Introduction to Digital Signal Processing

by Derrick Corea Technosoft Innovations, Inc

Introduction : 

Digital Signal Processing Applications is already part of our daily lives. It is present in cell phones, in photographic cameras, televisions, automobiles, computers, medical equipment, automation systems, control, communication, etc. Few people, however, are aware of the exact size of the science involved and the complex technology behind it all. For us users, it's all very simple and easy to use. Just press a button.

 

 In this sequence of articles, I will cover this topic very superficially and explain some basic and important topics about digital signal processing. I do not intend to present a course on this subject. That would be very long and tedious. Anyone who really wants to study this subject can consult the following book:

 

Discrete-Time Signal Processing -

Analog vs. Digital

All natural systems or processes are in essence analogous. The development of electronics went the same way: analogical in its essence. Thus, signal conditioning, filtering, and even mathematical operations were implemented analogously. There were no computational resources that could be used efficiently and economically viable for the implementation of more complex equipment. FIGURE 1 illustrates an analog electronic circuit with some complexity.

Like everything else that is natural, electronic components are also subject to variations in environmental conditions such as temperature, humidity, aging, pressure, chemical attack, corrosion, etc. It was necessary to use complex compensation procedures to correct the errors resulting from these variations.

 

Stock photo of 'Printed-circuit board- It is photographed by close up'

With the creation of the first integrated circuits around the early 1960s and the subsequent creation of silicon-integrated microprocessors from 1971 onwards, the way was opened for thinking about digitizing real-world signals and processing them digitally. The most obvious advantage of the digitized signal is that this signal no longer suffers from the variations to which the analog signals are subjected. You can reduce the analog interfaces to a minimum, enough to convert the analog signal to digital accurately and let the microprocessor handle and process that signal. The same is true in case you need to exit to the real world, converting it to an analog signal. At the same time, there was a still unexplored opportunity to develop technologies for digitized signals.

High slide JS

Continuous x Discrete

Analog signals are by nature continuous over time. They are theoretically constituted by a sequence of infinite points. When we digitize these signals, they go through a process known by sampling and are transformed into a finite sequence of discrete points. This transformation is performed by components known as Analog / Digital converters or simply A / D converters. The transformation of the digital signals into analog is carried out by electronic components known as Digital / Analog converters, or D / A converters. In FIGURE 3, an example of the process of transforming the continuous analog signal into a discretized signal can be seen.

 

Continuo_ Discrete

 

FIGURE 3: Transformation of analog signal into discrete

The resulting effect due to the sampling of the signal is visible: The discretized signal is not exactly the same as the analog signal. In FIGURE 3 the discretized signal is a coarse reproduction of the original signal. This has a number of consequences and implications. On the other hand, turning a continuous signal into a discrete signal brings the advantages of being able to tailor the signal size to be processed to the limited memory capacity of the processors. The memory of the processor, however large, is always finite, as is its processing power. Here we have pointed out some of the problems that must be taken into account and resolved when designing digital signal processing systems.

 

After all, what is Digital Signal Processing?

To the layman it may seem that digital signal processing is only possible with the aid of DSPs, silicon integrated processors specially developed for this function. This is an understandable mistake. DSPs are processors with a hardware architecture developed to carry out typical operations of the implementation of digital signal processing procedures. Many manufacturers have adopted an architecture known as Harvard Architecture, where there are separate data and addressing paths for data and instruction memories, a much more modern concept than the traditional Von Neumann architecture. In Harvard Architecture it is possible to address and access in a single instruction more than one memory location simultaneously, in a single machine cycle. Although they have a differentiated architecture, the DSPs can be used as microcontrollers, so much so that there is pressure from some manufacturers of these components, that these DSPs are also adopted for conventional operations. Some have even been renamed DSCs (Digital Signal Controllers), i.e. digital signal controllers.

 

The digital signal processing itself is a technology, a science, a number of abstract concepts which results in application of computational algorithms for performing specific operations on digital data. Some examples of digital signal processing:

·       Digital filtering of signals;

·       Speech recognition and synthesis;

·       Treatment and recognition of patterns in images;

Digital radio and TV

Digital signal processing algorithms can be run on DSPs, specially developed to optimize the performance of these algorithms and enable real-time operations. However, in suitable applications and in suitable applications, it is possible to perform digital signal processing in common microprocessors with low performance or even offline in a personal computer, table t, etc.

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Created on Aug 16th 2019 04:38. Viewed 290 times.

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