Monthly Archives: April 2020

Java Spring Framework

IN28MINUTES Blog

GitHub

github.com/in28minutes/learn, Amazing Cloud, Full Stack and Microservice Courses and Videos from in28Minutes

YouTube

Java Brains

What is the Spring framework really all about?
What is the difference between frameworks and libraries?
Spring Tutorial 01 – Understanding Dependency Injection

Spring Security Basics

Spring Security Basics

  • What is Spring Security really all about?
  • Five Spring Security Concepts – Authentication vs authorization
  • Adding Spring Security to new Spring Boot project
  • How to configure Spring Security Authentication
  • How to configure Spring Security Authorization
  • How Spring Security Authentication works
  • How to setup JDBC authentication with Spring Security from scratch
  • Spring Boot + Spring Security with JPA authentication and MySQL from scratch
  • Spring Boot + Spring Security + LDAP from scratch
  • What is JWT authorization really about
  • What is the structure of a JWT
  • Spring Boot + Spring Security + JWT from scratch
  • What is OAuth really all about – OAuth tutorial
  • OAuth terminologies and flows explained – OAuth tutorial
  • Implementing login with Facebook and Github from scratch

Spring Framework

Spring Framework

  • Spring Tutorial 01 – Understanding Dependency Injection
  • Spring Tutorial 02 – Setting Up
  • Spring Tutorial 03 – Understanding Spring Bean Factory
  • Spring Tutorial 04 – Writing Code Using the Bean Factory
  • Spring Tutorial 05 – ApplicationContext and Property Initialization
  • Spring Tutorial 06 – Using Constructor Injection
  • Spring Tutorial 07 – Injecting Objects
  • Spring Tutorial 08 – Inner Beans, Aliases and idref
  • Spring Tutorial 09 – Initializing Collections
  • Spring Tutorial 10 – Bean Autowiring
  • Spring Tutorial 11 – Understanding Bean Scopes
  • Spring Tutorial 12 – Using ApplicationContextAware
  • Spring Tutorial 13 – Bean Definition Inheritance
  • Spring Tutorial 14 – Lifecycle Callbacks
  • Spring Tutorial 15 – Writing a BeanPostProcessor
  • Spring Tutorial 16 – Writing a BeanFactoryPostProcessor
  • Spring Tutorial 17 – Coding To Interfaces
  • Spring Tutorial 18 – Introduction to Annotations and the Required Annotation
  • Spring Tutorial 19 – The Autowired Annotation
  • Spring Tutorial 20 – Some JSR-250 Annotations
  • Spring Tutorial 21 – Component and Stereotype Annotations
  • Spring Tutorial 22 – Using MessageSource To Get Text From Property Files
  • Spring Tutorial 23 – Event Handling in Spring
  • Spring Tutorial 24 – Introduction to AOP

Spring Boot Quick Start

Spring Boot Quick Start

  • Spring Boot Quick Start 1 – Introduction
  • Spring Boot Quick Start 2 – About The Course
  • Spring Boot Quick Start 3 – What is Spring Boot
  • Spring Boot Quick Start 4 – Spring and some of its problems
  • Spring Boot Quick Start 5 – What Spring Boot gives us
  • Spring Boot Quick Start 6 – Setting Up Development Environment
  • Spring Boot Quick Start 7 – Maven
  • Spring Boot Quick Start 8 – Creating a Spring Boot project
  • Spring Boot Quick Start 9 – Starting a Spring Boot application
  • Spring Boot Quick Start 10 – Spring Boot startup steps
  • Spring Boot Quick Start 11 – Adding a REST Controller
  • Spring Boot Quick Start 12 – Returning Objects From Controller
  • Spring Boot Quick Start 13 – What’s Happening Here: Bill Of Materials
  • Spring Boot Quick Start 14 – What’s Happening Here: Embedded Servlet Container
  • Spring Boot Quick Start 15 – How Spring MVC Works
  • Spring Boot Quick Start 16 – The REST API we’ll build
  • Spring Boot Quick Start 17 – Creating a business service
  • Spring Boot Quick Start 18 – Getting a single resource
  • Spring Boot Quick Start 19 – Creating a new resource using POST
  • Spring Boot Quick Start 20 – Implementing Update and Delete
  • Spring Boot Quick Start 21 – Unit Overview
  • Spring Boot Quick Start 22 – Using Spring Initializr
  • Spring Boot Quick Start 23 – Using Spring Boot CLI
  • Spring Boot Quick Start 24 – Using the STS IDE
  • Spring Boot Quick Start 25 – Using application properties
  • Spring Boot Quick Start 26 – What is JPA
  • Spring Boot Quick Start 27 – Adding Spring Data JPA
  • Spring Boot Quick Start 28 – Creating a Spring Data JPA Repository
  • Spring Boot Quick Start 29 – Making Crud Operations with Repository
  • Spring Boot Quick Start 30 – Adding Course APIs
  • Spring Boot Quick Start 31 – Adding Entity Relationship and Extending Repository
  • Spring Boot Quick Start 32 – Packaging and running a Spring Boot app
  • Spring Boot Quick Start 33 – Spring Boot Actuator
  • Spring Boot Quick Start 34 – Wrap Up

