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Filtering Noisy Data From Analog PT1000 Temperature Sensor with Selection Sort Algorithm
(Selçuk Üniversitesi, 2023 Nisan) Golcuk, Adem
There are many sensors to digitize measurable data in medicine, automotive, industry and all areas of life. When reading data from these sensors, interferences occurs due to both sensor error and environmental effects. Although hardware filters, such as low-pass filters, reduce erroneous data read from sensors, they cannot completely filter out. For this reason, it is seen that many digital filters have been developed in the literature. In this study, selection sort algorithm was used to filter the erroneous and noisy data obtained from PT1000 temperature sensors. Although the results produced by the selection sort software filter used in this study are tested with the data read from the PT1000 temperature sensor, it can be used to filter the noisy data produced by all sensors. As a result of the experimental studies, it was observed that the noisy data in the raw data was completely filtered and the data frequency read from the sensor decreased with the selection sort algorithm used in this study. It is understood that the selection sort algorithm used to filter the noisy data read from the sensor completely deletes the noisy data, but reduces the data reading frequency. As a result of this study, it is suggested that selection sort algorithm can be used to filter noisy data in sensor applications that do not require high frequency.