15-09-2017, 01:36 PM
Digital signal processing (DSP) is the use of digital processing such as computers. To perform a wide variety of signal processing operations. The signals processed in this manner are a sequence of numbers representing samples of a continuous variable in a domain such as time, space, or frequency.
Digital signal processing and analog signal processing are signal processing subfields. DSP applications include audio and voice signal processing, sonar, radar and other sensor processing, spectral estimation, statistical signal processing, digital image processing, signal processing for telecommunications, systems control, biomedical engineering, data processing seismic, among others.
DSP may involve linear or non-linear operations. The processing of non-linear signals is closely related to the identification of non-linear systems and can be implemented in time, frequency and spatio-temporal domains. The application of digital computing to signal processing allows many advantages over analog processing in many applications, such as detection and correction of transmission errors as well as data compression. DSP is applicable to both transmission data and static (stored) data.
The increased use of computers has resulted in the increased use, and the need, of digital signal processing. To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter. Sampling is generally carried out in two stages, discretization and quantification. Discretization means that the signal is divided into equal time intervals, and each interval is represented by a single amplitude measurement. Quantization means that each measure of amplitude is approximated by a value of a finite set. Rounding real numbers to whole numbers is an example.
The Nyquist-Shannon sampling theorem states that a signal can be reconstructed exactly from its samples if the sampling frequency is greater than twice the highest signal frequency. In practice, the sampling frequency is often significantly greater than twice that required by the limited bandwidth of the signal.
The theoretical DSP analyzes and derivations are typically performed in discrete-time signal models with no amplitude inaccuracies (quantification error), "created" by the abstract sampling process. Numerical methods require a quantized signal, such as those produced by an analog-to-digital converter (ADC). The processed result can be a frequency spectrum or a set of statistics. But it is often another quantized signal that is converted back to analog form by a digital-to-analog converter (DAC).
Digital signal processing and analog signal processing are signal processing subfields. DSP applications include audio and voice signal processing, sonar, radar and other sensor processing, spectral estimation, statistical signal processing, digital image processing, signal processing for telecommunications, systems control, biomedical engineering, data processing seismic, among others.
DSP may involve linear or non-linear operations. The processing of non-linear signals is closely related to the identification of non-linear systems and can be implemented in time, frequency and spatio-temporal domains. The application of digital computing to signal processing allows many advantages over analog processing in many applications, such as detection and correction of transmission errors as well as data compression. DSP is applicable to both transmission data and static (stored) data.
The increased use of computers has resulted in the increased use, and the need, of digital signal processing. To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter. Sampling is generally carried out in two stages, discretization and quantification. Discretization means that the signal is divided into equal time intervals, and each interval is represented by a single amplitude measurement. Quantization means that each measure of amplitude is approximated by a value of a finite set. Rounding real numbers to whole numbers is an example.
The Nyquist-Shannon sampling theorem states that a signal can be reconstructed exactly from its samples if the sampling frequency is greater than twice the highest signal frequency. In practice, the sampling frequency is often significantly greater than twice that required by the limited bandwidth of the signal.
The theoretical DSP analyzes and derivations are typically performed in discrete-time signal models with no amplitude inaccuracies (quantification error), "created" by the abstract sampling process. Numerical methods require a quantized signal, such as those produced by an analog-to-digital converter (ADC). The processed result can be a frequency spectrum or a set of statistics. But it is often another quantized signal that is converted back to analog form by a digital-to-analog converter (DAC).