Signal processing is an area of systems engineering, electrical engineering and applied mathematics
that deals with operations on or analysis of analog as well as digitized signals, representing
time-varying or spatially varying physical quantities.
Signals of interest can include sound, electromagnetic radiation, images, and sensor readings, for
example biological measurements such as electrocardiograms, control system signals, telecommunication
transmission signals, and many others.
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.
Oppenheim and Schafer further state that the "digitalization" or digital refinement of
these techniques can be found in the digital control systems of the 1940s and 1950s.
Application fields of signal processing Audio signal processing – for electrical
signals representing sound, such as speech or music
Speech signal processing – for processing and interpreting spoken words
Image processing – in digital cameras, computers and various imaging systems
Video processing – for interpreting moving pictures
Wireless communication - waveform generation, demodulation, filtering, equalization
Control systems Array processing – for processing signals
from arrays of sensors 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.
In communication systems, signal processing may occur at:
OSI layer 1 in the seven layer OSI model, the Physical Layer;
OSI layer 6, the Presentation Layer.
Typical devices Filters – - for example analog or digital
Samplers and Analog-to-digital 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
Mathematical methods applied in signal processing Differential equations
Difference equations Transform theory
Time-frequency analysis – for processing non-stationary signals
Spectral estimation – for determining the spectral content of a time series
Statistical signal processing – analyzing and extracting information from signals and
noise based on their stochastic properties Linear time-invariant system theory, and transform
theory System identification and classification
Calculus Vector spaces and Linear algebra
Functional analysis Probability and stochastic processes
Detection theory Estimation theory
Optimization Numerical methods
Time series Data mining – for statistical analysis
of relations between large quantities of variables, to extract previously unknown interesting
patterns Categories of signal processing
Analog signal processing
Analog signal processing is for signals that have not been digitized, as in legacy radio,
telephone, radar, and television systems.
This involves linear electronic circuits as well as non-linear ones.
The former are, for instance, passive filters, active filters, additive mixers, integrators
and delay lines.
Non-linear circuits include compandors, multiplicators, voltage-controlled filters, voltage-controlled
oscillators and phase-locked loops.
Discrete-time signal processing 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
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, 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 the processing of digitized discrete-time sampled signals.
Processing is done by general-purpose computers or by digital circuits such as ASICs, field-programmable
gate arrays or specialized digital signal processors.
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, finite impulse response filter,
Infinite impulse response filter, and adaptive filters such as the Wiener and Kalman filters.
Nonlinear signal processing Nonlinear signal processing involves the analysis
and processing of signals produced from nonlinear systems and can be in the time, frequency,
or spatio-temporal domains.
Nonlinear systems can produce highly complex behaviors including bifurcations, chaos, harmonics,
and subharmonics which cannot be produced or analyzed using linear methods.
See also Audio filter
Delay Dynamic range compression, companding, limiting,
and noise gating Reverberation
Notes and references
External links Signal Processing for Communications – free
online textbook by Paolo Prandoni and Martin Vetterli
Scientists and Engineers Guide to Digital Signal Processing – free online textbook
by Stephen Smith Signal Processing Techniques for Determining
Powerplant Characteristics The IEEE Signal Processing Society