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The concept and application of digital signal processing
Digital signal processing modifies and represents the discrete time signals by processing and sequencing them as number and/or symbols. Signal processing is the branch from which analog signal processing and digital signal processing has raised. Digital signal processing is further classified into sonar and radar signal processing,, audio and speech signal processing, sense array processing, statistical signal processing, spectral estimation, control of systems, seismic data processing, biomedical signal processing and other allied fields. The digital signal processing works in a series of steps to accomplish its ultimate goal of filtering, compression and measurement of real world analog signals. Signal sampling is done by the analog to digital convertor which converts the signals from analog to digital form. This is done by changing the analog signals to a series of numbers. Sometimes the digital to analog converter is also required for getting the output in analog form. The analog conversion has a discrete value range and is often the more complex process. But the digital processing is more advantageous and analog to digital conversions find their application in transmission, error detection and correction and data compression. The digital signal processing has evolved technogically since its birth and there are much more additional technologies present today like Microprocessors, stream processors, field programmable gate arrays etc. The digital signal processing usage has increased along with the increase in computer usage. For using an analog signal on computer, it must be converted to its digital form. For that, analog to digital converter is a must. The sampling is done in two distinct phases. The two stages/phages are quantization and decretization. Space between signals is portioned and categorized into equivalence classes. Then the representative signal with corresponding equivalence class is replaced in place of that signal. The values are then approximated using finite set values.
The domains of digital signal processing
There are various domains used for digital signal processing by the engineers. The domain can be frequency domain, time domain, and wavelet or autocorrelation domain. The characteristics of a domain are studied and then an ideally suited domain is selected for processing the signal. This is done by making and informed guess. The frequency spectrum or the frequency domain information is produced by a discrete Fourier transform. Cross correlation of the signal occur between them. The intervals between time and space vary. This is termed as autocorrelation.
The time and space domains
Filtering is the method used for the enhancement of input signal. This is the most common method for processing in the time and space domain. Digital filtering is also done. Numbers of surrounding samples are linearly transformed around the sample of output or input signal. Filters can be various types. Linear transformation of input samples is termed as linear filter. The filter using previous input or output signals is termed as casual filters. The filter using future input signals is termed as non casual filter. The filters which change in time are termed as adaptive filters. The output of a stable filter is bounded in a finite interval. Constant value with time is attained for the output also.
Application of digital signal processing
There are wide applications of dsp. These include audio compression, audio signal processing, video compression, speech recognition and processing, digital image processing, sonar and radar seismology and biomedicine. Other related application include weather and economic forecasting, control and analysis of industrial processes, image manipulation and computer graphics, transmission in digital mobile phones and speech compression, the equalization of hi-fi loudspeaker crossovers, medical imaging for cat scans etc.
Implementation aspects of digital signal processing
Specialized microprocessors are used for digital signal processing such as dsp56000, SHARC and tms320. The processing of data in these machines is done using fixed point arithmetic. There are versions which use the more powerful floating point arthemetic. Some applications require fast processing. For that FGPA is used. Companies like stream processors and free scale now produce mediocre implementation of digital signal processing.
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