Lecture notes and supplementary materials for PMBED209, Fall 2017

  1. lecture 1
    9/29/2017 signal and image representation, sampling and quantization, review of LTI system, impulse response, etc.
    Wiki page on companding quantization.
    Book on nonuniform sampling. Available at UCLA library. A quick browsing over TOC would reveal the scope of work and applications.
    Interactive lecture by Wilson J. Rugh on LTI systems, JHU
  2. lecture 2
    10/4/2017 Review of basic linear algebra, spans and basis, orthogonal projections, Fourier as a special basis.
    Reference book: Convex optimization by Boyd and Vandenberghe. Also open courseware and online video by Boyd.
    Local resource: Pro. L. Vandenberghe UCLA EE236 series.
    review of linear algebra: Khan Academy.
  3. lecture 3
    10/6/2017 Continue review of basic linear algebra, spans and basis, orthogonal projections.
  4. lecture 4
    10/11/2017 Gram Schmit procedure. Fourier as a special basis. Periodogram from nonuniform samples.
    Review of basic DFT etc. Foundations of signal processing.
  5. lecture 5
    10/13/2017 Sampling theorems (Nyquist-Shannon, Petersen-Middleton), lattice and its reciprocal, ideal vs practical ADC and DAC, introduction to compressed sensing.
  6. lecture 6
    10/18/2017 Least squares principle. Projections. Overdetermined system and normal equation. Pseudo inverse + QR.
  7. lecture 7
    10/20/2017 Least squares for underdetermined system. Minimum norm solution.
    Resource on introduction to Lagrangian multiplier: Khan Academy
  8. lecture 8
    10/25/2017 frequentist vs bayesian, Gauss-Markov model, minimum variance model, CRLB
  9. lecture 9
    10/27/2017 eigen-systems, dynamic view, steady state, special matrices
  10. 11/01/2017 class cancelled due to conflict schedule
  11. 11/02/2017 10-day window for 1 hour take-home midterm
  12. lecture 10
    11/03/2017 Optimization interpretation of PCA, SVD, FA. Local PCA and Kernel method.
  13. lecture 11
    11/08 Robust and probabilistic PCA, Classic ICA model
  14. 11/10/2017 Veterans day
  15. lecture 12
    Classic and nonlinear ICA, stochastic gradient optimization, nonlinear extensions. Midterm QAs. 11/15/2017
  16. lecture 13
    11/17/2017 image restoration, deblurring, and denoising, from estimation perspective
  17. lecture 14
    11/22/2017 complete discussion of image deblurring and denoising
  18. 11/24/2017 Thanksgiving holiday
  19. lecture 15
    11/29/2017 computer vision tasks: segmentation and object identification
  20. lecture 16
    12/01/2017 complete discussion on image registration; a crash introduction to neural networks and deep learning
  21. 12/06/2017 project presentation session 1
    CORK, TYLER EDWARD
    HAN, PEI
    HU, ZHEHAO
    KIM, YEUN
    LEE, DAVID SOOBIN
    PARK, JOSEPH
  22. 12/08/2017 project presentation session 2
    PEARIGEN, AIDAN RHODES
    SANG, YUDI
    WICKSTROM, ALEXANDER T
    YAO, JINGWEN
    ZHANG, XINHENG
    ZHOU, HANYUE
  23. 12/13/2017 9-12am Additional project presentation time.
    Back to Dan Ruan's teaching
    Back to Dan Ruan's home page