Syllabus

Signal Processing Onramp:

(1) Signal Data in MATLAB: (a) Importing a Signal, (b) Working with Time Information,
(c) Signal Analyzer , (d) View Power Spectrum, (e) Time Information and Sample Rate
(f) Compare Signals

(2) Preprocessing Signals: (a) Preprocessing Techniques, (b) Resample Signals
(c) Extract Interesting Regions, (d) Custom Preprocessing Function, (e) Align Signals

(3) Spectral Analysis: (a) Frequency Analysis, (b) Customize Power Spectrum Plot (c) Time-Frequency Analysis, (d) Create Time-Frequency Plots

(4) Filtering: (a) Low Pass Filter, (b) Band Pass Filter

(5) Signal Measurements: (a) View Generated Code, (b) Signal Measurements

Signal Generation and Resampling:

(1) Generate Digital Signals: (a) Generate a Signal, (b) Import a Signal
(c) Signal Sampling, (d) Generate a Tone, (e) Generate a Chirp

(2) Resample Signals: (a) Upsample and Downsample, (b) Effect of Aliasing, (c) Effect of Imaging,
(d) Upsample Audio, (e) Resample a Watermark

(3) Project: Generate and Resample a Square Wave.

Spectral Analysis Techniques:

(1) Discrete Fourier Transform: (a) Fourier Transform, (b) Fast Fourier Transform

(2) Periodogram: (a) Is FFT enough?, (b) Calculate the Periodogram

(3) Improve the Frequency Resolution of Spectrum: (a) Find Frequency Spikes, (b) Number of DFT Points, (c) Improve Precision: Zero Padding

(4) Manage Spectral Leakage: (a) Windowing:Key Points, (b) Frequency Resolution versus Spectral Leakage, (c) Compare Different Windows, (d) Resolve Tiny Tones, (e) Resolve Closely Spaced Tones

(5) Welch’s Method: (a) Spectral Analysis Techniques for Noisy Signals: pwelch method, (b) Spectrum of Noisy Signals, (c) Welch’s Method, (d) Decrease Window Length, (e) Downsides of Welch’s Method, (f)Increase Overlap.

(6) Parametric and Subspace Methods: (a) Frequencies in Short Signals (b) Parametric Methods: Burg Method, (d) Subspace Methods

Time Frequency Analysis

(1) Time-Varying Signals: (a) Time-Varying Frequency Signals: (i) Time-Varying Signals, (ii) Time-Varying Gear Box Signal , (iii) Time-Frequency Visualizations.

(2) Fourier Time-Frequency Analysis: (a) Spectrogram : The Spectrogram Function, (b) Effect of Window Length on Spectrogram, (c) Improve the Time Resolution: (i) Visualize Spectrogram, (ii) Improve Time Resolution, (iii) Spectrogram Matrix

(3) Wavelet Time-Frequency Analysis: (a) Scalogram: (i) Scalogram - Continuous wavelet transform, (ii) Wavelets (iii) How more number of vanishing moments is beneficial for signal
compression and noise removal? (iv) How meyer wavelets give faster attenuation? (v) How smooth signals has slow attenuation?
(b) Improve the Frequency Resolution: (i) Improve the Frequency Resolution (ii) Scalogram Matrix

(4) Project - Identify Fan Faults

Filter Design and Analysis Methods

(1) Design a Filter from Specifications: (a) Example - Verify Watermark,
(b) Filter Specifications: (i) Common Filter Specifications, (ii) Common Filter Configurations,
(c) Interactively Design a Filter: (i) Interactively Design a Filter, (ii) Design FIR Filter, (iii) designfilt, (iv) Design IIR Filter.

(2) Filter Design Methods: (a) Filter Design Algorithms, (b) Filter Designer: (i) Filter Designer, (ii) Design a Filter using Filter Designer

(3) Filter Analysis: (a) Filter Responses, (b) Impulse Response, (c) Frequency Response:
(i) Compute Frequency Response, (ii) Filter Analyzer App, (d) Filter Delay: (i) Compensate for Group Delay, (ii) Zero-Phase Filtering, (e) Filter Transients: (i) Filter Transients, (ii) FIR Filter Transient, (iii) IIR Filter Transient

(4) Arbitrary Response Filter Design and Analysis:
(a) Example - Underwater Sound Absorption, (b) Arbitrary Filter Response: (i) Create Desired Absorption Filter, (ii) Filter Coefficients, (iii) Arbitrary Filter Response .

(5) Filter Stability: (a) Zeros and Poles, (b) FIR Filter Stability: (i) FIR Zero-Pole Plot,
(ii) Reduce FIR Filter Delay, (c) IIR Filter Stability

Signal Plot Assignment

(1) Stem Function, (2) Hist and Bar Functions, (3) Signal Dilation or Downsampling or Decimation
(4) Convolution function (5) Correlation Analysis

Filter Structures Assignment
(1) Direct and Cascade Filter, (2) Parallel Filter, (3) All-Zero Lattice Filter, (4) Lattice Ladder Filter

To familiarize students with signal representation in MATLAB — importing, generating, sampling and resampling signals, and applying preprocessing operations such as filtering of outliers, region extraction, alignment and decimation, while understanding the effects of aliasing and imaging.

To impart a working knowledge of spectral estimation techniques, including the DFT/FFT, periodogram, windowing, Welch's method and parametric/subspace methods, and their relative merits for short, noisy and closely spaced-tone signals.

To introduce time–frequency analysis tools, namely the short-time Fourier transform (spectrogram) and the continuous wavelet transform (scalogram), for characterizing non-stationary and time-varying signals.

To develop competence in digital filter design, realisation and analysis — translating specifications into FIR and IIR designs, implementing them in direct, cascade, parallel and lattice structures, and assessing response, delay, transients and stability.

On successful completion of this laboratory course, the student will be able to:
CO1: Acquire, generate, resample and preprocess signals in MATLAB, and interpret their time-domain behaviour using stem, bar, convolution and correlation operations along with standard signal measurements.

CO2: Estimate the power spectrum of a given signal using FFT, periodogram, Welch, and parametric methods, and select the DFT length, window type, and overlap to balance frequency resolution against spectral leakage and variance.

CO3: Analyse time-varying signals using spectrograms and scalograms, and justify the choice of window length, wavelet family, and number of vanishing moments to achieve the required time–frequency resolution, as in fault identification applications.

CO4: Design FIR and IIR filters from given specifications using designfilt and the Filter Designer app, realise them in direct, cascade, parallel and lattice structures, and evaluate impulse/frequency response, group delay, transients and stability

Vinay K. Ingle and John G. Proakis, Digital Signal Processing Using MATLAB, Cengage Learning , 2012

John G. Proakis, Digital Signal Processing Using MATLAB, MATLAB , https://in.mathworks.com/matlabcentral/fileexchange/2189-digital-signal-processing-using-matlab

Dr. Ana Bell Prof. Eric Grimson Prof. John Guttag, INTRODUCTION TO COMPUTER SCIENCE AND PROGRAMMING IN PYTHON, Massachusetts Institute of Technology , https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/video_galleries/lecture-videos/