Signal Representations

Related lectures (32)

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Explores eigenfaces, DFT, DCT, and wavelet transforms in image processing.

Covers the development of Jupyter Notebooks for image processing labs with automated suggestions for students.

Covers spectral analysis, stationarity tests, challenges of nonstationary time series, and tools for analysis with missing data.

Introduces the basics of image processing, covering point and spatial operators, histogram processing, and transform-domain operations.

Explores data compression through signal sparsity, questioning the need for recording vast amounts of data.

Explores image interpolation, splines, wavelets, and geometric transformations in image processing.

Covers advanced topics in watermarking, including resisting scaling and rotations, self-referenced watermarking, and types of attacks.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Introduces system properties, Laplace Transform, and analog filters for signal analysis.