![]() The University places a high priority on approaches to learning and teaching that enhance the student experience. Bellanger Adaptive Digital Filters and Signal Analysis Marcel Dekker 1987 Online Learning P A Regalia Adaptive IIR Filtering in Signal Processing and Control Messerschmidtt Advanced Signal Processingī.D.O. Sim Adaptive Filtering, Prediction and Control Miller Introduction to Adaptive Arraysį Hsu and A.A. V Solo and X Kong Adaptive Signal Processing Algorithms Alexander Adaptive Signal Processing - Theory and Applications Root An Introduction to the Theory of Random Signals and Noiseī Widrow and S.D. Therrien Discrete Random Signals and Statistical Signal Processing Ramadan, Adaptive Filtering Primer with MATLAB®Ĭ.W. able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debateĪ.open to objective and constructive feedback from supervisors and peers.a capacity for self-reflection and a willingness to engage in self-appraisal.Self-awareness and emotional intelligence encouraged and valued in all aspects of learning.honed through assessment and practice throughout the program of studies.demonstrated through appropriate and relevant assessment.based on empirical evidence and the scientific approach to knowledge development.accredited or validated against national or international standards (for relevant programs).acquired from personal interaction with research active educators, from year 1.informed and infused by cutting edge research, scaffolded throughout their program of studies. ![]() This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below: University Graduate Attribute Linear Prediction - Forward and backward linear prediction Levinson Durbin Lattice filters. Least squares and recursive least squares. Optimum Linear Systems - Error surfaces and minimum mean square error Optimum discrete time Wiener filter Principle of orthogonality and canonical forms Constrained optimisation Method of steepest descent - convergence issues Stochastic gradient descent LMS - convergence in the mean and mis-adjustment Case study. Introductory and Preliminary material - Introduction to the concepts, key issues and motivating examples for adaptive filters Discrete time linear systems and filters Random variables and random processes, covariance matrices Z transforms of stationary random processes. Linear systems (discrete & continuous), linear algebra, probability theory, Fourier & Z transforms & MATLAB School of Electrical & Electronic Engineering
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