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  2. Signal reconstruction - Wikipedia

    en.wikipedia.org/wiki/Signal_reconstruction

    In our example, the vector space of sampled signals is n-dimensional complex space. Any proposed inverse R of F ( reconstruction formula , in the lingo) would have to map C n {\displaystyle \mathbb {C} ^{n}} to some subset of L 2 {\displaystyle L^{2}} .

  3. Oversampling - Wikipedia

    en.wikipedia.org/wiki/Oversampling

    The sampling theorem states that sampling frequency would have to be greater than 200 Hz. Sampling at four times that rate requires a sampling frequency of 800 Hz. This gives the anti-aliasing filter a transition band of 300 Hz ((f s /2) − B = (800 Hz/2) − 100 Hz = 300 Hz) instead of 0 Hz if the sampling frequency was 200 Hz. Achieving an ...

  4. Pulse-code modulation - Wikipedia

    en.wikipedia.org/wiki/Pulse-code_modulation

    Between samples no measurement of the signal is made; the sampling theorem guarantees non-ambiguous representation and recovery of the signal only if it has no energy at frequency f s /2 or higher (one half the sampling frequency, known as the Nyquist frequency); higher frequencies will not be correctly represented or recovered and add aliasing ...

  5. Multidimensional sampling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_sampling

    A simple illustration of aliasing can be obtained by studying low-resolution images. A gray-scale image can be interpreted as a function in two-dimensional space. An example of aliasing is shown in the images of brick patterns in Figure 5. The image shows the effects of aliasing when the sampling theorem's condition is not satisfied.

  6. Nyquist–Shannon sampling theorem - Wikipedia

    en.wikipedia.org/wiki/Nyquist–Shannon_sampling...

    The sampling theorem introduces the concept of a sample rate that is sufficient for perfect fidelity for the class of functions that are band-limited to a given bandwidth, such that no actual information is lost in the sampling process. It expresses the sufficient sample rate in terms of the bandwidth for the class of functions.

  7. Papoulis-Marks-Cheung Approach - Wikipedia

    en.wikipedia.org/wiki/Papoulis-Marks-Cheung_Approach

    The Papoulis-Marks-Cheung approach [1] is a theorem in multidimensional Shannon sampling theory that shows that the sampling density of a two-dimensional bandlimited function can be reduced to the support of the Fourier transform of the function.

  8. Quantization (signal processing) - Wikipedia

    en.wikipedia.org/wiki/Quantization_(signal...

    An analog-to-digital converter (ADC) can be modeled as two processes: sampling and quantization. Sampling converts a time-varying voltage signal into a discrete-time signal, a sequence of real numbers. Quantization replaces each real number with an approximation from a finite set of discrete values.

  9. Reconstruction filter - Wikipedia

    en.wikipedia.org/wiki/Reconstruction_filter

    The sampling theorem describes why the input of an ADC requires a low-pass analog electronic filter, called the anti-aliasing filter: the sampled input signal must be bandlimited to prevent aliasing (here meaning waves of higher frequency being recorded as a lower frequency).