in28minutes

Introduction to Spring Framework in 10 Minutes

Code Java

Understand Dependency Injection in Java

Anthony Ferrara

Dependency Injection

YouTube: Sarada Herke – Graph Theory

Graph Theory part-1

  • Graph Theory: 01. Seven Bridges of Konigsberg
  • Graph Theory: 02. Definition of a Graph
  • Graph Theory: 03. Examples of Graphs
  • Graph Theory: 04. Families of Graphs
  • Graph Theory: 05. Connected and Regular Graphs
  • Graph Theory: 06 Sum of Degrees is ALWAYS Twice the Number of Edges
  • Graph Theory: 07 Adjacency Matrix and Incidence Matrix
  • Graph Theory: 08-a Basic Problem Set (part 1/2)
  • Graph Theory: 08-b Basic Problem Set (part 2/2)

Graph Theory part-2

  • Graph Theory: 09. Graph Isomorphisms
  • Graph Theory: 10. Isomorphic and Non-Isomorphic Graphs
  • BONUS: 10-b Graph Theory with Sage
  • Graph Theory: 11. Neighbourhood and Bipartite Test with Colours
  • Graph Theory: 12. Spanning and Induced Subgraphs
  • Graph Theory: 13. Degrees at Least Two Means a Cycle Exists

Graph Theory part-3

  • Graph Theory: 14a. Basic Graph Theory Problem Set 2
  • Graph Theory: 14b. Basic Graph Theory Problem Set 2
  • Graph Theory: 14c. Basic Graph Theory Problem Set 2
  • Graph Theory: 15.There Exists a 3-Regular Graph of All Even Order at least 4
  • Graph Theory: 16. Walks Trails and Paths
  • Graph Theory: 17. Distance Between Vertices and Connected Components

Graph Theory part-4

  • Graph Theory: 18. Every Walk Contains a Path
  • Graph Theory: 19. Graph is Bipartite iff No Odd Cycle
  • Graph Theory: 20. Edge Weighted Shortest Path Problem
  • Graph Theory: 21. Dijkstra’s Algorithm
  • Graph Theory: 22. Dijkstra Algorithm Examples

Graph Theory part-5

  • Graph Theory: 23. Euler Trails and Euler Tours
  • Graph Theory: 24. Euler Trail iff 0 or 2 Vertices of Odd Degree
  • Graph Theory: 25. Graph Decompositions
  • Graph Theory: 26. Cycle Decomposition iff All Vertices Have Even Degre
  • Graph Theory: 27. Hamiltonian Graphs and Problem Set

Graph Theory part-6

  • Graph Theory: 28. Hamiltonian Graph Problems
  • Graph Theory: 29. Lovasz Conjecture on Hamilton Paths
  • Graph Theory: 30. The 5 Known Vertex-Transitive Non-Hamiltonian Graphs
  • Graph Theory: 31. Lemma on Hamiltonian Graphs
  • Graph Theory: 32. Necessary (not sufficient) Condition for Existence of a Hamilton Cycle
  • Graph Theory: 33. Petersen Graph is Not Hamiltonian

Graph Theory part-7

  • Graph Theory: 34. Bridge edges
  • Graph Theory: 35. Bridges in Connected Graphs
  • Graph Theory: 36. Definition of a Tree
  • Graph Theory 37. Which Graphs are Trees
  • Graph Theory: 38. Three ways to Identify Trees
  • Graph Theory: 39. Types of Trees
  • Graph Theory: 40. Cayley’s Formula and Prufer Seqences part 1/2
  • Graph Theory: 41. Cayley’s Formula and Prufer Seqences part 2/2

