Image Processing II: Haar Filterbank and Wavelet Transforms

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

Explores optimal transforms, image applications, linear algebra, and 2nd-order statistics in image processing.

Covers wavelet analysis and signal representations using orthonormal bases.

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

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

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

Explores the challenges of analyzing speech signals and the concept of time-frequency tiling.

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

Explores neural signals, EMG control, muscle synergies, and HD-EMG for prosthetic devices.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.