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ece 792 041 statistical methods for signal analytics instructor chau wai wong chauwai wong ncsu edu objective 1 to introduce various statistical tools and prepare students with solid background in ...

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                      ECE 792-041 Statistical Methods for Signal Analytics 
               Instructor: Chau-Wai Wong, chauwai.wong@ncsu.edu 
               Objective:  (1)  To  introduce  various  statistical  tools  and  prepare  students  with  solid  background  in  signal 
               processing related research, and (2) to engage students in real-world signal analytics tasks. 
               Prerequisites: Any signal processing course. Basic programming skills.  
               Corequisites:  Random Processes.                                 Time & Location: 2 meetings per week @ EB2 
               Textbooks:   
               S. Haykin, Adaptive Filter Theory, Eds. 3, 4, or 5, Pearson. 
               M. Hayes, Statistical Digital Signal Processing and Modeling, Wiley, 1996. 
               P. Stoica, R. L. Moses, Spectral Analysis of Signals, Prentice Hall, 2005. [Online] 
               T. Hastie et al., The Elements of Statistical Learning, Ed. 2, Springer, 2009. [Online] 
               H. Scheffe, The Analysis of Variance, Wiley, 1959. 
               J. J. Faraway, Linear Models with R, Taylor & Francis, 2005. [Online] 
               Topics: 
               I. Fundamentals 
               •    Statistics & Random processes: method of moments, maximum likelihood (MLE), least-squares (LS), 
                    orthogonality principle, normal equations; stationarity, ergodicity, power spectral density (PSD), 
                    autocorrelation function (ACF), partial autocorrelation function (PACF). 
               •    Numerical: condition number, eigendecomposition, singular-value decomposition (SVD), Levinson-Durbin 
                    algorithm, gradient descent, Newton's method, Quasi-Newton methods. 
               •    Model selection: cross-validation (CV), analytical methods (AIC, BIC, MDL, etc.) 
               II. Signal Modeling and Optimum Filtering 
               •    Time series models: autoregressive (AR), moving average (MA), 
                    ARMA. Yule-Walker equations, Wold decomposition. 
               •    Discrete Wiener filtering: forward and backward linear predictions. 
               •    Lattice prediction filter, joint-process estimation. 
               III. Adaptive Filtering 
               •    Least-mean-squares (LMS) algorithm. 
               •    Recursive least-squares (RLS) algorithm. 
               IV. Spectral and Frequency Estimation                                                       Spectrogram of a multi-trace signal 
               •    Nonparametric methods: periodograms and windowing methods, 
                    minimum-variance spectral estimation (Capon), amplitude and 
                    phase estimator (APES), iterative adaptive approach (IAA). 
               •    Parametric methods: AR, MA, and ARMA spectral estimation; maximum entropy method. 
               •    High-resolution subspace approaches: Pisarenko, MUSIC, ESPRIT. 
               V. Analysis of Variance (ANOVA) 
               •    Estimable functions, Gauss-Markoff theorem. 
               •    Confidence ellipsoids/intervals, t-test, F-test. 
               •    ANOVA. 
               Workload & Grading: There will be 2–3 projects and 6 homework assignments (60%), one midterm exam (20%), 
               and one final exam (20%). Projects are recommended to be done in Matlab, alternatively in R, Python, or C++. 
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...Ece statistical methods for signal analytics instructor chau wai wong chauwai ncsu edu objective to introduce various tools and prepare students with solid background in processing related research engage real world tasks prerequisites any course basic programming skills corequisites random processes time location meetings per week eb textbooks s haykin adaptive filter theory eds or pearson m hayes digital modeling wiley p stoica r l moses spectral analysis of signals prentice hall t hastie et al the elements learning ed springer h scheffe variance j faraway linear models taylor francis topics i fundamentals statistics method moments maximum likelihood mle least squares ls orthogonality principle normal equations stationarity ergodicity power density psd autocorrelation function acf partial pacf numerical condition number eigendecomposition singular value decomposition svd levinson durbin algorithm gradient descent newton quasi model selection cross validation cv analytical aic bic mdl...

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