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All Journal Jurnal Riset Fisika Indonesia Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics
Ramadhan, Aditia
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Astronomy Computer Science & IT Earth & Planetary Sciences Education Materials Science & Nanotechnology Mathematics Physics

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Calibration and Signal Processing of MPU6050 Sensor to Improve Navigation Stability of Autonomous Underwater Vehicle (AUV) Putro, Permono Adi; Pinardi, Kuntjoro; Ramadhan, Aditia; Zein, Aulia Sultan Rafael; Nurfalah, Yokeu
TIME in Physics Vol. 3 No. 1 (2025): March
Publisher : Universitas Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/timeinphys.2025.v3i1p31-44

Abstract

A stable and accurate navigation system is crucial for the operation of Autonomous Underwater Vehicles (AUVs), especially in underwater environments where GPS signals are unavailable. This study aims to enhance AUV navigation performance by optimizing the use of a low-cost inertial sensor, the MPU6050, through calibration and signal processing techniques. The methodology includes reading raw data from accelerometer and gyroscope sensors, performing static calibration to reduce bias and noise, applying Fast Fourier Transform (FFT) for signal analysis, and implementing digital filters such as low-pass and high-pass filters. The raw data reveal significant deviations due to gyroscope bias and accelerometer noise. Static calibration effectively reduces systematic errors, although residual biases remain. FFT analysis identifies dominant frequency components in each axis, while digital filtering helps eliminate irrelevant frequency noise. Precision testing demonstrates that the sensor produces relatively stable data post-calibration, with the Z-axis showing higher deviation compared to the X and Y axes. The combination of calibration and signal processing methods significantly improves the data quality and reliability of the MPU6050 sensor. This research contributes to the development of more efficient and low-cost AUV navigation systems and supports the broader application of simple IMU sensors in underwater exploration and maritime technologies.
Analisis Band Power, Relative Power, dan Entropi Sinyal EEG saat Relaksasi dengan Mata Tertutup berdasarkan Brain Region Robiyana, Iqbal; Nurizati; Sumardi, Tedi; Ramadhan, Aditia; Suhendra, Muhammad Agung
Jurnal Riset Fisika Indonesia Vol 6 No 01: Desember 2025
Publisher : Jurusan Fisika, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jrfi.v6i01.6895

Abstract

Electroencephalography (EEG) is a widely used neurophysiological method for monitoring brain activity through scalp electrodes. This study investigates EEG signal characteristics in a resting state with eyes closed, focusing on three quantitative features: band power, relative power, and entropy. The experiment involved five healthy volunteers who were instructed to sit in a relaxed position with eyes closed for five minutes in a quiet, dimly lit room. EEG signals were recorded using an Emotiv EPOC+ device with 14 channels placed according to the international 10–20 system. The recorded signals were processed in MATLAB, including bandpass filtering (1–50 Hz), baseline correction, and artifact rejection. Subsequently, the signals were segmented into two-second epochs for feature extraction. Band power was calculated using the Fast Fourier Transform (FFT) for delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. Relative power was computed as the ratio of each band’s power to the total power of the signal, while signal entropy was estimated using Shannon entropy to assess complexity. EEG channels were grouped into four brain regions: frontal, temporal, parietal, and occipital. Results show that the occipital region exhibited the highest average band power, consistent with dominant alpha activity during eye closure. Relative power distributions were uniform across subjects and regions. The highest entropy values were observed in the temporal and frontal regions, indicating higher signal complexity in those areas. These findings highlight the effectiveness of combining spectral and nonlinear features to characterize brain activity during rest and provide valuable baselines for future applications in Brain-Computer Interfaces (BCI), stress detection, and neuropsychological mapping.
Co-Authors Iqbal Robiyana Muhamad Agung Suhendra Nurfalah, Yokeu Nurizati Permono Adi Putro Pinardi, Kuntjoro Tedi Sumardi Zein, Aulia Sultan Rafael