| Course Name | Intelligent Signal Processing for Advanced Wireless Systems |
| Course Code | 25WN601 |
| Program | M.Tech. Wireless Networks & Applications (Specialising in IoT, AI, 5G, Blockchain) (For Working Professionals & Regular Students) |
| Semester | 1 |
| Credits | 3 |
| Campus | Amritapuri |
Linear Algebraic Signal Models and System Representation
Normed spaces, inner product spaces, and Hilbert spaces, Linear independence, orthogonality, orthonormal basis, Eigenvalues, eigenvectors, and Singular Value Decomposition (SVD), Linear Time-Invariant (LTI) systems: convolution and impulse response, Signal subspaces and state-space/difference equation-based system modeling.
Spectral Signal Characterization and Discrete-Time System Analysis
Fourier Transform (Continuous-Time Fourier Transform (CTFT), Discrete-Time Fourier Transform (DTFT), Discrete Fourier Transform (DFT ): Definition and their properties, Z-Transform, Inverse Z-Transform and their properties: region of convergence, stability, causality, Transfer functions of LTI systems, Hilbert Transform, analytic signals, and complex envelopes. Power and energy spectral density, Parseval’s theorem, Sampling theory: aliasing, anti-aliasing, and reconstruction filters, Multirate processing: interpolation and decimation techniques.
Time-Frequency Localization and Multiresolution Analysis
Short-Time Fourier Transform (STFT) and spectrogram computation, Limitations of fixed-resolution Fourier methods, Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT), Time-frequency localization and multiresolution signal analysis, Wavelet filter banks, decomposition levels, and signal denoising strategies.
Advanced Digital and Adaptive Filtering Techniques
Design and implementation of digital IIR and FIR filters, Kalman filtering for state estimation and prediction in time-series, Wiener filtering for optimal signal estimation in noisy environments, Adaptive filtering algorithms -Least Mean Squares (LMS), Recursive Least Squares (RLS), Median and nonlinear filters for robust artifact suppression in physiological and environmental data.
Laboratory: Sampling, Spectral and Discrete Analysis, Time-Frequency Techniques, and Filtering Methods Applied to Real-World Signal Processing.
Course Outcome Statement (CO)
|
CO No. |
Course Outcome |
|
CO1 |
Apply mathematical signal modeling concepts to represent and analyze sensor data. |
|
CO2 |
Perform spectral and time-frequency analyses to extract features from sensor signals. |
|
CO3 |
Apply sampling theory, multirate signal processing, and advanced filtering to optimize sensor data acquisition and enhancement. |
|
CO4 |
Design and implement signal preprocessing pipelines using MATLAB/Python for intelligent decision-making in real-world sensor applications. |
CO – PO Affinity Map
|
PO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PS01 |
PSO2 |
PSO3 |
|
|
CO |
||||||||||||||||
|
CO1 |
3 |
3 |
3 |
– |
2 |
– |
2 |
– |
2 |
– |
– |
1 |
2 |
– |
– |
|
|
CO2 |
3 |
3 |
2 |
– |
2 |
– |
3 |
– |
2 |
– |
– |
1 |
3 |
– |
– |
|
|
CO3 |
3 |
3 |
3 |
– |
3 |
– |
2 |
– |
2 |
– |
– |
1- |
3 |
– |
– |
|
|
CO4 |
3 |
3 |
3 |
– |
3 |
– |
2 |
– |
3 |
– |
– |
2 |
3 |
– |
– |
|
3-strong, 2-moderate, 1-weak
DISCLAIMER: The appearance of external links on this web site does not constitute endorsement by the School of Biotechnology/Amrita Vishwa Vidyapeetham or the information, products or services contained therein. For other than authorized activities, the Amrita Vishwa Vidyapeetham does not exercise any editorial control over the information you may find at these locations. These links are provided consistent with the stated purpose of this web site.