Graph Theory part-8

  • Graph Theory: 42. Degree Sequences and Graphical Sequences
  • Graph Theory: 43. Havel-Hakimi Theorem on Graphical Sequences
  • Graph Theory: 44. Degree Sequence of a Tree
  • Graph Theory: 45. Specific Degrees in a Tree
  • Graph Theory: 46. Relation Between Minimun Degree and Subtrees
  • Graph Theory: 47. Subgraphs of Regular Graphs
  • Graph Theory: 48. Complement of a Graph
  • Graph Theory: 49. Cartesian Product of Graphs

Graph Theory part-9

  • Graph Theory: 50. Maximum vs Maximal
  • Graph Theory: 51. Eccentricity, Radius & Diameter
  • Graph Theory: 52. Radius and Diameter Examples
  • Graph Theory: 53. Cut-Vertices
  • Graph Theory: 54. Number of Cut-Vertices
  • Graph Theory: 55. Bridges and Blocks
  • Graph Theory: 56. Central Vertices are in a Single Block

Graph Theory part-10

  • Graph Theory: 57. Planar Graphs
  • Graph Theory: 58. Euler’s Formula for Plane Graphs
  • Graph Theory: 59. Maximal Planar Graphs
  • Graph Theory: 60. Non Planar Graphs
  • Graph Theory: 61. Characterization of Planar Graphs
  • Graph Theory: 62. Graph Minors and Wagner’s Theorem
  • Graph Theory: 63. Petersen Graph is Non-Planar

YouTube: Digital Image Processing by Alan Saberi

Digital Image procesing

  • Digital image processing: p000 Welcome and Start Here
  • Digital image processing: p001 – What is image and video processing (part 1)
  • Digital image processing: p002 – What is image and video processing (part 2)
  • Digital image processing: p003 – Course logistics
  • Digital image processing: p004 – Images are everywhere
  • Digital image processing: p005- Human visual system
  • Digital image processing: p006 – Image formation – Sampling Quantization
  • Digital image processing: p007 The why and how of compression
  • Digital image processing: p008 – Huffman coding
  • Digital image processing: p009 JPEGs 8×8 blocks
  • Digital image processing: p010 – The Discrete Cosine Transform (DCT)
  • Digital image processing: p011 – Quantization
  • Digital image processing: p012- JPEG_LS and MPEG
  • Digital image processing: p013- Bonus Run-length compression
  • Digital image processing: p014 Introduction to image enhancement
  • Digital image processing: p015 – Enhancement Histogram modification
  • Digital image processing: p016 Histogram equalization
  • Digital image processing: p017- Histogram matching
  • Digital image processing: p018 Introduction to local neighborhood operations
  • Digital image processing: P019 Mathematical properties of averaging
  • Digital image processing: p020 – Non-Local means
  • Digital image processing: p021 IPOL Demo – Non-Local means
  • Digital image processing: p022- Median filter
  • Digital image processing: p023 Demo – Median filter
  • Digital image processing: p024 – Demo – Unsharp masking
  • Digital image processing: p025 – Gradients of scalar and vector images
  • Digital image processing: p026- Concluding remarks
  • Digital image processing: p027 – What is image restoration
  • Digital image processing: p028 – Noise types
  • Digital image processing: p029- Demo – Types of noise
  • Digital image processing: p030- Demo – Types of noise – Noise and histograms
  • Digital image processing: p031 – Estimating noise
  • Digital image processing: p032 – Degradation Function
  • Digital image processing: p033 – Wiener filtering
  • Digital image processing: p034 – Demo – Wiener and Box filters
  • Digital image processing: p035 – Concluding remarks
  • Digital image processing: p036- Introduction to Segmentation
  • Digital image processing: p037 – On Edges and Regions
  • Digital image processing: p038 – Hough Transform with Matlab Demo
  • Digital image processing: p039 – Line Segment Detector with Demo
  • Digital image processing: p040- Otsus Segmentation with Demo
  • Digital image processing: p041 – Congratulations! 😀
  • Digital image processing: p042 – Interactive Image Segmentation
  • Digital image processing: p043 Graph Cuts
  • Digital image processing: p044 – Mumford-Shah
  • Digital image processing: p045 – Active Contours
  • Digital image processing: p046 – Behind the Scenes of Adobes Roto Brush
  • Digital image processing: p047 – End of the Week
  • Digital image processing: p048- Introduction to PDEs in Image and Video Processing
  • Digital image processing: p049 – Planar Differential Geometry
  • Digital image processing: p050 Surface Differential Geometry
  • Digital image processing: p051- Curve Evolution
  • Digital image processing: p052 – Level Sets and Curve Evolution
  • Digital image processing: p053- Calculus of Variations
  • Digital image processing: p054 – Anisotropic Diffusion
  • Digital image processing: p055 Active Contours
  • Digital image processing: p056 Bonus Cool Contrast Enhancement via PDEs
  • Digital image processing: p057 – Introduction to Image Inpainting
  • Digital image processing: p058 – Inpainting in Nature
  • Digital image processing: p059 PDEs and Inpainting
  • Digital image processing: p060- Inpainting via Calculus of Variations
  • Digital image processing: p061 Smart Cut and Paste
  • Digital image processing: p062 – Photoshop Inpainting Healing Brush
  • Digital image processing: p063- Video Inpainting and Conclusions
  • Digital image processing: p064 – Introduction to Sparse Modeling – Part 1
  • Digital image processing: p065 Introduction to Sparse Modeling – Part 2
  • Digital image processing: p066 – Sparse Modeling – Implementation
  • Digital image processing: p067- Dictionary Learning
  • Digital image processing: p068- Sparse Modeling Image Processing Examples
  • Digital image processing: p069 – A Note on Compressed Sensing
  • Digital image processing: p070 – GMM and Structured Sparsity
  • Digital image processing: p071- Bonus Sparse Modeling and Classification – Activity Recognition
  • Digital image processing: p072- – Introduction to Medical Imaging
  • Digital image processing: p073- Image Processing and HIV (Part I)
  • Digital image processing: p074- Image Processing and HIV (Part I)
  • Digital image processing: p075 – Brain Imaging Diffusion Imaging Deep Brain Stimulation
  • Digital image processing: p076- – Final course

