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### 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).
• 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.

### 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.

### 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).

### 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.

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

### 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).

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

### 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:

• 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.

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

### Video 38: Wavelet Based Robust Spread Spectrum Watermarking of Color Images (With Code)

Hello Viewers, in this video, Watermarking of color images using Discrete Wavelet Transform (DWT) is presented. The proposed watermarking scheme is based on spread spectrum watermarking, which is robust against several attacks.

This video has following contents:

• Color Space Selection.
• Proposed DWT based spread-spectrum watermarking for color images.
• Watermark embedding and watermark extraction algorithms.
• Performance evaluation parameters.
• MATLAB Code to implement watermarking (Embedding/ Extraction).
• Result analysis.

### Video 37: Hand Gesture Recognition using Basic Image Processing and Device Control in Real Time

Hello Viewers, in this video, a real time Hand Gesture Recognition (HGR) using simple image processing steps, is presented. This HGR is able to count number of fingers when a hand is shown to the camera. The proposed scheme uses few morphological operations to recognize number of fingers and it generates DTMF tone equivalent to fingers count. With this DTMF signal, we can control devices (ON/ OFF) with help of DTMF decoder and relay driver circuit.

This video has following contents:

• What is Hand Gesture Recognition (HGR)?
• Hand Gesture Recognition (HGR) scheme using basic image processing.
• Various steps of achieving HGR.
• DTMF generation and device control.
• MATLAB Code to implement HGR and Device control.

Previous Videos:

1) Device Control using DTMF signals and MATLAB's App Designer: Click Here

### Video 36: Wavelet Based Robust Spread Spectrum Watermarking of Grayscale Images

Hello Viewers, in this video, Watermarking of grayscale images using Discrete Wavelet Transform (DWT) is presented. The proposed watermarking scheme is based on spread spectrum watermarking, which is known to be robust against several attacks.

This video has following contents:

• What is image watermarking?
• Types of watermarking and challenges in image watermarking
• DWT based spread-spectrum watermarking for grayscale images.
• Watermark embedding and watermark extraction algorithms.
• Performance evaluation parameters.
• MATLAB Code to implement watermarking (Embedding/ Extraction).
• Result analysis.

Research paper of I. J. Cox:

### Video 35: Fourier Series implementation using Symbolic Math's Toolbox of MATLAB

Hello Viewers, in this video, Fourier series is implemented and simulated using Symbolic Math's Toolbox of MATLAB.

Both the forms of Fourier series i.e. Trigonometric and Exponential are implemented.

The proposed programs are versatile and have capability to receive any function of t. The program gives plots of harmonics, original function and approximated function, Magnitude spectrum and Phase spectrum.

With these programs, students can solve any Fourier series problem of their text books.

This video has following contents:

• What is Fourier Series?
• Trigonometric Fourier Series.
• Exponential Fourier Series.
• MATLAB Code to implement Trigonometric Fourier Series.
• MATLAB Code to implement Exponential Fourier Series.

### Video 34: Device Control using DTMF signals and MATLAB's App Designer

Hello Viewers, in this video, a device control method is presented using DTMF signals. These DTMF signals are generated by a GUI app which is built by MATLAB App Designer. Also related hardware such as DTMF decoder, Relay driver etc. are also explained.

The content provides a good platform to learn and helps the students to build a project on the suggested framework. It can be used to control up to 16 different electrical equipment.

This video has following contents:

• Device Control Strategy.
• Understanding DTMF generation.
• DTMF Recognition and Relay Driver Hardware.
• MATLAB Code to generate DTMF tones.
• Building Application using App Designer.
• MATLAB code for DTMF App.

### Video 33: Content Based Image Retrieval (CBIR) using Wavelet Features, CLD and EHD of MPEG-7

Hello Viewers, in this video, a Content Based Image Retrieval (CBIR) system is implemented. This CBIR utilizes Color Layout Descriptor (CLD) for color feature extraction and Wavelets and Edge Histogram Descriptor (EHD) for extraction of texture features.

This CBIR uses a feature vector of just length of 181 and it also shows enough robustness against distorted input images.

The implementation is done in MATLAB.

This video has following contents:

• What is Content Based Image Retrieval (CBIR)?
• CLD and EHD as feature vectors.
• Use of Wavelets for features extraction.
• Image Databases used for image retrieval.
• Training Procedure and Testing Procedure.
• Performance Parameters for a CBIR system.
• MATLAB code of complete CBIR system.

