Signal transmission using electronic signal processing.Transducers convert signals from other physicalwaveforms to electriccurrent orvoltage waveforms, which then are processed, transmitted aselectromagnetic waves, received and converted by another transducer to final form.
The signal on the left looks like noise, but the signal processing technique known asspectral density estimation (right) shows that it contains five well-defined frequency components.
According toAlan V. Oppenheim andRonald W. Schafer, the principles of signal processing can be found in the classicalnumerical analysis techniques of the 17th century. They further state that the digital refinement of these techniques can be found in the digitalcontrol systems of the 1940s and 1950s.[3]
Continuous-time signal processing is for signals that vary with the change of continuous domain (without considering some individual interrupted points).
The methods of signal processing includetime domain,frequency domain, andcomplex frequency domain. This technology mainly discusses the modeling of alinear time-invariant continuous system, integral of the system's zero-state response, setting up system function and the continuous time filtering of deterministic signals. For example, in time domain, a continuous-time signal passing through alinear time-invariant filter/system denoted as, can be expressed at the output as
In some contexts, is referred to as the impulse response of the system. The aboveconvolution operation is conducted between the input and the system.
Discrete-time 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 discrete-time signal processing is a technology based on electronic devices such assample and hold circuits, analog time-divisionmultiplexers,analog delay lines andanalog feedback shift registers. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals.[7]
The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without takingquantization error into consideration.
Nonlinear signal processing involves the analysis and processing of signals produced fromnonlinear systems and can be in the time,frequency, or spatiotemporal domains.[8][9] Nonlinear systems can produce highly complex behaviors includingbifurcations,chaos,harmonics, andsubharmonics which cannot be produced or analyzed using linear methods.
Polynomial signal processing is a type of non-linear signal processing, wherepolynomial systems may be interpreted as conceptually straightforward extensions of linear systems to the nonlinear case.[10]
Statistical signal processing is an approach which treats signals asstochastic processes, utilizing theirstatistical properties to perform signal processing tasks.[11] Statistical techniques are widely used in signal processing applications. For example, one can model theprobability distribution of noise incurred when photographing an image, and construct techniques based on this model toreduce the noise in the resulting image.
Graph signal processing generalizes signal processing tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.[12] Graph signal processing presents several key points such as sampling signal techniques,[13] recovery techniques[14] and time-varying techiques.[15] Graph signal processing has been applied with success in the field of image processing, computer vision[16][17][18] and sound anomaly detection.[19]
Samplers andanalog-to-digital converters forsignal 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.
Differential equations[24] – for modeling system behavior, connecting input and output relations in linear time-invariant systems. For instance, a low-pass filter such as anRC circuit can be modeled as a differential equation in signal processing, which allows one to compute the continuous output signal as a function of the input or initial conditions.
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
^abBillings, S. A. (2013).Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. Wiley.ISBN978-1-119-94359-4.
^Slawinska, J.; Ourmazd, A.; 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.ISBN978-1-5386-1571-3.S2CID52153144.
^V. John Mathews; Giovanni L. Sicuranza (May 2000).Polynomial Signal Processing. Wiley.ISBN978-0-471-03414-8.
