Class Schedule for Spring 2020
This volume describes the essential tools and techniques of statistical signal processing. At every stage, theoretical ideas are linked to specific applications in communications and signal processing. The book begins with an overview of basic probability, random objects, expectation, and second-order moment theory, followed by a wide variety of examples of the most popular random process. Trophy hunter 2003 download free. full version free. Sl.No Chapter Name MP4 Download; 1: Lec 1: Overview of Statistical Signal Processing: Download: 2: Lec 2: Probability and Random Variables: Download: 3: Lec 3: Linear Algebra of Random Variables.
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Khan Academy This is an incredible resource to preview upcoming material or review previous lessons for CRCT practice. If you wish to join my class, go to 'Coach' and add my coach name: MatthewGraham@gwinnett.k12.ga.us This will allow me to see all of your progress as well as what concepts you struggle with. Khan academy (2011)mr. graham's 8th grade algebra websites. Learn eighth grade math for free—functions, linear equations, geometric transformations, and more. Full curriculum of exercises and videos. If you're seeing this message, it means we're having trouble loading external resources on our website.
Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on 'advanced topics' ignored by other books on the subject. Algorithms for Convolution. The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts.
Signal Processing Pdf
Statistical Signal Processing Stanford
| Week | Topic | HW (Due Thursdays) |
|---|---|---|
| 1 1/13 | Class organization. Probability: random variables and random vectors, expected values, characteristic functions. Random processes: Definitions Tuesday, Thursday | |
| 2 1/20 | Random processes: Second-order description (mean, correlation function, power spectrum). Linear vector spaces: inner products, norms, Hilbert spaces, separability. Tuesday, Thursday | PS I: 2.3, 2.4, 2.8, 2.10, 2.11 |
| 3 1/27 | Vector space for random processes: inner product, Karhunen-Loève expansion. Optimization theory: constrained and unconstrained problems. Estimation theory: notions of error. Tuesday, Thursday | PS II: 2.17, 2.19, 2.35, 2.47 |
| 4 2/3 | Estimation theory: parameter estimation, minimum mean-squared error estimation, MAP estimation, linear estimators and the Orthogonality Principle, maximum likelihood estimation, Cramér-Rao bound. Tuesday, Tuesday audio backup, Thursday | PS III: 2.14, 2.16, 3.1, 4.1 |
| 5 2/10 | Estimation theory: The Cramér-Rao bound. Poisson processes and estimating their characteristics. Tuesday, Thursday | Spring Recess |
| 6 2/17 | Linear and nonlinear waveform parameter estimates. Linear signal estimation: Wiener filters. Tuesday, Thursday | PS IV: 4.2, 4.3, 4.9, 4.11, 4.14 |
| 7 2/24 | Linear signal estimation: Wiener filters, adaptive filters. Tuesday, Thursday | PS V: 4.8, 4.12, 4.16, 4.23 |
| 8 3/2 | Linear signal estimation: Kalman filters. General signal estimation: Bayesian filtering. Tuesday (missing some audio), (backup audio); Thursday | Quiz I Due |
| 9 3/9 | Estimation theory: spectral estimation. Filtering in the context of basis expansions: Denoising, wavelets, compressive sensing. Detection theory: likelihood ratio test. Tuesday, Thursday | |
| 10 3/16 | Spring Break | |
| 11 3/23 | Detection theory: ROC curves, Neymann-Pearson detection, Stein’s lemma. Tuesday, Thursday | PS VI: 4.31, 4.36, 4.38, 4.41 |
| 12 3/30 | Distance measures for densities, M models, null-hypothesis testing. Tuesday | PS VII: 4.44, 4.46, 4.54, 5.1 |
| 13 4/6 | Sequential detection. Uncertainties in models: simultaneous estimation and detection. Tuesday, Thursday | PS VIII: 5.2, 5.4, 5.6, 5.10, 5.13 |
| 14 4/13 | Detection theory: Signals in additive noise. Signal and noise unknowns. Tuesday, Thursday | PS IX: 5.5, 5.23, 5.27, 5.51 |
| 15 4/20 | Non-Gaussian detection theory, type-based detection. Tuesday, Thursday | Quiz II Due |