Signal Representation: Fourier Transforms

Related lectures (30)

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Covers the concepts of analog, discrete, and digital signals, sampling times, frequencies, and pulses.

Explores scaling and renormalization in statistical mechanics, emphasizing critical points and invariant properties.

Explores spectrogram analysis, including DFT, STFT, and CQT, for tasks like music transcription and onset detection.

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

Introduces the discrete-time Fourier transform and its application to stable LTI systems, covering properties and frequency response.

Explores the properties of Fourier and inverse Fourier transforms, analyzing finite wave trains, Gaussian functions, and 3D space transformations.

Explores the Fourier transform, Schwartz space, and Riemann-Lebesgue lemma in quantum physics.

Explains Ewald summation, self-interaction correction, and short-range interaction calculations.

Covers matrix operations, Fourier transformations, Gaussian models, and signal representations using algebraic methods.

Explores frequency periodicity in the discrete Fourier transform for signal reconstruction.