Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Aliasing

Related lectures (29)

Explores the Sampling Theorem, digital control, signal reconstruction, and anti-aliasing filters.

Explores spectral analysis, aliasing, and linear filtering in time series data.

Explores sinusoidal aliasing, from periodic complex exponentials to aliasing of sinusoids.

Explores Fourier analysis, sampling, and signal reconstruction quality assessment through practical experiments.

Explores aliasing in signal processing, emphasizing raw-sampling and spectrum manipulation to prevent distortion.

Explores synchrotrons, x-ray lasers, lensless imaging, Nyquist frequency, aliasing, oversampling, and constraints in scattering data analysis.

Explores sinc interpolation and the spectral representation of interpolated signals based on the chosen interpolation period.

Covers syntax and typing rules in programming languages, discussing aliasing, mutability, and store locations.

Covers bandpass sampling for signals with limited frequency range, ensuring information preservation through proper sampling techniques.

Covers the spectral representation of downsampling and rational sampling rate change.

Explores the sampling theorem through sinusoid reconstruction, under-sampling effects, and signal filtering importance.

Explores sampling strategies for signals, emphasizing the importance of controlling errors in the sampling process.

Introduces records, variants, evaluation rules, typing rules, aliasing challenges, and benefits in programming languages.

Explores the sampling theorem, basis expansion, Nyquist frequency, and signal representation.

Covers the Fourier series representation of periodic signals and harmonic analysis.

Introduces signals, frequencies, and bandwidth, including pure sinusoids, Fourier theory, and spectral representation.

Explores estimation, ergodicity, sampling, and linear time invariant filters in time series analysis.

Covers sampling, transformations, acquisition models, aliasing problems, and examples in image processing.

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.