Classification Based on Machine Learning Methods for Identification of Image Matching Achievements

Dewi Astria  Faroek; Rusydi Umar; Imam Riadi

doi:10.29207/resti.v6i2.3826

Dewi Astria Faroek Universitas Ahmad Dahlan, Yogyakarta
Rusydi Umar Universitas Ahmad Dahlan, Yogyakarta
Imam Riadi Universitas Ahmad Dahlan, Yogyakarta

DOI: https://doi.org/10.29207/resti.v6i2.3826

Keywords: image matching, logo, machine learning, kNN, RF, MLP

Abstract

The random noise signal is widely used as a test signal to identify a physical or biological system. In particular, the Gaussian distributed white noise signal (Gaussian White Noise) is popularly used to simulate environmental noise in telecommunications system testing, input noise in testing ADC (Analog to Digital Converter) devices, and testing other digital systems. Random noise signal generation can be done using resistors or diodes. The weakness of the noise generator system using physical components is the statistical distribution. An alternative solution is to use a Pseudo-Random System that can be adjusted for distribution and other statistical parameters. In this study, the implementation of the Gaussian distributed pseudo noise generation algorithm based on the Enhanced Box-Muller method is described. Prototype of noise generation system using a minimum system board based on Cortex Microcontroller or MCU-STM32F4. The test results found that the Enhanced Box-Muller (E Box-Muller) method can be applied to the MCU-STM32F4 efficiently, producing signal noise with Gaussian distribution. The resulting noise signal has an amplitude of ±1Volt, is Gaussian distributed, and has a relatively broad frequency spectrum. The noise signal can be used as a jamming device in a particular frequency band using an Analog modulator.

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