Signal processing
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Signal processing is an electrical engineering subfield that focuses on analysing, modifying, and synthesizing signals such as sound, images, and scientific measurements.^{[1]} Signal processing techniques can be used to improve transmission, storage efficiency and subjective quality and to also emphasize or detect components of interest in a measured signal.^{[2]}
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 digital refinement of these techniques can be found in the digital control systems of the 1940s and 1950s.^{[3]}
In 1948, Claude Shannon wrote the influential paper "A Mathematical Theory of Communication" which was published in the Bell System Technical Journal.^{[4]} The paper laid the groundwork for later development of information communication systems and the processing of signals for transmission.^{[5]}
Signal processing matured and flourished in the 1960s and 1970s, and digital signal processing became widely used with specialized digital signal processor chips in the 1980s.^{[5]}
Categories
Analog
Analog signal processing is for signals that have not been digitized, as in most 20thcentury radio, telephone, radar, and television systems. This involves linear electronic circuits as well as nonlinear ones. The former are, for instance, passive filters, active filters, additive mixers, integrators, and delay lines. Nonlinear circuits include compandors, multipliers (frequency mixers, voltagecontrolled amplifiers), voltagecontrolled filters, voltagecontrolled oscillators, and phaselocked loops.
Continuous time
Continuoustime signal processing is for signals that vary with the change of continuous domain (without considering some individual interrupted points).
The methods of signal processing include time domain, frequency domain, and complex frequency domain. This technology mainly discusses the modeling of linear timeinvariant continuous system, integral of the system's zerostate response, setting up system function and the continuous time filtering of deterministic signals
Discrete time
Discretetime signal processing is for sampled signals, defined only at discrete points in time, and as such are quantized in time, but not in magnitude.
Analog discretetime signal processing is a technology based on electronic devices such as sample and hold circuits, analog timedivision 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 discretetime signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.
Digital
Digital signal processing is the processing of digitized discretetime sampled signals. Processing is done by generalpurpose computers or by digital circuits such as ASICs, fieldprogrammable gate arrays or specialized digital signal processors (DSP chips). Typical arithmetical operations include fixedpoint and floatingpoint, realvalued and complexvalued, multiplication and addition. Other typical operations supported by the hardware are circular buffers and lookup tables. Examples of algorithms are the fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters.
Nonlinear
Nonlinear signal processing involves the analysis and processing of signals produced from nonlinear systems and can be in the time, frequency, or spatiotemporal domains.^{[6]}^{[7]} Nonlinear systems can produce highly complex behaviors including bifurcations, chaos, harmonics, and subharmonics which cannot be produced or analyzed using linear methods.
Polynomial signal processing is a type of nonlinear signal processing, where polynomial systems may be interpreted as conceptually straight forward extensions of linear systems to the nonlinear case.^{[8]}
Statistical
Statistical signal processing is an approach which treats signals as stochastic processes, utilizing their statistical properties to perform signal processing tasks.^{[9]} Statistical techniques are widely used in signal processing applications. For example, one can model the probability distribution of noise incurred when photographing an image, and construct techniques based on this model to reduce the noise in the resulting image.
Application fields
 Audio signal processing – for electrical signals representing sound, such as speech or music^{[10]}
 Image processing – in digital cameras, computers and various imaging systems
 Video processing – for interpreting moving pictures
 Wireless communication – waveform generations, demodulation, filtering, equalization
 Control systems
 Array processing – for processing signals from arrays of sensors
 Process control – a variety of signals are used, including the industry standard 420 mA current loop
 Seismology
 Financial signal processing – analyzing financial data using signal processing techniques, especially for prediction purposes.
 Feature extraction, such as image understanding and speech recognition.
 Quality improvement, such as noise reduction, image enhancement, and echo cancellation.
 Source coding including audio compression, image compression, and video compression.
 Genomic signal processing^{[11]}
In communication systems, signal processing may occur at:
 OSI layer 1 in the seven layer OSI model, the physical layer (modulation, equalization, multiplexing, etc.);
 OSI layer 2, the data link layer (forward error correction);
 OSI layer 6, the presentation layer (source coding, including analogtodigital conversion and data compression).
Typical devices
 Filters – for example analog (passive or active) or digital (FIR, IIR, frequency domain or stochastic filters, etc.)
 Samplers and analogtodigital converters for signal acquisition and reconstruction, which involves measuring a physical signal, storing or transferring it as digital signal, and possibly later rebuilding the original signal or an approximation thereof.
