Oversampling

n. (Computers) correction of jagged appearances in graphic objects by filling in the jagged areas with intermediate colors (like antialiasing)

In signal processing, oversampling is the process of sampling a signal with a sampling frequency significantly higher than twice the bandwidth or highest frequency of the signal being sampled. An oversampled signal is said to be oversampled by a factor of β, defined as or. where is the sampling frequency is the bandwidth or highest frequency of the signal; the Nyquist frequency is .There are three main reasons for performing oversampling:It aids in anti-aliasing because realizable analog anti-aliasing filters are very difficult to implement with the sharp cutoff necessary to maximize use of the available bandwidth without exceeding the Nyquist limit. By increasing the bandwidth of the sampled signal, the anti-aliasing filter has less complexity and can be made less expensively by relaxing the requirements of the filter at the cost of a faster sampler. Once sampled, the signal can be digitally filtered and downsampled to the desired sampling frequency. In modern integrated circuit technology, digital filters are much easier to implement than comparable analog filters of high order.In practice, oversampling is implemented in order to achieve cheaper higher-resolution A/D and D/A conversion. For instance, to implement a 24-bit converter, it is sufficient to use a 20-bit converter that can run at 256 times the target sampling rate. Averaging a group of 256 consecutive 20-bit samples adds 4 bits to the resolution of the average, producing a single sample with 24-bit resolution. Note that this averaging is possible only if the signal contains perfect equally distributed noise (i.e. if the A/D is perfect and the signal's deviation from an A/D result step lies below the threshold, the conversion result will be as inaccurate as if it had been measured by the low-resolution core A/D and the oversampling benefits will not take effect).Noise reduction/cancellation. If multiple samples are taken of the same quantity with a random noise signal, then averaging several samples reduces the noise by a factor of . See standard error (statistics). This means that the signal-to-noise-ratio improves by a factor of 2 (3dB) if we oversample by a factor of 4 relative to the Nyquist rate (ie a of 4).
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