1. Color Layout Descriptor (CLD) of Mpeg-7 for Image Retrieval: Click Here

2. Edge Histogram Descriptor (EHD) of Mpeg-7 for Image Retrieval: Click Here

1. Wang Image Database: http://wang.ist.psu.edu/docs/related/​

### Video 32: Edge Histogram Descriptor (EHD) of Mpeg-7 for Image Retrieval

Hello Viewers, in this video, Edge Histogram Descriptor (EHD) of MPEG-7 family is explained. It is shown that EHD is one of the effective visual descriptor which focuses on the spatial edge distribution in an image. EHD mainly captures five types of edge orientations such as Vertical, Horizontal, Diagonal 45, Diagonal 135 and isotropic.

This EHD is compact in size and is just length of 80 points. If global bin is also attached then it becomes of size 85. Due to small size, it is suitable for fast image search. In this video, Theory of EHD, its implementation in MATLAB and its effectiveness in image retrieval is shown.

This video has following contents:

• What is Content Based Image Retrieval (CBIR)?
• MPEG-7 Visual Descriptors.
• Edge Histogram Descriptor (EHD).
• Steps to compute EHD.
• MATLAB Code for implementing EHD.
• Applying EHD on images and interpreting the outcome.
• Using EHD for image retrieval.

### Video 31: Color Layout Descriptor (CLD) of MPEG-7 for Image Retrieval (With MATLAB Code)

Hello Viewers, in this video, Color Layout Descriptor (CLD) of MPEG-7 family is explained. It is shown that CLD is one of the effective visual descriptor which focuses on the color distribution in an image.

This CLD is compact in size and obtained using fast computation, therefore, it is suitable for fast image search. CLD also shows scale invariance property. In this video, Theory of CLD, its implementation in MATLAB and its effectiveness in image retrieval is shown.

This video has following contents:

• What is Content Based Image Retrieval (CBIR)?
• MPEG-7 Visual Descriptors.
• Color Layout Descriptor (CLD).
• Steps to compute CLD.
• MATLAB Code for implementing CLD.
• Applying CLD on two images and getting their closeness.
• Showing effectiveness of CLD in image retrieval.

### Video 30: Producing Colors with RGB LEDs using Arduino and MATLAB (With GUI and MATLAB Code)

Hello Viewers, in this video, A scheme is presented to produce any color using RGB LEDs controlled by Arduino Uno board. The Arduino Uno board is interfaced with MATLAB.

By viewing this video, one can learn how to interface Arduino Uno with MATLAB and also will get idea that how different colors are produced from three primary colors Red, Green and Blue.

A Graphical User Interface (GUI) is also created using MATLAB guide to produce colors.

This video has following contents:

• What is Arduino Board?
• How to produce 16.7 million colors?
• Hardware schematics (Arduino with RGB LED).
• Adding Hardware Support Package for Arduino in MATLAB.
• Designing GUI using MATLAB’s GUIDE.
• MATLAB code for color production.

### Video 29: Design and Comparative Performance Analysis of P, I, D, PI, PD & PID Controllers (With MATLAB Code)

Hello Viewers, in this video, different types of continuous controllers such as,

Proportional (P), Integral (I), Derivative (D), Proportional + Integral (PI), Proportional + Derivative (PD) and Proportional + Integral + Derivative (PID)

are explained with their brief theory and properties.

Their performances are compared with each other highlighting their advantages and disadvantages. The MATLAB implementation is also given for all these controllers.

This video has following contents:

• Why need Controllers?
• Types of Basic Controllers (P, I and D Controllers) and their properties.
• Controller combinations (PI, PD and PID Controllers) and their properties.
• MATLAB code for design and implementing PI, PD and PID Controllers.
• Comparative performance analysis of PI, PD and PID Controllers.

### Video 28: Discrete Fourier Transform (DFT) of images and Image Filtering (With Example MATLAB Codes)

Hello Viewers, in this video, Discrete Fourier Transform (DFT) of images is introduced. A brief theoretical background of Discrete Time Fourier Transform (DTFT) is first introduced and explained how DTFT is  evolved in DFT.  Also advantages of DFT are explained along with its limitations.

