Video 67: Local Binary Pattern (LBP)

 

Hello viewers. In this video, a very popular texture descriptor Local Binary Pattern (LBP) is explained. Here its basic theory, calculation method, its performance analysis and MATLAB implementation is given.  This video includes following components,

• Introduction to Local Binary Pattern (LBP).

• How an LBP feature is obtained from an image.

• Implementation of LBP in MATLAB (with MATLAB Code).

• Comparative performance analysis of LBP.

• LBP as feature vector for Machine Learning applications.

Download Timo Ojala Paper: Click here

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Video 66: Evolution of Laplace and Z-Transform from Fourier Transform

 

Hello viewers. In this video, the evolution of Laplace transform, and Z-transform from Fourier transform is explained. It is explained how the problem of non-convergence of Fourier for some signals is overcome by Laplace and Z-transform for continuous time and discrete time signals respectively.

This lecture includes following points.

• Fourier Transform review (CTFT and DTFT).
• Problem with Fourier Transform.
• Birth of Laplace Transform.
• Birth of Z-Transform.

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Video 65: Texture Classification using Wavelet Scattering Transform (with MATLAB Code)

 

Hello viewers. In this video, Texture Classification is presented based on Wavelet Scattering Transform (WST). WST is also briefly explained. This lecture explains that how WST coefficients can be used as feature vectors for classification task. Texture images are taken from KTH_TIPS and KYBERGE image databases. 

This video includes following components,

* Brief introduction to Wavelet Scattering Transform (WST).

* Computing Image Features (WST Coefficients as Features).

* Texture Image Databases used.

* Training Algorithm.

* Testing Procedure.

* MATLAB Implementation (with MATLAB Code).

Download:

Texture Image Database: Download

Link of previous video:

1. Introduction to Wavelet Theory and Its Applications: Click Here

2. Wavelet Scattering Transform for Signals and Images: Click Here

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Video 64: Wavelet Scattering Transform (WST) for Signals and Images (with MATLAB code)

 

Hello Viewers. In this video, Wavele Scattering Transform (WST) is explained for both 1D and 2D signals. This lectures explains that how WST coefficients can be computed for 1D and 2D signals. The MATLAB implementation is disscussed. MATLAB code is given and well explained for both the cases of 1D and 2D signals.

This video includes following components,

* Introduction to Wavelet Scattering Transform (WST).

* Computing WST coefficients of 1D signal.

* WST network similarity as CNN and WST coefficients as feature vector.

* WST for images (2D signals).

* MATLAB implementation (with MATLAB code).

Link of previous video:

Introduction to Wavelet Theory and Its Applications: Click Here

Download:

Test Images and Audio: Click Here

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Video 63: Comparison of Threshold Estimation Methods for Wavelet based Denoising of Audio Signals (with MATLAB Code)

 

Hello Viewers. In this video, a comparative study is shown to help us in selecting best combination of thresholding method, wavelet function and level of decomposition for denoising of audio of some Indian musical instruments.

This video includes following components,

• Introduction to denoising using wavelets.
• Various Noise estimation and Threshold Selection methods.
• MATLAB implementation (with MATLAB code).
• Applying these methods on audio of some Indian musical instruments.
• Comparative study and Result Analysis.

Wavelet transform is a very powerful tool in the field of Signal Denoising. It gives far better denoising results as compared to frequency selective filters.

Links of previous videos.

1. Introduction to Wavelet Theory and Its Applications: Click Here

2. Wavelet based denoising of audio signals using MATLAB and SIMULINL: Click Here

3.  Wavelet Based Denoising of 1D Signals using Python: Click Here

Download Audio Files

Click Here

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Video 62: Color Edge Features and DWT based Image Retrieval (With MATLAB Code)

 

Hello viewers, in this video, Content Based Image Retrieval (CBIR) is implemented. This CBIR utilizes both the color and edge features of the images. For this purpose, Color Edge Histograms are obtained. To reduce the size of feature vector, Discrete Wavelet Transform (DWT) is also used. The simulation results show the effectiveness of the proposed algorithm for effective CBIR.     

 

This video includes following contents, 

* Introduction to Content Based Image Retrieval (CBIR).

* Color Edge Feature (Proposed  Algorithm).

* Finding Feature Vector (Training Process).

* Testing Process.

* MATLAB implementation (with MATLAB code).

* Result Analysis.

