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From Wikipedia, the free encyclopedia

Signal processing is an area of electrical engineering, systems engineering and applied mathematics that deals with operations on or analysis of signals, in either discrete or continuous time to perform useful operations on those signals. Signals of interest can include sound, images, time-varying measurement values and sensor data, for example biological data such as electrocardiograms, control system signals, telecommunication transmission signals such as radio signals, and many others. Signals are analog or digital electrical representations of time-varying or spatial-varying physical quantities. In the context of signal processing, arbitrary binary data streams and on-off signals are not considered as signals, but only analog and digital signals that are representations of analog physical quantities.

Contents

Typical operations and applications

Processing of signals includes the following operations and algorithms with application examples[1]:

In communication systems, signal processing may occur at OSI layer 1, the Physical Layer (modulation, equalization, multiplexing, etc) in the seven layer OSI model, as well as at OSI layer 6, the Presentation Layer (source coding, including analog-to-digital conversion and data compression).

History

According to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal processing can be found in the classical numerical analysis techniques of the 17th century. They further state that the "digitalization" or digital refinement of these techniques can be found in the digital control systems of the 1940s and 1950s. [2]

Mathematical topics embraced by signal processing

Categories of signal processing

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Analog signal processing

Analog signal processing is for signals that have not been digitized, as in classical radio, telephone, radar, and television systems. This involves linear electronic circuits such as passive filters, active filters, additive mixers, integrators and delay lines. It also involves non-linear circuits such as compandors, multiplicators (frequency mixers and voltage-controlled amplifiers), voltage-controlled filters, voltage-controlled oscillators and phase-locked loops.

Discrete time signal processing

Discrete time signal processing is for sampled signals that are considered as defined only at discrete points in time, and as such are quantized in time, but not in magnitude.

Analog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals.

The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.

Digital signal processing

Digital signal processing is for signals that have been digitized. Processing is done by general-purpose computers or by digital circuits such as ASICs, field-programmable gate arrays or specialized digital signal processors (DSP chips). Typical arithmetical operations include fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Other typical operations supported by the hardware are circular buffers and look-up tables. Examples of algorithms are the Fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, Wiener filter and Kalman filter.

Fields of signal processing

Notes and references

  1. ^ Mathematical Methods and Algorithms for Signal Processing, Todd K. Moon, Wynn C. Stirling, Prentice Hall, 2000, ISBN 0-201-36186-8, page 4.
  2. ^ Digital Signal Processing, Prentice Hall, 1975, ISBN 0-13-214635-5, page 5.

External links

Signal Processing for Communications – free online textbook by Paolo Prandoni and Martin Vetterli (2008)


Study guide

Up to date as of January 14, 2010

From Wikiversity

Lessons


Simple English

Signal processing is the analysis, interpretation and manipulation of signals. Signals of interest include sound, images, biological signals such as ECG, radar signals, and many others.

Processing of such signals includes storage and reconstruction, separation of information from noise (e.g., aircraft identification by radar), compression (e.g., image compression), and feature extraction (e.g., speech-to-text conversion).

Signal classification

For analog signals, signal processing may involve the amplification and filtering of audio signals for audio equipment or the modulation and demodulation of signals for [telecommunications]http://en.wikipedia.org/wiki/Telecommunication. For digital signals, signal processing may involve the compression, error checking and error detection of digital signals.

  • Analog signal processing—for signals that have not been digitized, as in classical radio, telephone, radar, and television systems
  • Digital signal processing—for signals that have been digitized. Processing is done by digital circuits such as ASICs, FPGAs, general-purpose microprocessors or computers, or specialized digital signal processor chips.
  • Statistical signal processing—analyzing and extracting information from signals based on their statistical properties
  • Audio signal processing—for electrical signals representing sound, such as music
  • Speech signal processing—for processing and interpreting spoken words
  • Image processing—in digital cameras, computers, and various imaging systems
  • Video signal processing—for interpreting moving pictures
  • Array processing—for processing signals from arrays of sensors

Method of Signal processing

Signal processing is the analysis, interpretation and manipulation of acquired signals. Acquired signals must to be processed depending on the purpose of measurement, a method of measurement, and a property of acquired signals.

When signals are processed, statistics is used because it's essential to know a distribution of data and represent data by numerical formulas. In other words, to study signal processing, it's demanded to study statistics (like the theory of error, the arithmetical mean, probability, a stochastic variable, accuracy, and detailed drawing, etc.).

In most cases, signals are regular, as it is acquired from electric instruments like telemeter, or communications equipment, etc. But there are also many accidentally occurred irregular signals which make it difficult to find formulas that fit exactly. Here, the irregular means it's hard to predict the result which is not yet occurred. When irregular signals are acquired, photon is necessary, so it is measured, and caculated.

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