Digital signal processing
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Digital signal processing (DSP) is concerned with the representation of the signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, biomedical signal processing, seismic data processing, etc.
Since the goal of DSP is usually to measure or filter continuous realworld analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required output signal is another analog output signal, which requires a digital to analog converter. Even if this process is more complex than analog processing and has a discrete value range, the stability of digital signal processing thanks to error detection and correction and being less vulnerable to noise makes it advantageous over analog signal processing for many, though not all, applications. ^{[1]}
DSP algorithms have long been run on standard computers, on specialized processors called digital signal processors (DSPs), or on purposebuilt hardware such as applicationspecific integrated circuit (ASICs). Today there are additional technologies used for digital signal processing including more powerful general purpose microprocessors, fieldprogrammable gate arrays (FPGAs), digital signal controllers (mostly for industrial apps such as motor control), and stream processors, among others.^{[2]}
Contents 
[edit] DSP domains
In DSP, engineers usually study digital signals in one of the following domains: time domain (onedimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. Autocorrelation is defined as the crosscorrelation of the signal with itself over varying intervals of time or space.qq
[edit] Signal sampling
With the increasing use of computers the usage of and need for digital signal processing has increased. In order to use an analog signal on a computer it must be digitized with an analog to digital converter (ADC). Sampling is usually carried out in two stages, discretization and quantization. In the discretization stage, the space of signals is partitioned into equivalence classes and quantization is carried out by replacing the signal with representative signal of the corresponding equivalence class. In the quantization stage the representative signal values are approximated by values from a finite set.
The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal. In practice, the sampling frequency is often significantly more than twice the required bandwidth.
A digital to analog converter (DAC) is used to convert the digital signal back to analog. The use of a digital computer is a key ingredient in digital control systems.
[edit] Time and space domains
The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Filtering generally consists of some 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 "linear" filter is a linear transformation of input samples; other filters are "nonlinear." Linear filters satisfy the superposition condition, i.e. if an input is a weighted linear combination of different signals, the output is an equally weighted linear combination of the corresponding output signals.
 A "causal" filter uses only previous samples of the input or output signals; while a "noncausal" filter uses future input samples. A noncausal filter can usually be changed into a causal filter by adding a delay to it.
 A "timeinvariant" filter has constant properties over time; other filters such as adaptive filters change in time.
 Some filters are "stable", others are "unstable". A stable filter produces an output that converges to a constant value with time, or remains bounded within a finite interval. An unstable filter can produce an output that grows without bounds, with bounded or even zero input.
 A "finite impulse response" (FIR) filter uses only the input signal, while an "infinite impulse response" filter (IIR) uses both the input signal and previous samples of the output signal. FIR filters are always stable, while IIR filters may be unstable.
Most filters can be described in Zdomain (a superset of the frequency domain) by their transfer functions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response. The output of an FIR filter to any given input may be calculated by convolving the input signal with the impulse response. Filters can also be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions.
[edit] Frequency domain
Signals are converted from time or space domain to the frequency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. 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.
Filtering, particularly in non realtime work can also be achieved by converting to the frequency domain, applying the filter and then converting back to the time domain. This is a fast, O(n log n) operation, and can give essentially any filter shape 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 frequency components with smaller magnitude while retaining the order of magnitudes of frequency components.
Frequency domain analysis is also called spectrum or spectral analysis.
[edit] Applications
The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, RADAR, SONAR, seismology, and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room matching equalization of sound in Hifi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, computergenerated animations in movies, medical imaging such as CAT scans and MRI, MP3 compression, image manipulation, high fidelity loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.
[edit] Implementation
Digital signal processing is often implemented using specialised microprocessors such as the DSP56000, the TMS320, or the SHARC. These often process data using fixedpoint arithmetic, although some versions are available which use floating point arithmetic and are more powerful. For faster applications FPGAs^{[3]} might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge from companies including Freescale and startup Stream Processors, Inc. For faster applications with vast usage, ASICs might be designed specifically. For slow applications, a traditional slower processor such as a microcontroller may be adequate.
[edit] Techniques
[edit] Related fields
 Analog signal processing
 Automatic control
 Computer Engineering
 Computer Science
 Data compression
 Electrical engineering
 Information theory
 Telecommunication
[edit] References
 ^ James D. Broesch, Dag Stranneby and William Walker. Digital Signal Processing: Instant access. ButterworthHeinemann. p. 3.
