
Signal processing : 
DEFINITION 
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Signal processing is an area of systems engineering, electrical engineering and applied mathematics that deals with operations on or analysis of signals, or measurements of timevarying or spatially varying physical quantities.



Introduction: 


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Signals of interest can include sound, electromagnetic radiation, images, and sensor data 


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Some examples are biological data such as electrocardiograms, control system signals, telecommunication transmission signals, and many others. 


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The goals of signal processing can roughly be divided into the following categories.
 Signal acquisition and reconstruction, which involves measuring a physical signal, storing it, and possibly later rebuilding the original signal or an approximation thereof.
 Quality improvement, such as noise reduction, image enhancement, and echo cancellation.
 Signal compression, including audio compression, image compression, and video compression.
 Feature extraction, such as image understanding and speech recognition.



Block Diagram 


Categories: 
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There are four categories that fall under the roof of signal processing
 Analog signal processing
 Discrete signal processing
 Digital signal Processing
 Non Linear signal processing


Analog Signal Processing: 
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 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 nonlinear ones. The linear ones are, for instance, passive filters, active filters, additive mixers, integrators and delay lines.
 Nonlinear circuits include compandors, multiplicators (frequency mixers and voltagecontrolled amplifiers), voltagecontrolled filters, voltagecontrolled oscillators and phaselocked loops.


DiscreteTime Signal Processing: 


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 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 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 Signal Processing: 
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 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 transforms (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters.



Analog Signal Processing: 
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 Nonlinear signal processing involves the analysis and processing of signals produced from nonlinear systems and can be in the time, frequency, or Spatiotemporal domains.
 Nonlinear systems can produce highly complex behaviours including bifurcations, chaos, harmonics, and sub harmonics which cannot be produced or analyzed using linear methods.



Application 
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 Signal compression, including image compression, and video compression.
 Feature extraction, such as image understanding and speech recognition.



Advantages 
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 Data collected at some location.
 Once all of data is collected, it then has to be processed in order to have usable information.
 Quite frequently, data is collected and processed in two separate locations.















