work demonstrated that digital filtering could be the more economical option in the long run, paving the way for the DSP revolution that we enjoy today.
Digital Signal Processing is an incredibly vast and diverse field. Encompassing everything from audio to image processing, speech and image recognition, to applications in medical and seismic sciences. However, at a high level, DSP can be divided into two primary categories: digital filtering and spectrum analysis
Digital filtering is about designing and implementing tailormade filters that can remove unwanted noise or frequencies from a signal. Examples include finite impulse response (FIR) and infinite impulse response (IIR) filters. These filters can be designed by various techniques, like windowing or frequency sampling and can be implemented using hardware, software or a combination of both.
Spectrum analysis, on the other hand, involves analyzing the frequency components of a signal. This can be done using popular algorithms like the FFT. Statistical methods can also be used, such as autocorrelation and power spectral density estimation.
More recently, wavelet transformations have emerged as a powerful tool for DSP. Wavelets are mathematical functions that can be used to analyze signals at different scales or resolutions, making them particularly useful in applications such as image compression and denoising.
DSP is a dynamic and exciting field with endless possibilities, and these categories are just the tip of the iceberg.
The guide is going to cover the basic concepts of DSP, including Fourier transforms, digital filtering, and signal processing algorithms. Later, we might also delves into more advanced topics, such as image processing and speech recognition.
This repository will be regularly updated with new chapters and materials, making it a go-to resource for anyone interested in mastering DSP, albeit over time!
To access each module, simply navigate to the respective folder.
| Module | Description |
|---|---|
| Fundamentals of Linear Systems | Discussing Linearity, Time-Invariance, Convolution, Impulse-Response, LTI Systems, Causality, Stability, Frequency Response |
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