Video 44: Walsh-Hadamard Transform (Signal Filtering and Image Compression)

Hello Viewers, in this video, Walsh-Hadamard Transform (WHT) is explained. This video tutorial includes, its theory, applications and implementation of signal filtering and Image compression using WHT in MATLAB.

The Walsh-Hadamard Transform is non-sinusoidal, orthogonal transform that is widely used in the areas of signals and image processing.

This video has following contents:

• Introduction and Applications.
• Forward and Inverse Walsh-Hadamard Transform (1-D).
• Hadamard Matrix and Walsh Matrix (Sequency and Dyadic Ordering).
• WHT for Images.
• Example: Computing WHT of 1-D signals.
• Example:  Computing WHT of 2-D signals.
• Fast WHT algorithm.
• MATLAB Code for filtering of Noisy ECG signal using WHT.
• MATLAB Code for Image Compression using WHT.

Download Resources: 

1. ECG Signal: Download

2. Lena Image: Download

3. Pepper Image: Download

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Video 43: Symmetrical Components Analysis of Unbalanced Three-Phase Vector (Theory and MATLAB Code)

Hello Viewers, in this video, Symmetrical Components Analysis of three-phase unbalanced voltage or current vectors is presented.

These symmetrical components are useful in solving unsymmetrical faults in power system. In this video, method of obtaining symmetrical components is explained and also a MATLAB program is implemented to do the same.

This video has following contents:

• Introduction (Symmetrical and Asymmetrical 3-phase vectors) .
• Positive Sequence, Negative Sequence and Zero Sequence Vectors.
• Operator ‘a’.
• Method of getting PS, NS and ZS components.
• Example Analysis.
• MATLAB Code to get symmetrical components.

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Video 42: Animated 2D and 3D Plots using MATLAB

Hello Viewers, in this video, It is explained that how one can create animated 2D and 3D plots using MATLAB.

This video tutorial shows the implementation of slow motion 2D, 3D curve plotting, Slow motion movement of data cursor with values, Recording animated plots as videos and 3D surface plots in slow motion.

This video has following contents:

• Why animated plots?
• 2D animated plots: Slow motion Plots (Basic approach).
• 2D animated plots: Slow motion Plots (With inbuilt functions).
• Slow moving Marker with values.
• Making video of animated plots.
• 3D animated plots (Basic approach).
• 3D animated plots (With inbuilt functions).

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Video 41: Detecting STOP Traffic Sign using Deep RCNN (Real time and Offline mode)

Hello Viewers, in this video, It is explained that how one can implement a deep RCNN for detecting 'STOP' traffic signs from image and videos in both offline and real time mode.

Also, it is shown that how one can use MATLAB's Labeler app to create database for training.

Here, transfer learning is used and for fine tuning, a pre trained RCNN is re trained on our own image dataset which is created with help of Image Labeler app.

This video has following contents:

• Introduction.
• What is Deep R-CNN?
• Proposed Scheme using Deep R-CNN.
• Understanding MATLAB’s Image Labeler for image database creation.
• MATLAB Code for training.
• MATLAB Code for testing.
• Code execution and result analysis.

Links of previous videos:

1. How to create a Deep CNN: Click Here

2. ECG signals classification using wavelet features and deep CNN: Click Here​

3. Implementing deep CNN in Python using TF and Keras (Face Mask detection): Click Here

Download Resources: 

1. Test Video: Download (Source: Mathworks.com)

2. Traffic Sign Image Database: Download

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Video 40: How to Choose a Right Wavelet and Wavelet Transform? (Understanding Wavelet Properties)

Hello Viewers, in this video, It is explained that how one can choose appropriate wavelet transform and a right wavelet for a particular application.

To choose a right wavelet, it is important to understand few basic properties of the wavelets such as, Vanishing moments, Support width, Regularity, Symmetry and Orthogonality. 

These properties actually help us in selection of right wavelet.

This video has following contents:

• Introduction.
• What to choose CWT or DWT?
• Understanding wavelets properties.
• Wavelets for Feature Extraction.
• Wavelets for Denoising.
• Wavelets for Compression.
• Wavelets for detection of change or discontinuity.
• Wavelets for other applications (Analysis of Variance, Watermarking, Edge detection, ECG signals, OCR).

Link of previous videos:

1. Introduction to wavelet theory and its applications: Click Here​

2. Continuous Wavelet Transform (CWT) of 1D signals using Python and MATLAB: Click Here

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Video 39: Implementing Deep CNN in Python using TensorFlow and Keras (Face Mask Detection Problem)

Hello Viewers, in this video, it is shown that how a deep Convolutional Neural Network (CNN) can be implemented in Python using TensorFlow (TF) and Keras (K).

To understand its working, an interesting example is taken for implementation, where we can classify face images 'With Mask' and 'Without Mask'.

This video has following contents:

• Objective: Recognize a face with Mask or without Mask.
• Proposed Deep CNN based scheme.
• Introduction  to TensorFlow, Keras and Python.
• Image Dataset used.
• Python Code to implement deep CNN using TF and K.
• Simulation result analysis.

Links of previous videos:

1. How to create a Deep Neural Network in MATLAB: Click Here

2. Introduction to Deep Learning: Click Here

3. Anaconda Download: https://www.anaconda.com/products/individual#Downloads

4. Image Dataset: https://www.kaggle.com/ashishjangra27/face-mask-12k-images-dataset

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