Implementation of Moving Average Filter on STM32F4 for Vibration Sensor Application

Thinh, Duong Truong; Quan, Nguyen Ba Hoang; Maneetien, Nopadon

doi:10.1109/gtsd.2018.8595630

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…Despite its limitations, such as its inability to preserve sudden transitions in the signal, this filter is frequently used due to its simplicity, effectiveness in reasonable data volumes and ease of implementation and comprehension (Hidayat et al, 2015;Thinh et al, 2018;Purnama et al, 2022;Ahn & Ko, 2009). The time complexity of this algorithm is linear and it is a robust and effective noise removal tool, with a reduced sensitivity to data anomalies and sudden transitions.…”

Section: Simple Moving Average Filtermentioning

confidence: 99%

Filtering Strategies for Smartphone Emitted Digital Signals

OBRETIN,

CORNEA

2024

Appl. Comput. Sci.

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In today's digitalized and technology-driven society, where the number of IoT devices and the volume of collected data is exponentially increasing, the use of sensor data has become a necessity in certain fields of activity. This paper presents a concise history of sensor evolution and specialization, with a focus on the sensors used for localization, particularly those present in microelectromechanical systems (MEMS) that make up inertial measurement units. The study starts with a general overview and progresses towards a more specific analysis of data sets collected from an accelerometer. In the materials and methods section, it emphasizes the imperfect nature of sensor measurements and the need to filter digital signals. Three different digital signal filtering techniques belonging to distinct filter categories are comparatively analyzed, with each technique having its own particularities, advantages and disadvantages. The analysis considers the effectiveness in reducing measurement errors, the impact of the filtering process on the original signal, the ability to highlight the underlying phenomenon, as well as the performance of the analyzed algorithms. The ultimate purpose of this article is to determine which filtration method is best suited for the signal at hand in the context of an indoor localization application.

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Section: Simple Moving Average Filtermentioning

confidence: 99%

Filtering Strategies for Smartphone Emitted Digital Signals

OBRETIN,

CORNEA

2024

Appl. Comput. Sci.

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“…The CMA is a type of moving average filter that can be used to smooth out and minimize the noise in the RSSI signal [29]. The CMA filter is a quick and easy approach to compute the average of a series of RSSI values over time.…”

Section: Cumulative Moving Averagementioning

confidence: 99%

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

Yaddarabullah,

Rahman,

Saad

2024

IJEECS

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This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.

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“…Mikrokontroler ini adalah μC yang tergolong sebagai medium-density devices (1024 Kbytes) [17]. Pada catatan terdahulu tentang Maple Mini, sebenarnya μC STM32F407VGT6 inilah yang dipergunakan sebagai inti di sistem itu [18]. Gambar 2.…”

Section: B Mikrokontroler Stm32unclassified

Implementasi Sistem Pendeteksi Api 360 Derajat Dengan Metode Multiplexer Dan Logika Fuzzy Pada Robot Pemadam Api Beroda

Saputro

2022

CYCLOTRON

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Dalam bidang pendidikan, robot digunakan sebagai bahan riset dan pengembangan untuk menghasilkan sesuatu yang lebih baik. Salah satunya adalah robot pemadam api yang sering digunakan untuk mengatasi kebakaran yang terjadi pada suatu tempat. Masalah yang sering dihadapi dalam pengembangan robot pemadam api adalah pendeteksian dan penentuan titik api yang kurang akurat sehingga dapat memakan waktu yang cukup lama. Pada penelitian ini mengimplementasikan sistem pendeteksi api 360 derajat dengan metode multiplexer dan logika fuzzy untuk mengatasi permasalahan tersebut. Dengan menggunakan sensor IR Receiver 940nm dan UV-Tron pendeteksian api akan lebih akurat. Kontrol sensor api menggunakan STM32F103 dengan metode multiplexer dan kontrol Motor DC menggunakan STM32F407 sehingga pemrosesan data bisa berjalan lebih cepat. Pengolahan input dan output pada sistem ini menggunakan metode logika fuzzy sehingga sistem bisa berjalan lebih halus dan memerlukan waktu yang lebih sedikit untuk menentukan dimana posisi api berada. Pengujian pemadaman 1 titik api sebanyak 12 kali memerlukan rata-rata waktu 9 detik, pemadaman 2 titik api sebanyak 6 kali memerlukan rata-rata waktu 25,16 detik, dan pemadaman 3 titik api sebanyak 4 kali memerlukan rata-rata waktu 37,75 detik.Kata kunci: Multiplexer, Logika Fuzzy, Robot Pemadam Api, IR Receiver 940nm, STM32

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Implementation of Moving Average Filter on STM32F4 for Vibration Sensor Application

Cited by 11 publications

References 7 publications

Filtering Strategies for Smartphone Emitted Digital Signals

Filtering Strategies for Smartphone Emitted Digital Signals

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

Implementasi Sistem Pendeteksi Api 360 Derajat Dengan Metode Multiplexer Dan Logika Fuzzy Pada Robot Pemadam Api Beroda

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