YouTube: Digital Image Processing by Rich Radke

Intro to Digital Image Processing (ECSE-4540) Lectures, Spring 2015

  • DIP Lecture 1: Digital Image Modalities and Processing
  • DIP Lecture 2: The human visual system, perception, and color
  • DIP Lecture 3: Image acquisition and sensing
  • DIP Lecture 4: Histograms and point operations
  • DIP Lecture 5: Geometric operations
  • DIP Lecture 6: Spatial filters
  • DIP Lecture 7: The 2D Discrete Fourier Transform
  • DIP Lecture 8: Frequency domain filtering; sampling and aliasing
  • DIP Lecture 9: Unitary image transforms
  • DIP Lecture 10: Edge detection
  • DIP Lecture 11: Edge linking and line detection
  • DIP Lecture 12: Thresholding
  • DIP Lecture 12a: Image Segmentation
  • DIP Lecture 12b: Snakes, active contours, and level sets
  • DIP Lecture 13: Morphological image processing
  • DIP Lecture 13a: Region description and filtering
  • DIP Lecture 14: Object and feature detection
  • DIP Lecture 15: Lossless image coding
  • DIP Lecture 16: Lossy image compression
  • DIP Lecture 17: Image restoration and the Wiener filter
  • DIP Lecture 18: Reconstruction from parallel projections and the Radon transform
  • DIP Lecture 19: Fan-beam reconstruction
  • DIP Lecture 20: Dithering and halftoning
  • DIP Lecture 21: Digital watermarking
  • DIP Lecture 22: Image blending
  • DIP Lecture 23: Photomontage and inpainting
  • DIP Lecture 24: Image retargeting
  • DIP Lecture 24a: Digital Image Forensics
  • DIP Lecture 25: Active shape models

Kalman Filter

KalmanFilter.NET
Kalman Filter For Dummies
What is a Kalman filter?