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1. Previous video:

   Color Layout Descriptor (CLD) of MPEG7 for Image Retrieval: Click Here

   

2. Previous video:

   Edge Histogram Descriptor (EHD) of MPEG7 for Image Retrieval: Click Here

   

3. Previous video:

   Content Based Image Retrieval (CBIR) using Wavelet features, CLD and EHD of MPEG7: Click Here

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Download Resources:

Image Database: Click Here

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Video 61: Time Series Prediction using ANFIS (With MATLAB Code)

 

Hello viewers, in this video, The Time Series Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS) is explained. The time series taken here is Mackey-Glass chaotic time series, which is considered as benchmark problem. The ANFIS based algorithm for time series prediction is explained in detail.     

 

This video includes following contents, 

* Introduction to time-series prediction.

* ANFIS for time-series prediction.

* Mackey-Glass chaotic time series (A benchmark).

* Time series prediction algorithm using ANFIS.

* MATLAB implementation (with MATLAB code).

* Result Analysis.

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1. Previous video:

"Fuzzy Logic Controller (FLC)": Click Here

2. Previous video:

"ANFIS (Adaptive Neuro Fuzzy Inference System)": Click Here

 

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Video 60: ANFIS: Neuro-Fuzzy Inference System (Theory and MATLAB Implementation)

 

Hello viewers, in this video, The Neuro-Fuzzy modelling highlighting ANFIS is explained. The basic theory of ANFIS is presented. Also the complete process of MATLAB implementation is given. In MATLAB implementation, the ANFIS is used as universal approximator. The two functions 1D sin(t) and 2D sin(r)/r are realized using ANFIS.   

 This video includes following contents, 

• Neuro – Fuzzy Modelling.
• Adaptive Neuro-Fuzzy Inference System (ANFIS).
• ANFIS Architecture.
• ANFIS Hybrid learning algorithm.
• ANFIS Applications.
• ANFIS as Universal Approximator (UA).
• MATLAB Implementation of ANFIS as UA (with MATLAB code).

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1. Previous video:

"Fuzzy Logic Controller (FLC)": Click Here

2. Link for research paper of Jang: Click Here 

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Video 59: Understanding Fuzzy Logic Controllers (Theory and MATLAB Implementation)

 

Hello viewers, in this video, Fuzzy Logic Controller (FLC) is explained. Along with basic theory, the complete FLC design steps are explained. An FLC for steam throttle control is designed. Also the complete process of MATLAB implementation is explained.   

 

This video includes following contents, 

* Why need controllers?

* Classical controller and Fuzzy logic controller (FLC).

* Inside Fuzzy Logic Controller (FLC).

* Design of FLC for a throttle control system.

* Fuzzification.

* Rule base implementation.

* De-fuzzification.

* Simulation of designed FLC using MATLAB. 

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Video 58: Transform Basics (Concepts of Orthogonal, Bi-orthogonal vectors, Basis functions & Transforms)

 

Hello viewers, in this video, basic background of vector algebra is explained which will help the viewer to understand how transforms are evolved. The veiwer will learn about vector inner product, vector norm, orthogonal, orthonoroml, bi-orthogonal vectors and basis functions. Also it is explained how the basis functions can create a transforms.  

 

This video includes following contents, 

• Vector inner product. 
• Orthogonal vectors.
• Orthogonal and Orthonormal basis functions.
• Bi-Orthogonal and Bi-Orthonormal basis functions.
• Evolution of Transform and creating transformation matrix.
• 1D and 2D Orthogonal transforms (Real and Complex).
• 1D and 2D Bi-Orthogonal Transforms (Real and Complex).
• Numerical examples of various transforms.
• Some popular transforms.

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Video 57: Rice Grain Quality Assessment using Morphological Image Processing (Offline and Realtime mode)

 

Hello viewers, in this video, an implementation of rice quality assessment algorithm is shown. The proposed algorithm is based on morphological image processing. The quality of rice is estimated only on the basis of the length of rice grains. This implementation is done for both offline and online (Realtime) mode. In offline mode, images of rice grains are given to the algorithm and in real time mode, a camera is attached which takes images of rice grains and tells the outcome in real time.

 

This video includes following contents, 

• Need of automated rice grain quality assessment. 
• Steps of proposed scheme based on morphological image processing.
• Solving touching grains problem.
• MATLAB Code for proposed scheme.
• Testing in offline and online mode.

