Oversampling

Applied sciences
Information engineering
Signal processing
Sampling theory

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Related lectures (24)

Page 2 of 3

Nyquist Rate and Sampling Theorem

Explains the Nyquist rate and Sampling Theorem for reconstructing band-limited signals through examples and sampling techniques.

Data Preprocessing: Handling Challenges

Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.

Image Processing I: Ordered Dithering and Fourier Transform

Explores ordered dithering and Fourier transform in image processing.

Analog to Digital Conversion

Explores analog to digital conversion principles, CMOS technology, precision limitations, and integration of converters.

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Wireless Receivers: Time and Phase Offset Compensation

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

Model Assessment: Metrics and Selection

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Sampling strategies

Explores research process, variable types, causality vs correlation, and sampling strategies.

The Sampling Theorem

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

Signal Sampling and Reconstruction

Covers signal sampling, reconstruction, aliasing, and examples of signal reconstruction using low-pass filters.