YouTube

Michel van Biezen

SPECIAL TOPICS 1 – THE KALMAN FILTER

  • Special Topics – The Kalman Filter (1 of 55) What is a Kalman Filter?
  • Special Topics – The Kalman Filter (2 of 55) Flowchart of a Simple Example (Single Measured Value)
  • Special Topics – The Kalman Filter (3 of 55) The Kalman Gain: A Closer Look
  • Special Topics – The Kalman Filter (4 of 55) The 3 Calculations of the Kalman Filter
  • Special Topics – The Kalman Filter (5 of 55) A Simple Example of the Kalman Filter
  • Special Topics – The Kalman Filter (6 of 55) A Simple Example of the Kalman Filter (Continued)
  • Special Topics – The Kalman Filter (7 of 55) The Multi-Dimension Model 1
  • Special Topics – The Kalman Filter (8 of 55) The Multi-Dimension Model 2-The State Matrix
  • Special Topics – The Kalman Filter (9 of 55) The Multi-Dimension Model 3: The State Matrix
  • Special Topics – The Kalman Filter (10 of 55) 4: The Control Variable Matrix
  • Special Topics – The Kalman Filter (11 of 55) 5: Find the State Matrix of a Falling Object
  • Special Topics – The Kalman Filter (12 of 55) 6: Update the State Matrix
  • Special Topics – The Kalman Filter (13 of 55) 7: State Matrix of Moving Object in 2-D
  • Special Topics – The Kalman Filter (14 of 55) 8: What is the Control Variable Matrix?
  • Special Topics – The Kalman Filter (15 of 55) 9: Converting from Previous to Current State 2-D
  • Special Topics – The Kalman Filter (16 of 55) 10: Converting from Previous to Current State 3-D
  • Special Topics – The Kalman Filter (17 of 55) 11: Numerical Ex. of Finding the State Matrix 1-D
  • Special Topics – The Kalman Filter (18 of 55) What is a Covariance Matrix?
  • Special Topics – The Kalman Filter (19 of 55) What is a Variance-Covariance Matrix?
  • Special Topics – The Kalman Filter (20 of 55) Example of Covariance Matrix and Standard Deviation
  • Special Topics – The Kalman Filter (21 of 55) Finding the Covariance Matrix, Numerical Ex. 1
  • Special Topics – The Kalman Filter (22 of 55) Finding the Covariance Matrix, Numerical Ex. 2
  • Special Topics – The Kalman Filter (23 of 55) Finding the Covariance Matrix, Numerical Example
  • Special Topics – The Kalman Filter (24 of 55) Finding the State Covariance Matrix: P=?
  • Special Topics – The Kalman Filter (25 of 55) Explaining the State Covariance Matrix
  • Special Topics – The Kalman Filter (26 of 55) Flow Chart of 2-D Kalman Filter – Tracking Airplane
  • Special Topics – The Kalman Filter (27 of 55) 1. The Predicted State – Tracking Airplane
  • Special Topics – The Kalman Filter (28 of 55) 2. Initial Process Covariance – Tracking Airplane
  • Special Topics – The Kalman Filter (29 of 55) 3. Predicted Process Covariance – Tracking Airplane
  • Special Topics – The Kalman Filter (30 of 55) 4. Calculate the Kalman Gain – Tracking Airplane
  • Special Topics – The Kalman Filter (31 of 55) 5. The New Observation – Tracking Airplane
  • Special Topics – The Kalman Filter (32 of 55) 6. Calculate Current State – Tracking Airplane
  • Special Topics – The Kalman Filter (33 of 55) 7. Update Process Covariance – Tracking Airplane
  • Special Topics – The Kalman Filter (34 of 55) 8. Current Becomes Previous – Tracking Airplane
  • Special Topics – The Kalman Filter (35 of 55) 1, 2, 3 of Second Iteration – Tracking Airplane
  • Special Topics – The Kalman Filter (36 of 55) 4. Kalman Gain Second Iteration – Tracking Airplane
  • Special Topics – The Kalman Filter (37 of 55) 5, 6 of Second Iteration – Tracking Airplane
  • Special Topics – The Kalman Filter (38 of 55) 7, 8 of Second Iteration – Tracking Airplane
  • Special Topics – The Kalman Filter (39 of 55) Part 1 of Third Iteration – Tracking Airplane
  • Special Topics – The Kalman Filter (40 of 55) Part 2 of Third Iteration – Tracking Airplane
  • Special Topics – The Kalman Filter (41 of 55) Graphing 1st 3 Iterations (t vs x) – Tracking Airplane
  • Special Topics – The Kalman Filter (42 of 55) Graphing 1st 3 Iterations (t vs v) – Tracking Airpl***

