Digital Signal Processing Algorithms
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Follow on Instagram for UpdatesDigital Signal Processing Algorithms Syllabus — Unit-wise Content
The official GTU curriculum for Digital Signal Processing Algorithms (subject code 2710501) as prescribed for EAC( · Semester 1 divides the course into 7 units, covering 34 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 (2710501)Unit 1 Introduction
- Discrete time systems
- Analysis of Discrete-time Linear time invariant systems
- Frequency analysis of Discrete time signals and Fourier Transform properties
- Frequency Response of LTI system
- The Z- transforms
- Inverse Z-transforms
- The Schur-Cohn stability test
Unit 2 Implementation of Discrete time systems
- Structures for FIR, IIR Systems
- Quantization of Filter Co-efficients
- Round-off Effects in Digital Filters
Unit 3 Discrete Fourier Transform and its applications
- Frequency domain Sampling
- Properties of DFT, Circular Convolution
- Linear Filtering Methods Based on the DFT, The discrete Cosine Transform
- frequency analysis of signals using the DFT
Unit 4 Fast Fourier Transform
- Efficient computation of DFT (FFT Algorithms-DIT and DIF)
- Application of FFT Algorithms
- The Goertzel Algorithm
- The Chirp-z Algorithm
- Quantization Effects in the Computation of DFT
Unit 5 Design of Digital Filters
- Design Techniques of FIR Filters – Windowing methods
- frequency sampling method
- Optimum Equiripple Linear Phase Filters
- FIR differentiators
- Hilbert Transformers
- design techniques of IIR filters : Impulse invariance
- Bilinear transformation
- finite difference
- Frequency Transformations
Unit 6 Discrete Wavelet Transform
- Introduction and applications 2 5
Unit 7 Power Spectrum Estimation
- Estimation of spectra from finite duration signals
- Periodogram Nonparametric methods - Bartlett
- Welch and Blackman-Tukey methods
- Parametric methods – ARMA
- AR and MA model based spectral estimation
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