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Digital signal processing ('''DSP''') is the study of and Speech Signal Processing , sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, Image Processing , signal processing for communications, biomedical signal processing, etc. Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an Analog To Digital Converter . Often, the required output signal is another analog output signal, which requires a Digital To Analog Converter . The Algorithm s required for DSP are sometimes performed using specialized Computers , which make use of specialized microprocessors called Digital Signal Processor s (also abbreviated ''DSP''). These process signals in Real Time and are generally purpose-designed Application-specific Integrated Circuit s (ASICs). When flexibility and rapid development are more important than unit costs at high volume, DSP algorithms may also be implemented using Field-programmable Gate Array s (FPGAs). DSP DOMAINS In DSP, engineers usually study digital signals in one of the following domains: Time Domain (one-dimensional signals), spatial domain (multidimensional signals), Frequency Domain , Autocorrelation domain, and Wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a Discrete Fourier Transform produces the frequency domain information, that is the Frequency Spectrum . Autocorrelation is defined as the Cross-correlation of the signal with itself over varying intervals of time or space. SIGNAL SAMPLING See Also: Sampling (signal processing) With the increasing use of Computer s the usage and need of digital signal processing has increased. In order to use an analog signal on a computer it must be digitized with an Analog To Digital Converter (ADC). Sampling is usually carried out in two stages, Discretization and Quantization . In the discretization stage, the space of signals is partitioned into Equivalence Class es and discretization is carried out by replacing the signal with representative signal of the corresponding equivalence class. In the quantization stage the representative signal values are approximated by values from a finite set. In order for a sampled analog signal to be exactly reconstructed, the ); and Fc +/-BWx, a frequency band centered on a carrier frequency ("direct demodulation"). A Digital To Analog Converter (DAC) is used to convert the digital signal back to analog. The use of a digital computer is a key ingredient into Digital Control Systems . TIME AND SPACE DOMAINS The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Filtering generally consists of some transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:
Most filters can be described in Z-domain (a superset of the frequency domain) by their Transfer Function s. A filter may also be described as a Difference Equation , a collection of Zeroes and Pole s or, if it is an FIR filter, an Impulse Response or Step Response . The output of an FIR filter to any given input may be calculated by Convolving the input signal with the Impulse Response . Filters can also be represented by block diagrams which can then be used to derive a sample processing Algorithm to implement the filter using hardware instructions. FREQUENCY DOMAIN Signals are converted from time or space domain to the frequency domain usually through the Fourier Transform . The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared. The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to get information of which frequencies are present in the input signal and which are missing. There are some commonly used frequency domain transformations. For example, the Cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components. APPLICATIONS The main applications of DSP are Audio Signal Processing , Audio Compression , Digital Image Processing , Video Compression , Speech Processing , Speech Recognition , Digital Communication s, RADAR , SONAR , seismology, and biomedicine. Specific examples are Speech Compression and transmission in digital Mobile Phone s, room matching equalization of sound in Hifi and Sound Reinforcement applications, Weather Forecasting , Economic Forecasting , Seismic data processing, analysis and control of Industrial Process es, computer-generated Animation s in Movie s, Medical Imaging such as CAT scans and MRI , Image Manipulation , high fidelity loudspeaker crossovers and equalization, and Audio Effects for use with Electric Guitar Amplifiers . IMPLEMENTATION Digital signal processing is often implemented using s might be designed specifically. For slow applications such as flame scanning, a traditional slower processor such as a microcontroller can cope. TECHNIQUES RELATED FIELDS
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