 ^ Dag Stranneby and William Walker (2004). Digital Signal Processing and Applications (2nd ed. ed.). Elsevier. http://books.google.com/books?id=NKK1DdqcDVUC&pg=PA241&dq=called+digital+signal+processor+hardware+applicationspecific+integrated+circuit+generalpurpose+microprocessors+fieldprogrammable+gate+arrays+dsp+asic+fpga+stream&as_brr=3&ei=cA0mSLWwB4qIswOEvsC5DQ&sig=EwJs3lv92Kai7Xw0i2XG9UpBVuA.
 ^ JpFix. "FPGABased Image Processing Accelerator". http://www.jpfix.com/About_Us/Articles/FPGABased_Image_Processing_Ac/fpgabased_image_processing_ac.html. Retrieved on 20080510.
[edit] Further reading
 Alan V. Oppenheim, Ronald W. Schafer, John R. Buck : DiscreteTime Signal Processing, Prentice Hall, ISBN 0137549202
 Boaz Porat: A Course in Digital Signal Processing, Wiley, ISBN 0471149616
 Richard G. Lyons: Understanding Digital Signal Processing, Prentice Hall, ISBN 0131089897
 Jonathan Yaakov Stein, Digital Signal Processing, a Computer Science Perspective, Wiley, ISBN 0471295469
 Sen M. Kuo, WoonSeng Gan: Digital Signal Processors: Architectures, Implementations, and Applications, Prentice Hall, ISBN 0130352144
 Bernard Mulgrew, Peter Grant, John Thompson: Digital Signal Processing  Concepts and Applications, Palgrave Macmillan, ISBN 0333963563
 Steven W. Smith: Digital Signal Processing  A Practical Guide for Engineers and Scientists, Newnes, ISBN 075067444X
 Paul A. Lynn, Wolfgang Fuerst: Introductory Digital Signal Processing with Computer Applications, John Wiley & Sons, ISBN 0471979848
 James D. Broesch: Digital Signal Processing Demystified, Newnes, ISBN 1878707167
 John G. Proakis, Dimitris Manolakis: Digital Signal Processing  Principles, Algorithms and Applications, Pearson, ISBN 0133942899
 Hari Krishna Garg: Digital Signal Processing Algorithms, CRC Press, ISBN 0849371783
 P. Gaydecki: Foundations Of Digital Signal Processing: Theory, Algorithms And Hardware Design, Institution of Electrical Engineers, ISBN 0852964315
 Paul M. Embree, Damon Danieli: C++ Algorithms for Digital Signal Processing, Prentice Hall, ISBN 0131791443
 Anthony Zaknich: Neural Networks for Intelligent Signal Processing, World Scientific Pub Co Inc, ISBN 9812383050
 Vijay Madisetti, Douglas B. Williams: The Digital Signal Processing Handbook, CRC Press, ISBN 0849385725
 Stergios Stergiopoulos: Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging RealTime Systems, CRC Press, ISBN 0849336910
 Joyce Van De Vegte: Fundamentals of Digital Signal Processing, Prentice Hall, ISBN 0130160776
 Ashfaq Khan: Digital Signal Processing Fundamentals, Charles River Media, ISBN 1584502819
 Jonathan M. Blackledge, Martin Turner: Digital Signal Processing: Mathematical and Computational Methods, Software Development and Applications, Horwood Publishing, ISBN 1898563489
 Bimal Krishna, K. Y. Lin, Hari C. Krishna: Computational Number Theory & Digital Signal Processing, CRC Press, ISBN 0849371775
 Doug Smith: Digital Signal Processing Technology: Essentials of the Communications Revolution, American Radio Relay League, ISBN 0872598195
 Henrique S. Malvar: Signal Processing with Lapped Transforms, Artech House Publishers, ISBN 0890064679
 Charles A. Schuler: Digital Signal Processing: A HandsOn Approach, McGrawHill, ISBN 0078297443
 James H. McClellan, Ronald W. Schafer, Mark A. Yoder: Signal Processing First, Prentice Hall, ISBN 0130909998
 Artur Krukowski, Izzet Kale: DSP System Design: Complexity Reduced Iir Filter Implementation for Practical Applications, Kluwer Academic Publishers, ISBN 1402075588
 John G. Proakis: A SelfStudy Guide for Digital Signal Processing, Prentice Hall, ISBN 0131432397