MATLAB code is also given for computing DFT of images and viewing its spectrum. Various low pass and high pass filters such as Ideal, Butterworth and Gaussian filters are explained along with MATLAB code. Frequency domain filtering of noisy images is highlighted with MATLAB code.

Also one example of single frequency removal is explained using notch filter.

This video has following contents:

• 2D-Discrete Fourier Transform (DFT), its Applications and Limitations.
• MATLAB Code for 2D-DFT of a square function and of a natural image.
• Various types of filters (LPF and HPF: Ideal, Butterworth and Gaussian).
• MATLAB code for implementing various filters.
• Frequency domain filtering of noisy images.
• MATLAB Code for image filtering using implemented filters.
• MATLAB Code for removing particular frequency using a Notch Filter.

1. Discrete Cosine Transform (DCT) of Images and Image Compression: Click Here

### Video 27: Discrete Cosine Transform (DCT) of Images and Image Compression (Examples with MATLAB codes)

Hello Viewers, in this video, Discrete Cosine Transform (DCT)of images is introduced. A brief theoretical background of DCT is highlighted along with its application to image compression.

Mathematical equations are also explained for forward DCT and inverse DCT and basis functions are also discussed. MATLAB codes are also given for basis function generation, image compression using dct2() and dctmtx().

This video has following contents:

• Computing 2D FDCT, IDCT and DCT basis functions.
• MATLAB Code for 2D-DCT basis functions.
• DCT computation methods (DCT using FFT and DCT using transformation matrix).
• Image Compression using DCT.
• MATLAB Code for image compression using dct2().
• MATLAB Code for image compression using dctmtx() and block processing.

### Video 26: ECG's QRS Peak Detection and Heart Rate Estimation using Discrete Wavelet Transform (DWT) in MATLAB

Hello Viewers, in this video, a scheme is presented to detect QRS complex (R-Peaks) of an ECG signal using un-decimated Discrete Wavelet Transform (DWT). It is shown that how band pass filtering of ECG signal is achieved to preserve R-peaks using DWT which is superior than frequency selective BPF. Also by counting total number of R-peaks, Heart Rate (in beats per minute) is  calculated. Also robustness of the proposed scheme is shown on different types of ECG signals taken from MIT-BIH arrhythmia and ECG-ID databases from PhysioNet.

• This video has following contents:
• What is QRS Complex in ECG signal?
• ECG Database on PhysioNet.
• An introduction to Symlet4 wavelet.
• Proposed DWT based Algorithm for R-Peak detection and Heart Rate estimation.
• MATLAB Code for R-Peak detection and Heart Rate estimation.

1. ECG signals Classification using CWT and Deep Neural Network in MATLAB: Click Here

2. Continuous Wavelet Transform of 1-D signals using PYTHON and MATLAB: Click Here

1. PhysioNet: https://physionet.org/​

3. WFDB-SWIG toolbox for MATLAB: https://physionet.org/content/wfdb-swig-matlab/1.0.0/

4. PhysioNet Bank ATM: https://archive.physionet.org/cgi-bin/atm/ATM

### Video 25: Image Fusion using Discrete Wavelet Transform (DWT) in MATLAB (Restoration, Mixing and Morphing)

Hello Viewers, in this video, Image fusion technique using Discrete Wavelet Transform (DWT) is presented. It is shown that how image fusion can be used to achieve image restoration, Image mixing and face morphing.

This video has following contents:

• What is Image Fusion?
• Types of Image Fusion.
• Achieving Restoration, Mixing and Morphing of images using fusion.
• Proposed DWT based Fusion Algorithm.
• MATLAB Code for Fusion of Color Images.

### Video 24: ECG Signals Classification using Continuous Wavelet Transform (CWT) & Deep Neural Network in MATLAB

Hello Viewers, in this video, ECG signals are classified using pretrained deep CNN such as AlexNet via transfer learning. As we know that AlextNet can accepts input as image only, therefore, it is not possible to give 1D ECG signals to AlexNet directly.

To solve this problem, we utilize the strength of Continuous Wavelet Transform (CWT) to represent 1D ECG signals into image, so that it can be fed as input to deep CNN AlexNet.

Using CWT, we obtain CWT coefficients of 1D ECG signal and these coefficients are arranged as scalogram to represent in form of image. The ECG database is taken from Physionet.

