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Signal Computing: Digital Signals in the Software Domain pdf

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Signal Computing: Digital Signals in the Software Domain.



As computers have become ubiquitous, they have also become more and more embedded not only in the devices we own and use but in our lives. As a result, computers become embedded in the physical world, with their primary purpose being to detect and analyze happenings in our world and to produce responses that afect that world. As computing professionals, we need to understand how computers can process information from the physical world as digital signals: multimedia (sound, images, video) and other measurements (in medical instruments, cars, cell phones, eyeglasses, etc). 

This is why we have chosen to coin the phrase “Signal Computing”. Digital signals place great demands on processing power, network bandwidth, storage capacity, I/O speed, and software design. As a result, signal computing is a great laboratory for exercising the full range of knowledge of computer science. 

In this book, you will learn how digital signals are captured, represented, processed, communicated, and stored in computers. The specific topics we will cover include: physical properties of the source information (such as sound or images), devices for information capture (microphones, cameras), digitization, compression, digital signal representation (JPEG, MPEG), digital signal processing (DSP), signal analysis and feature extraction via re-representation as functions of frequency, and network communication. By the end of this book, you should understand the problems and solutions facing signal computing systems development in the areas of data structures and algorithms, data analytics, feature extraction, information retrieval, user interfaces, and communications. 

While there certainly may be many opportunities for you to work in signal computing, the value of this study extends far beyond. Studying signal computing and its underlying mathematics directly exercises key computer science abilities in areas like abstraction and algorithmics. You will see that this book interleaves mathematical topics with applications and algorithms. At each step of the way, we take a representation of digital signals or operations on them that you are familiar with, reach a concept in which it is awkward or dicult to use, and then develop an alternative representation that simplifies matters. This is exactly what computing professionals do in their careers — identify that a problem at hand can be represented by some abstraction with known properties that can be manipulated by well-understood algorithms. We hope that the journey you take through the mathematical abstractions here will not only give you an important set of tools to use later on, but will also help exercise your fundamental ability to move from one representation to another. 

Beyond your own personal professional capabilities, we hope that this book will also give you a better understanding of the design process that electrical engineers go through when they design the details of signal processing systems, such as filters. Even if you do not go on to build software components that perform signal processing, there is a good chance that you will work on large systems that have DSP components. We believe that it will be invaluable for you to understand the basics of how such DSP components work and what your electrical engineering colleagues are working on.


Objectives:

By the end of this book, you should know:

• What physical signals are like in the “real” world and how their properties afect how we perceive them.

• How these signals are digitized and the tradeofs among sampling speed, levels of uantization, file size, etc.

• How to perform simple signal filtering to remove noise, emphasize important features, etc. You should be well-prepared to work with electrical engineers in the design of

more advanced signal processing systems. 

• How to carry out simple time-series analysis techniques to analyze frequency and characterize unknown signals.

• How multimedia file sizes can be reduced by compression, and the tradeofs among compression, processing overhead, and media quality


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