 Signal compressors
 Digital signal processors (DSPs)
Mathematical methods applied
 Differential equations^{[12]}
 Recurrence relation^{[13]}
 Transform theory
 Timefrequency analysis – for processing nonstationary signals^{[14]}
 Spectral estimation – for determining the spectral content (i.e., the distribution of power over frequency) of a time series^{[15]}
 Statistical signal processing – analyzing and extracting information from signals and noise based on their stochastic properties
 Linear timeinvariant system theory, and transform theory
 Polynomial signal processing – analysis of systems which relate input and output using polynomials
 System identification^{[6]} and classification
 Calculus
 Complex analysis^{[16]}
 Vector spaces and Linear algebra^{[17]}
 Functional analysis^{[18]}
 Probability and stochastic processes^{[9]}
 Detection theory
 Estimation theory
 Optimization^{[19]}
 Numerical methods
 Time series
 Data mining – for statistical analysis of relations between large quantities of variables (in this context representing many physical signals), to extract previously unknown interesting patterns
See also
 Audio filter
 Bounded variation
 Digital image processing
 Dynamic range compression, companding, limiting, and noise gating
 Information theory
 Nonlocal means
 Reverberation
References
 ^ Sengupta, Nandini; Sahidullah, Md; Saha, Goutam (August 2016). "Lung sound classification using cepstralbased statistical features". Computers in Biology and Medicine. 75 (1): 118–129. doi:10.1016/j.compbiomed.2016.05.013. PMID 27286184.
 ^ Alan V. Oppenheim and Ronald W. Schafer (1989). DiscreteTime Signal Processing. Prentice Hall. p. 1. ISBN 0132167719.
 ^ Oppenheim, Alan V.; Schafer, Ronald W. (1975). Digital Signal Processing. Prentice Hall. p. 5. ISBN 0132146355.
 ^ "A Mathematical Theory of Communication – CHM Revolution". Computer History. Retrieved 20190513.
 ^ ^{a} ^{b} Fifty Years of Signal Processing: The IEEE Signal Processing Society and its Technologies, 1948–1998. The IEEE Signal Processing Society. 1998.
 ^ ^{a} ^{b} Billings, S. A. (2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and SpatioTemporal Domains. Wiley. ISBN 9781119943594.
 ^ Slawinska, J., Ourmazd, A., and Giannakis, D. (2018). "A New Approach to Signal Processing of Spatiotemporal Data". 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE Xplore. pp. 338–342. doi:10.1109/SSP.2018.8450704. ISBN 9781538615713. S2CID 52153144.CS1 maint: uses authors parameter (link)
 ^ V. John Mathews; Giovanni L. Sicuranza. Polynomial Signal Processing. Wiley. ISBN 9780471034148.
 ^ ^{a} ^{b} Scharf, Louis L. (1991). Statistical signal processing: detection, estimation, and time series analysis. Boston: Addison–Wesley. ISBN 0201190389. OCLC 61160161.
 ^ Sarangi, Susanta; Sahidullah, Md; Saha, Goutam (September 2020). "Optimization of datadriven filterbank for automatic speaker verification". Digital Signal Processing. 104: 102795. arXiv:2007.10729. doi:10.1016/j.dsp.2020.102795. S2CID 220665533.
 ^ Anastassiou, D. (2001). "Genomic signal processing". IEEE Signal Processing Magazine. IEEE. 18 (4): 8–20. doi:10.1109/79.939833.
 ^ Patrick Gaydecki (2004). Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design. IET. pp. 40–. ISBN 9780852964316.
 ^ Shlomo Engelberg (8 January 2008). Digital Signal Processing: An Experimental Approach. Springer Science & Business Media. ISBN 9781848001190.
 ^ Boashash, Boualem, ed. (2003). Time frequency signal analysis and processing a comprehensive reference (1 ed.). Amsterdam: Elsevier. ISBN 0080443354.
 ^ Stoica, Petre; Moses, Randolph (2005). Spectral Analysis of Signals (PDF). NJ: Prentice Hall.
 ^ Peter J. Schreier; Louis L. Scharf (4 February 2010). Statistical Signal Processing of ComplexValued Data: The Theory of Improper and Noncircular Signals. Cambridge University Press. ISBN 9781139487627.
 ^ Max A. Little (13 August 2019). Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics. OUP Oxford. ISBN 9780191024313.
 ^ Steven B. Damelin; Willard Miller, Jr (2012). The Mathematics of Signal Processing. Cambridge University Press. ISBN 9781107013223.
 ^ Daniel P. Palomar; Yonina C. Eldar (2010). Convex Optimization in Signal Processing and Communications. Cambridge University Press. ISBN 9780521762229.
Further reading
 P Stoica, R Moses (2005). Spectral Analysis of Signals (PDF). NJ: Prentice Hall.
 Kay, Steven M. (1993). Fundamentals of Statistical Signal Processing. Upper Saddle River, New Jersey: Prentice Hall. ISBN 0133457117. OCLC 26504848.
 Papoulis, Athanasios (1991). Probability, Random Variables, and Stochastic Processes (third ed.). McGrawHill. ISBN 0071008705.
 Kainam Thomas Wong [1]: Statistical Signal Processing lecture notes at the University of Waterloo, Canada.
 Ali H. Sayed, Adaptive Filters, Wiley, NJ, 2008, ISBN 9780470253885.
 Thomas Kailath, Ali H. Sayed, and Babak Hassibi, Linear Estimation, PrenticeHall, NJ, 2000, ISBN 9780130224644.
External links
 Signal Processing for Communications – free online textbook by Paolo Prandoni and Martin Vetterli (2008)
 Scientists and Engineers Guide to Digital Signal Processing – free online textbook by Stephen Smith