This video has following contents:

• Types of ECG Signals for Classification.
• ECG Signal Database.
• Converting 1D ECG signals to Image using CWT Scalogram.
• Transfer Learning via pretrained AlexNet deep CNN.
• MATLAB Code for CWT Scalogram Image database creation.
• MATLAB Code for AlexNet Training and Validation.

1. Continuous Wavelet Transform of 1D signals using Python and MATLAB: Click Here

2. How to create a deep neural network in MATLAB : Click Here

3. ECG signal database GitHub repository: https://github.com/mathworks/physionet_ECG_data/

### Video 23: Continuous Wavelet Transform (CWT) of 1-D Signals using Python and MATLAB (with Scalogram plots)

Hello Viewers, in this video, Continuous Wavelet Transform (CWT) and its applications are discussed. A brief theory of wavelet and CWT is presented. Also Python and MATLAB implementation is shown to compute continuous wavelet transform coefficients in the form beautiful Scalograms. These Scalograms are very important for study of CWT of 1-D signals, highlighting their properties such as frequency break, time discontinuity, burst etc. These Scalograms can also be used as image input to some deep neural network for 1D signals classification.

This video has following contents:

• Theory of Continuous Wavelet Transform(CWT).
• CWT Applications.
• CWT of 1-D signals using Python (Using PyWavelet).
• Python Code for CWT of simple signal and signal with discontinuity.
• CWT of 1-D signals using MATLAB (Older and newer functions support).
• MATLAB Code for CWT of simple signal and signal with discontinuity.

PyWavelet Documentation

### Video 22: How to Create a Deep Neural Network in MATLAB (Digit Recognition Example)

Hello viewers, In this video, It is explained that how one can create a deep neural network such as Convolutional Neural Network (CNN) in MATLAB. All the layers are also explained in details with their structure. This CNN will be trained with images of handwritten digits of MATLAB's dataset.

• MATLAB’s Digit Dataset.
• Digit Dataset Preparation.
• Structure of a Convolutional Neural Network (CNN).
• Explanation of layers of a CNN and Training Parameters.
• MATLAB Code of Training and Validation.
• MATLAB Code of Discrete Testing.

2. MNIST Dataset (Digit and Fashion) to PNG/JPG Images Conversion using MATLAB: Click Here

Digit Database:

### Video 21: MNIST Dataset (Digit & Fashion) to PNG/ JPG Images Conversion using MATLAB

Hello viewers, In this video, It is explained that how MNIST dataset which is in complex format (idx-ubytes and csv) can be converted in to simple png/ jpg images in structured folders. So that it becomes easy to visualize the dataset and to have an idea that what types of images we actually have in dataset for training and testing. For this purpose, two popular MNIST datasets are considered for conversion,

1. Handwritten Digit Dataset and

2. Fashion Dataset.

This video covers followings contents,

• MNIST Digit dataset (Yann Le and Kaggle link).
• Need of dataset as PNG/ JPG images.
• Conversion logic from csv to PNG/ JPG images and folder preparation.
• MATLAB Code for conversion of Digit dataset to PNG/ JPG images.
• MATLAB Code to conversion of Fashion dataset to PNG/ JPG images.

1. Digit Dataset,

Yann Le page (idx-ubytes): http://yann.lecun.com/exdb/mnist/​

Kaggle Page (csv): https://www.kaggle.com/oddrationale/mnist-in-csv

2. Fashion Dataset,

GitHub Page (idx-ubytes): https://github.com/zalandoresearch/fashion-mnist

Kaggle Page (csv): https://www.kaggle.com/zalando-research/fashionmnist

### Video 20: Introduction to Deep Learning

Hello viewers, In this video, A brief introduction of Deep Learning (DL) is presented. Also Supervised, Unsupervised and Reinforcement learning are also discussed. Convolutional Neural Networks are also explained and various popular deep neural architectures such as Alexnet, Googlenet, Resnet, RCNN and YOLO are presented. In the last, training a deep neural model is also explained via transfer learning and training from scratch.

This video covers followings contents,

• Branches of AI and Branches of ML.
• Supervised, Unsupervised and Reinforcement Learning.
• Deep Learning.
• Deep Learning Vs. Machine Learning.
• Convolutional Neural Network (CNN).
• Deep Learning Architecture (AlexNet, GoogleNet, VGG Net, ResNet, RCNN, YOLO)
• How to create Deep Learning Model?
• Training from scratch and Transfer Learning.