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Lecture
Signals & Systems II: Sampling and Signal Representation
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Related lectures (31)
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Fourier Transform: Basics and Applications
Covers the basics of the Fourier transform and its applications in signal processing.
Introduction to Sampling
Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.
Discrete Fourier Transform: Sampling and Interpretation
Explores discrete Fourier transform, signal reconstruction, sampling interpretation, and periodic signal repetition.
Signals & Systems I: Micro-Systems and Communication Systems
Introduces the fundamentals of signals and systems, communication systems, and signal processing.
Frequency Estimation (Theory)
Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.
Signal Processing: Basics and Applications
Covers the basics of signal processing, including Fourier transform, linear systems, and signal manipulation.
The Sampling Theorem
Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.
Sampling and Reconstruction
Covers the concepts of sampling and reconstruction in signal processing, explaining the conditions for accurate reconstruction.
Signals & Systems I: Sampling and Reconstruction
Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.
Principles of Digital Communication
Covers the principles of digital communication, focusing on the Nyquist Sampling Theorem and signal space dimension.