Download

Test Images: Download

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Video 56: Pseudo Coloring (Grayscale image to Color image Conversion)

 

Hello viewers, In this video, the concept of Pseudo Coloring is presented. Pseudo coloring is the process of converting grayscale image into color images. This lecture covers two methods, first is Intensity Slicing with few colors and with 256 colors while second method is Color Transformation. This video includes following contents, 

• What is Pseudo Coloring? 
• Need of Pseudo Coloring.
• Pseudo Coloring using Intensity Slicing.
• Pseudo Coloring using Color transformation.
• MATLAB Codes for Intensity Slicing, Color Transformation

Download

Test Images: Download

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Video 55: Discrete Fourier Transform (DFT) of 1-D Signals

 

Hello viewers, In this video, the basic theory of Discrete Fourier Transform (DFT) is presented. It includes background of DTFT and evolution of DFT from DTFT. It also explains that how DFT can be expressed as linear transformation for easy calculation of forward DFT and inverse DFT. Also computational complexity of DFT/ IDFT calculation is discussed and compared with FFT. The MATLAB implementation is also given by which one can find N-point DFT of any real sequence of any length. This video includes following topics,

* Introduction to Discrete Time Fourier Transform (DTFT).

* Need of Discrete Fourier Transform (DFT).

* DFT Computation method.

* DFT as linear transformation.

* Algorithm complexity.

* MATLAB Code to find N-point DFT of any sequence.

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Link of previous video

Discrete Fourier Transform (DFT) of images and image filtering: Click Here

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Video 54: Face Recognition using Wavelet Features and PCA (With MATLAB Code)

Hello viewers, In this video, a face recognition scheme is implemented using Wavelet Features and Principal Component Analysis (PCA). Here wavelets are used to extract facial features and PCA is used to reduce the size of wavelet feature vectors. The proposed scheme is very robust and capable to recognize the faces even some changes occur in faces such growing beard and mustache or putting goggles etc.

This video covers followings contents,

• Face image database (Faces94).
• Face image database preparation for training and testing.
• Finding wavelet features.
• PCA for dimension reduction.
• Training and testing procedures.
• MATLAB Code for Training.
• MATLAB Code for Testing (Discrete and Bulk).

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Links of previous videos:

1. Principal Component Analysis (PCA) for Images and Signals: Click Here

2. Face Recognition using PCA in MATLAB: Click Here

Links for Face Image Database:

1. https://cmp.felk.cvut.cz/~spacelib/faces/ (All Faces)

2. https://cmp.felk.cvut.cz/~spacelib/faces/faces94.html (Face94 database)

3. https://www.exptech.co.in/p/resource-download.html (Database used in video)

Download Resources:

1. All Image Database: Download

2. Test Images for Robustness: Download

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Video 53: ECG based Heart Disease Diagnosis using Wavelet Features and Deep CNN (Arrhythmia detection)

Hello viewers. This is recorded video of an invited guest lecture delivered by me in an international conference held in July 2021.

This video is about heart disease diagnosis using wavelet features and deep CNN mainly focusing on Arrhythmia detection and heart rate estimation.

This video includes following contents.

• Introduction (Problem Statement).
• Basics of ECG signals and QRS Complex. 
• ECG Database on PhysioNet.
• Proposed wavelet based algorithm for Heart Rate estimation and Arrhythmia detection.
• Deep CNN based approach of Arrhythmia detection.
• Conclusions.

This lecture is based on my previously published YouTube videos, which you can find on following links.

1. ECG signals Classification using CWT and Deep Neural Network in MATLAB: Click Here
2. ECG's QRS Peak Detection and Heart Rate Estimation using Discrete Wavelet Transform (DWT) in MATLAB: Click Here

Other Links:

1. PhysioNet: https://physionet.org/

2. PhysioNet Databases: https://physionet.org/about/database/

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

4. ECG signal database GitHub repository:

     https://github.com/mathworks/physionet_ECG_data/

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Video 52: LTI System Analysis using Python (With Python Code)

 

Hello Viewers, In this video, the introduction to LTI systems is described. Also various time domain and frequency domain analysis of LTI systems are implemented using Python. The Python program can plot various time response curves such as Impulse response, Step response and Ramp response. It can also give various stability plots such as Root locus, Nyquist plot, Bode plot, Log magnitude vs Phase plot (Nichols Plot) and Pole-zero plot.

The Python implementation is done using Spyder IDE in Anaconda environment. The Python Control System Library (v0.9.0) is used to write Python program.

