- 1st Edition
- Digital Signal Processing: A Practical Guide for Engineers and Scientists
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- ISBN 13: 9780750674447
- Digital signal processing - Wikipedia

### 1st Edition

This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Covers all major DSP topics Full of insider information and shortcuts Basic techniques and algorithms explained without complex numbers "synopsis" may belong to another edition of this title.

Book Description : Will help design engineers on the job master the tough but essential subject of digital signal processing. Buy New Learn more about this copy. Other Popular Editions of the Same Title. Search for all books with this author and title. Customers who bought this item also bought.

## Digital Signal Processing: A Practical Guide for Engineers and Scientists

Stock Image. New Paperback Quantity Available: Book Depository hard to find London, United Kingdom. Seller Rating:. Digital Signal Processing Smith, Steven. Published by Newnes New Quantity Available: 2.

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New Paperback Quantity Available: 1. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:. A filter can be represented by a block diagram , which can then be used to derive a sample processing algorithm to implement the filter with hardware instructions.

A filter may also be described as a difference equation , a collection of zeros and poles or an impulse response or step response. The output of a linear digital filter to any given input may be calculated by convolving the input signal with the impulse response. Signals are converted from time or space domain to the frequency domain usually through use of the Fourier transform.

## ISBN 13: 9780750674447

The Fourier transform converts the time or space information to a magnitude and phase component of each frequency. With some applications, how the phase varies with frequency can be a significant consideration. Where phase is unimportant, often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared. The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing.

Frequency domain analysis is also called spectrum- or spectral analysis. Filtering, particularly in non-realtime work can also be achieved in the frequency domain, applying the filter and then converting back to the time domain.

This can be an efficient implementation and can give essentially any filter response including excellent approximations to brickwall filters. There are some commonly-used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform.

This emphasizes the harmonic structure of the original spectrum.

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The Z-transform provides a tool for analyzing stability issues of digital IIR filters. It is analogous to the Laplace transform , which is used to design and analyze analog IIR filters. In numerical analysis and functional analysis , a discrete wavelet transform DWT is any wavelet transform for which the wavelets are discretely sampled.

As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information. The accuracy of the joint time-frequency resolution is limited by the uncertainty principle of time-frequency. Applications of DSP include audio signal processing , audio compression , digital image processing , video compression , speech processing , speech recognition , digital communications , digital synthesizers , radar , sonar , financial signal processing , seismology and biomedicine.

Specific examples include speech coding and transmission in digital mobile phones , room correction of sound in hi-fi and sound reinforcement applications, weather forecasting , economic forecasting , seismic data processing, analysis and control of industrial processes , medical imaging such as CAT scans and MRI , MP3 compression, computer graphics , image manipulation , audio crossovers and equalization , and audio effects units. DSP algorithms may be run on general-purpose computers and digital signal processors. Additional technologies for digital signal processing include more powerful general purpose microprocessors , field-programmable gate arrays FPGAs , digital signal controllers mostly for industrial applications such as motor control , and stream processors.

## Digital signal processing - Wikipedia

For systems that do not have a real-time computing requirement and the signal data either input or output exists in data files, processing may be done economically with a general-purpose computer. This is essentially no different from any other data processing , except DSP mathematical techniques such as the FFT are used, and the sampled data is usually assumed to be uniformly sampled in time or space.

An example of such an application is processing digital photographs with software such as Photoshop. When the application requirement is real-time, DSP is often implemented using specialized or dedicated processors or microprocessors, sometimes using multiple processors or multiple processing cores. These may process data using fixed-point arithmetic or floating point. For more demanding applications FPGAs may be used.

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This immensely practical and thorough book has been written for engineers and scientists in a wide variety of fields, with the goal of explaining DSP techniques while avoiding the barriers of abstract theory and detailed mathematics, enabling readers to put the powerful tools of DSP to work in their research and designs. All techniques are explained through analogies, not simply proven to be true through mathematical derivations. The math is included where it is needed, but it is not used as the primary means of conveying the information.

The illustrations are profuse and help to support the text in creative ways.

Numerous detailed applications examples from many fields bring the theory to life. The coverage is thorough, detailed, and enlightening.

Best book on the topic; teaches you what DSP means. Steven Smith. Statistics Probability and Noise. Properties of Convolution. The Discrete Fourier Transform.