PID Controller Simulator GUI

PID controller is a name commonly given to a three-term controller. P stands for the proportional term, I for the integral term, and D for the derivative term in the controller [1]. PID controllers are probably the most widely used industrial controller. Even complex industrial control systems may comprise a control network whose main control building block is a PID control module [1].

This repository contains a MATLAB application (GUI) as well as Python code that can be used as an educational tool to understand, visualize, and test the concept of PID controllers in a real-life example. The application is inspired by the system built in [2]. In this tool, the PID controller will attempt to balance a green ball on a red moving bar. The position of the ball is measured, and the slope of the bar is modified (increased or decreased) in order to balance the green ball at a position defined by the user. 

In the simulation provided in the repository, the user can control:

• the initial position of the ball

• the initial slope of the bar

• the desired position at which the ball should top

• the PID controller's gains, namely, Kp, Ki, and Kd gains that correspond to P, I, and D terms, respectively.

Link to Repository: https://github.com/ludvikalkhoury/PID-Controller-Simulation.git 

Reference: 

[1] Crowe, J., Chen, G. R., Ferdous, R., Greenwood, D. R., Grimble, M. J., Huang, H. P., ... & Zhang, Y. (2005). PID control: new identification and design methods. Springer-Verlag London Limited. 

[2] Electronoobs. (2019, July 20). PID Balance+Ball | full explanation & tuning [Video]. YouTube. https://www.youtube.com/watch?v=JFTJ2SS4xyA 

Sperm Head Segmentation and Localization using Urbano's Method

This GitHub repository provides a Python code of the segmentation and localization methods presented by Urbano et al. (2017) [1]. The main library I used in order to implement all Image Processing operations is "OpenCV." The input to the code is a semen sample and the output is the same sample with red squares around the detected heads. 

Link to Repository: https://lnkd.in/ekPbez5 

Reference: 

[1] Urbano, L. F., Masson, P., VerMilyea, M., & Kam, M. (2016). Automatic tracking and motility analysis of human sperm in time-lapse images. IEEE transactions on medical imaging, 36(3), 792-801.

Keywords: 

Urbano's segmentation and detection, Github, Python, OpenCV, image processing, binarization, sperm, head detection, video reading, video generation. 

Handwritten Digit Recognition using HOG Features and SVM

Handwritten digit recognition is a well-studied problem. Various solutions to this problem are present in the literature. One solution is to extract the Histogram of Oriented Gradients (HOG) features from digits and use them (HOG features) to train a multi-class classifier such as a Support Vector Machine (SVM).

In the following repository, I provide a Matlab and a Python code, for handwritten digit classification using HOG and SVM. I used the MNIST data for training and testing.

Link to Repository: https://lnkd.in/eeJhBvu

Keywords:

Github, Python, OpenCV, image processing, HOG, SVM, machine learning, handwritten digit recognition, MNIST dataset.