Advanced Digital Signal Processing Ece 6th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective II) detail syllabus for Electronics And Communication Engineering (Ece), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (EC8091), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other ece 6th sem syllabus for be 2017 regulation anna univ you can visit Ece 6th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective II subjects do refer to Professional Elective II. The detail syllabus for advanced digital signal processing is as follows.
Course Objective:
- To learn and understand the concepts of stationary and non-stationary random signals and analysis and characterization of discrete-time random processes
- To enunciate the significance of estimation of power spectral density of random processes
- To introduce the principles of optimum filters such as Wiener and Kalman filters
- To introduce the principles of adaptive filters and their applications to communication engineering
- To introduce the concepts of multi-resolution analysis
Unit I
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Unit II
Spectrum Estimation
Bias and consistency, Non-parametric methods – Periodogram, modified-Periodogram -performance analysis. Bartletts method, Welchs method, Blackman-Tukey method. Performance comparison. Parametric methods – autoregressive (AR) spectrum estimation – autocorrelation method, Pronys method, solution using Levinson Durbin recursion.
Unit III
Optimum Filters
Wiener filters – FIR Wiener filter – discrete Wiener Hopf equation, Applications – filtering, linear prediction. IIR Wiener filter – causal and non-causal filters. Recursive estimators – discrete Kalman filter.
Unit IV
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Unit V
Multiresolution Analysis
Short-time Fourier transform – Heisenberg uncertainty principle. Principles of multi-resolution analysis – sub-band coding, the continuous and discrete wavelet transform – properties. Applications of wavelet transform – noise reduction, image compression.
Course Outcome:
At the end of the course, the student should be able to:
- Articulate and apply the concepts of special random processes in practical applications
- Choose appropriate spectrum estimation techniques for a given random process
- Apply optimum filters appropriately for a given communication application
- Apply appropriate adaptive algorithm for processing non-stationary signals
- Apply and analyse wavelet transforms for signal and image processing based applications
Text Books:
- Monson H. Hayes, “Statistical digital signal processing and modeling”, John Wiley and Sons Inc. New York, Indian reprint 2008. (Unit I-IV)
- P. P. Vaidyanathan, “Multirate systems and filter banks”, Prentice Hall Inc. 1993 (Unit V)
References:
- John G. Proakis and Dimitris G.Manolakis, Digital Signal Processing – Principles, Algorithms and Applications, Fourth Edition, Pearson Education / Prentice Hall, 2007.
- Sophoncles J. Orfanidis, “Optimum signal processing”, McGraw Hill, 2000
For detail syllabus of all other subjects of BE Ece, 2017 regulation do visit Ece 6th Sem syllabus for 2017 Regulation.
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