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Ömer Katı's Projects

a-tooth-shaped-microstrip-antenna-for-ieee-802.11ax-wi-fi-applications icon a-tooth-shaped-microstrip-antenna-for-ieee-802.11ax-wi-fi-applications

This is the final project of the Antenna Design course. The antenna is based on Markina et. al, 2018, and operates at both 2.4 and 5 GHz. In addition, it conforms to the IEEE 802.11ax Europe standards with enhanced bandwidths. Also, the maximum gain of the higher band is improved by 2.65 dBi, and the physical size is reduced by 35%.

eigenvalues-and-eigenvectors-calculator icon eigenvalues-and-eigenvectors-calculator

This program is implemented as a project for EE 242 course, and it implements Normalized Power Iteration with Deflation algorithmm to calculate most dominant eigenvalue, its eigenvector and the second most dominant eigenvalue.

estimating-ph-of-solutions-using-ph-indicators-and-absorbance-spectroscopy icon estimating-ph-of-solutions-using-ph-indicators-and-absorbance-spectroscopy

This repo contains code and resources for estimating pH of solutions via pH indicators and absorbance spectroscopy. Solutions are mixed with indicators, resulting in measured absorbance spectra. Using 2 relative absorbance calculations and 1 calibration measurement, the unknown pH of a solution can be determined.

image-processor-tool icon image-processor-tool

In this project, the main aim is to utilize the OV7670 Camera Module to take a photo and display it on the VGA display using different signal processing algorithms realized on De1-SoC board using C language.

linear-equation-solver icon linear-equation-solver

This program implements the Gaussian elimination algorithm with partial pivoting together with backward substitution to solve Ax = b, where A is an n-n square matrix. The project was a part of EE 242 (Numerical Methods for EE) course.

ml-and-em-classification icon ml-and-em-classification

In this project, maximum likelihood estimation (MLE) and expectation maximization algorithms (EM) are applied to a dataset. Before the classification algorithms, principle component analysis (PCA) and multiple discriminant analysis (MDA) are applied to the dataset.

parzen-window-estimation-with-pnn icon parzen-window-estimation-with-pnn

In this project, PNN (w/ and w/o voting scheme) and KNN are applied to a dataset. Before the classification algorithms, principle component analysis (PCA) and multiple discriminant analysis (MDA) are applied to the dataset.

secant-and-bisection-methods icon secant-and-bisection-methods

This project implements secant and bisection algorithms in order to solve f(x)=0 for any given polynomial f. This project was a part of EE 242 (Numerical Methods for EE) course.

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