This video includes following contents:

• Introduction to LTI systems.
• Various (Time domain/ Freq. domain) analysis of LTI systems.
• Anaconda and Spyder for Python implementation.
• Python Control System Library (v0.9.0).
• Python code for LTI System Analysis.

Link for previous video,

1. LTI System Analyzer using MATLAB GUI: Click Here

Other Links:

1. Anaconda Download: https://www.anaconda.com/distribution/

2. Spyder IDE: https://www.spyder-ide.org/

3. Python Control System Library (0.9.0): https://python-control.readthedocs.io/en/0.9.0/index.html

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Video 51: Solution of State Equations (Homogeneous & Nonhomogeneous) with MATLAB Simulation

 

Hello Viewers, in this video, the theory of solution of state equations is explained. Both the cases of homogeneous and non-homogeneous equations are considered. To clear the concept, some numerical examples are also solved. Also a MATLAB code is developed which is very efficient and capable to solve any state equations for any types of inputs. With this MATLAB program, students can solve questions of their text books and can verify their theoretical outcomes.

This video includes following contents:

• State Space model of systems.
• Solution of homogeneous state equation.
• State Transition Matrix (STM) and its properties.
• Example of solution of homogeneous state equation.
• Solution of non-homogeneous state equation.
• Examples of solution of non-homogeneous state equation.
• MATLAB code for solution of state equations.

Link for previous videos,

1. Introduction to State Space Analysis: Click Here

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Video 50: Introduction to State Space Analysis (Physical Systems Modelling) (With MATLAB Code)

 

Hello Viewers, in this video, the theory of state space modelling is explained. the modern approach based on state space is compared with classical approach of system modelling which is based on transfer functions highlighting advantages of state space method.

Also various physical systems such as Electrical and Mechanical systems are considered for state space modelling. 

The relationship between TF and SS for an LTI system is also established. Also a MATLAB code is explained to model a system in SS and to do various analysis of it.

This video includes following contents:

• Why State Space? (Classical and Modern approach of system modelling).
• Introduction to State space system modelling.
• Basic Definitions: State variables, State, State vector and State space.
• State space modelling of physical systems (Mechanical/ Electrical).
• State space to transfer function conversion and vice-versa. 
• MATLAB code for State space modelling.

Link for previous videos,

1. LTI System Analyzer using MATLAB GUI: Click Here

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Video 49: LTI System Analyzer using MATLAB GUI (With Code)

 

Hello Viewers, in this video, a graphical user interface (GUI) is created to analyze any LTI system. This GUI can plot various time response curves such as Impulse response, Step response, Ramp response. It can also give various stability plots such as Root locus, Nyquist plot, Bode plot, Log magnitude vs phase plot and Pole-zero plot.

This tutorial also covers basics of LTI systems. 

This video includes following contents:

• Introduction to LTI systems.
• Various (Time domain/ Freq. domain) analysis of LTI systems.
• Creating MATLAB GUI for LTI system analyzer using GUIDE.
• MATLAB code of Call Back functions for various GUI components.
• Program Simulation.

Link for previous videos,

1. Device Control using DTMF signals and MATLAB App designer: Click Here

2. Producing Colors with RGB LEDs using Arduino and MATLAB: Click Here

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Video 48: Denoising of Signals and Images using Wavelet Packet Transform

 

Hello Viewers, in this video, denoising of signals and images using Wavelet Packet Transform (WPT) is explained.

This tutorial explains the basic theoretical background of WPT based denoising scheme, noise variance estimation and universal threshold calculation. The denoising is implemented in MATLAB to do experiments with different noisy 1D and 2D signals. The concept of full tree and best tree based decomposition is also included.

This video includes following contents:

• Introduction to Wavelet Packet Transform (WPT).
• Objective and Noise Model.
• WPT based Denoising scheme.
• Noise variance estimation and finding universal threshold.
• Thresholding methods.
• MATLAB functions for WPT and WPT based denoising.
• MATLAB code for denoising 1D signals (Audio).
• MATLAB code for denoising 2D signals (Images).

Link for previous videos,

1. Introduction to wavelet theory and Its applications: Click Here

2. Wavelet Packet Transform of Signals and Images: Click here

3. Wavelet based denoising of audio signals using MATLAB & Simulink: Click Here

4. Wavelet based denoising of images using MATLAB: Click Here

Download Resources: 

1. Test Images: Download 

2. Test Audio Clips: Download

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