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ADVANCE SIGNAL PROCESSING
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ME02067021
Code: ME02067021
Credits: 4
Semester 2
AI
Advance Signal Processing & Estimation
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Follow on Instagram for UpdatesAdvance Signal Processing & Estimation Syllabus — Unit-wise Content
The official GTU curriculum for Advance Signal Processing & Estimation (subject code ME02067021) as prescribed for AI · Semester 2 divides the course into 4 units, covering 60 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02067021)Unit 1 DESIGN OF DIGITAL FILTERS
- Causality and Its Implications
- Characteristics of Practical Frequency- Selective Filters
- Design o f FIR Filters Symmetric and Antisymmeiric: FIR Filters
- Design of Linear-Phase FIR Filters Using Windows
- Design of Linear- Phase FIR Filters by the Frequency-Sampling Method
- Design of Optimum Equiripple Linear-Phase FIR Filters
- Design of FIR Differentiators
- Design of Hilbert Transformers
- Comparison of Design Methods for Linear-Phase FIR Filters
- Design of IIR Filters From Analog Filters
- IIR Filter Design by Approximation of Derivatives
- IIR Filter Design by Impulse Invariance
- IIR Filter Design by the Bilinear Transformation
- The Matched-
- Characteristics of Commonly Used Analog Filters
- Some Examples of Program Name: Master of Engineering Level: PG Branch: Applied Instrumentation Subject Code : ME02067021 Subject Name : Advance Signal Processing & Estimation Digital Filter Designs Based on the Bilinear Transformation
- Frequency Transformations in the Analog Domain
- Frequency Transformations in the Digital Domain
- Design of Digital Filters Based on Least-Squares Method: Pade approximation
- FIR Least-Squares Inverse (Wiener) Filters
- Design of IIR Filters in the Frequency Domain
Unit 2 MULTIRATE DIGITAL SIGNAL PROCESSING
- Decimation by a Factor D, Interpolation by a Factor I, Sampling Rate Conversion by a Rational Factor I/D Filter Design and Implementation for Sampling-Rate Conversion: Direct-Form FIR Filter Structures
- Polyphase Filter Structures
- Time- Variant Filter Structures
- Multistage Implementation of Sampling-Rate Conversion
- Sampling-Rate Conversion of Bandpass Signals
- Decimation and Interpolation by Frequency Conversion
- Modulation- Free Method for Decimation and Interpolation
- Applications of Multirate Signal Processing
- Design of Phase Shifters
- Interfacing of Digital Systems with Different Sampling Rates
- Implementation of Narrowband Lowpass Filters
- Implementation of Digital Filter Banks
- Subband Coding of Speech Signals
- Oversampling A/D and D/A Conversion
Unit 3 LINEAR PREDICTION AND OPTIMUM LINEAR FILTERS
- Innovations Representation of a Stationary Random Process
- Rational Power Spectra
- Relationships Between the Filter Parameters and the Autocorrelation Sequence
- Forward and Backward Linear Prediction
- Forward Linear Prediction
- Backward Linear Prediction
- Wiener Filters for Filtering and Prediction
- FIR Wiener Filter
- Orthogonality Principle in Linear Mean-Square Estimation
- IIR Wiener Filter
- Noncausal Wiener Filter
Unit 4 POWER SPECTRUM ESTIMATION
- Estimation of Spectra from Finite-Duration Observations of Signals
- Computation of the Energy Density Spectrum
- Estimation of the Autocorrelation and Power Spectrum of Random Signals: The Periodogram
- The Use of the DFT in Power Spectrum Estimation Non parametric Methods for Power Spectrum Estimation The Bartlett Method: Averaging Periodograms
- The Welch Method: Averaging Modified Periodograms
- The Blackman and Tukey Method: Smoothing the Periodogram
- Performance Characteristics of Nonparametric Power Spectrum Estimators Parametric Methods for Power Spectrum Estimation Relationships Program Name: Master of Engineering Level: PG Branch: Applied Instrumentation Subject Code : ME02067021 Subject Name : Advance Signal Processing & Estimation Between the Autocorrelation and the Model Parameters
- The Yule- Walker Method for the AR Model Parameters
- The Burg Method for the AR Model Parameters
- Unconstrained Least-Squares Method for the AR Model Parameters
- Sequential Estimation Methods for the AR Model Parameters
- Selection of AR Model Order
- MA Model for Power Spectrum Estimation
- ARMA Model for Power Spectrum Estimation
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