Lecture

Sampling Signals: Stroboscopic Effect (Spectrum Folding)

Related lectures (30)

Explores filtering signals with a moving average filter and the process of sampling, emphasizing the importance of signal reconstruction from samples.

Signals, Instruments, and Systems

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Sampling: Signal Reconstruction and Aliasing

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

Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Sampling of Signals 6: Sampling a Pure Sinusoid

Explores the sampling of pure sinusoids, emphasizing the Nyquist theorem and its practical implications.

Sampling: DT-time processing of CT signals

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Signal Sampling

Explores representing analog signals digitally through sampling and quantization, discussing sampling frequency, undersampling consequences, and the stroboscopic effect.

Sampling Complex Exponentials

Covers the sampling of complex exponentials and the challenges of reconstruction in different scenarios.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

The Sampling Theorem

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

Wireless Receivers: Time and Phase Offset

Covers the impact and compensation of time and phase offset in wireless receivers.

Signal Modulation and Sampling

Covers signal modulation, sampling, and their applications in communication systems.

Digital Signal Processing: Theory

Covers the theory of digital signal processing, including sampling, transformation methods, digitization, and PID controllers.

Filtering before Sampling

Emphasizes the necessity of filtering signals before sampling to prevent undersampling effects.

Introduction to Sampling

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

Sampling and Reconstruction

Covers sampling, Fourier Transform, and reconstruction using low-pass filters in signal processing.

Data Representations and Processing in Machine Learning

Covers data representations and processing techniques essential for effective machine learning algorithms.

Signal Sampling: Bandwidth and Spectrum

Introduces signals, frequencies, bandwidth, filtering, and sampling in signal processing.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.