MATLAB

Understanding Kalman Filters

  • Understanding Kalman Filters, Part 1: Why Use Kalman Filters?
  • Understanding Kalman Filters, Part 2: State Observers
  • Understanding Kalman Filters, Part 3: Optimal State Estimator
  • Understanding Kalman Filters, Part 4: Optimal State Estimator Algorithm
  • Understanding Kalman Filters, Part 5: Nonlinear State Estimators
  • Understanding Kalman Filters, Part 6: How to Use a Kalman Filter in Simulink
  • Understanding Kalman Filters, Part 7: How to Use an Extended Kalman Filter in Simulink

Taylor, Fourier, Laplace, Wavelet

Taylor

Fourier

  • Fourier-Analyse
  • Fourier-Transformation

Laplace

Wavelet

Wavelet and Fourier Transform | Easy understanding | Important features
Easy Introduction to Wavelets
Understanding Wavelets, Part 1: What Are Wavelets
Understanding Wavelets, Part 2: Types of Wavelet Transforms
Understanding Wavelets, Part 3: An Example Application of the Discrete Wavelet Transform
Understanding Wavelets, Part 4: An Example Application of Continuous Wavelet Transform
Understanding Wavelets, Part 5: Machine Learning and Deep Learning with Wavelet Scattering

OpenPnP – Open Source SMD Pick and Place System

McuOnEclipse

TAG ARCHIVES: OPENPNP
Building a DIY SMT Pick&Place Machine with OpenPnP and Smoothieboard (NXP LPC1769)

github.com/ErichStyger/McuOpenPnP_Machine, Repository for the McuOnEclipse OpenPnP machine
github.com/Scavenger18/RepoPNP, Files concering PAIND PNP Feeder

Hardware

SmoothieWare -> SmoothieBoard

Software

github.com/openpnp/openpnp/, Open Source SMT Pick and Place Hardware and Software

outgoingbot

OpenPNP done timing motors., 12.05.2019
OpenPNP – Hardware Update, 27.05.2019
Smd Feeder Idea. Laser cut + 1 stepper, 02.06.2019
OpenPNP update. Feeder system is done., 24.02.2020

YouTube: Bert Huang’s Machine Learning and AI

Fall 2015 Machine Learning

Fall 2015 Machine Learning

  • 1.1 Machine Learning
  • Types of Machine Learning
  • Model Selection in Machine Learning
  • 2 Probability and Naive Bayes
  • 3 Decision Trees
  • 4 Logistic Regression
  • 5 Perceptron
  • Neural Networks
  • 7 Learning Objective Functions
  • Back Propagation
  • 9 Support Vector Machines
  • 10 Dual SVM and Kernels
  • 11 Feature Maps and Kernels
  • 12 SMO and Stochastic SVM
  • 13 Regression
  • 14 Principal Component Analysis
  • 15 Clustering and Mixture Models
  • 16 Variational EM and K Means
  • 15.1 GMM Degeneracy
  • 17 Probabilistic Graphical Models and Bayesian Networks
  • Markov Models
  • Hidden Markov Models
  • Undirected Graphical Models
  • Belief Propagation
  • Graphical Models Wrap up
  • What is Learning Theory?
  • Model Complexity and VC Dimension
  • Fairness in Machine Learning
  • The Queen is King | Magnus Carlsen vs Fabiano Caruana | Norway Chess 2018

CS4804 Intro to AI, Fall 2016

CS4804 Intro to AI, Fall 2016

  • Intelligent Agents
  • Intro to Search
  • Informed Search
  • A* Optimality
  • Pruning (Alpha-Beta)
  • Probability Basics
  • More Realistic Adversarial Settings
  • Markov Decision Processes
  • Passive Reinforcement Learning
  • Active Reinforcement Learning
  • Logic
  • First Order Logic Overview
  • Bayesian Networks
  • Markov Models
  • Hidden Markov Models
  • Particle Filters
  • Types of Machine Learning
  • Model Selection in Machine Learning
  • Neural Networks
  • Back Propagation