Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm
A method for denoising accelerometer data based on the L2‐norm total variation (LTV) algorithm is presented. In order to collect accelerometer data, a wireless accelerometer sensor was developed that is directly connected to a central node. By benefiting from the LTV algorithm, the obtained signals...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-03-01
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| Series: | Electronics Letters |
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| Online Access: | https://doi.org/10.1049/el.2016.3811 |
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| _version_ | 1832096706051702784 |
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| author | R. Abbasi‐Kesbi A. Nikfarjam |
| author_facet | R. Abbasi‐Kesbi A. Nikfarjam |
| author_sort | R. Abbasi‐Kesbi |
| collection | DOAJ |
| description | A method for denoising accelerometer data based on the L2‐norm total variation (LTV) algorithm is presented. In order to collect accelerometer data, a wireless accelerometer sensor was developed that is directly connected to a central node. By benefiting from the LTV algorithm, the obtained signals from the accelerometer are denoised. The proposed method is tested by denoising in different accelerometer signals and the results are evaluated by signal‐to‐noise ratio and power spectral density functions of the signals. The obtained results reveal that noise reduction has been implemented satisfactorily. Hence, the measurement accuracy of accelerometer signals for the proposed method have improved ∼4–10% than other the three low‐pass filters including Savitzky–Golay, equiripple‐pass‐band and Butterworth. |
| format | Article |
| id | doaj-art-ce0ce2e7b21e44f9ae02315faf568ab8 |
| institution | Kabale University |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2017-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-ce0ce2e7b21e44f9ae02315faf568ab82025-02-05T12:30:43ZengWileyElectronics Letters0013-51941350-911X2017-03-0153532232410.1049/el.2016.3811Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithmR. Abbasi‐Kesbi0A. Nikfarjam1MEMS & NEMS Laboratory, Faculty of New Sciences and TechnologiesUniversity of TehranTehranIranMEMS & NEMS Laboratory, Faculty of New Sciences and TechnologiesUniversity of TehranTehranIranA method for denoising accelerometer data based on the L2‐norm total variation (LTV) algorithm is presented. In order to collect accelerometer data, a wireless accelerometer sensor was developed that is directly connected to a central node. By benefiting from the LTV algorithm, the obtained signals from the accelerometer are denoised. The proposed method is tested by denoising in different accelerometer signals and the results are evaluated by signal‐to‐noise ratio and power spectral density functions of the signals. The obtained results reveal that noise reduction has been implemented satisfactorily. Hence, the measurement accuracy of accelerometer signals for the proposed method have improved ∼4–10% than other the three low‐pass filters including Savitzky–Golay, equiripple‐pass‐band and Butterworth.https://doi.org/10.1049/el.2016.3811Butterworth low pass filterequiripple‐pass‐band low pass filterSavitzky‐Golay low pass filternoise reductionpower spectral density functionsignal‐to‐noise ratio |
| spellingShingle | R. Abbasi‐Kesbi A. Nikfarjam Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm Electronics Letters Butterworth low pass filter equiripple‐pass‐band low pass filter Savitzky‐Golay low pass filter noise reduction power spectral density function signal‐to‐noise ratio |
| title | Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm |
| title_full | Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm |
| title_fullStr | Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm |
| title_full_unstemmed | Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm |
| title_short | Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm |
| title_sort | denoising mems accelerometer sensors based on l2 norm total variation algorithm |
| topic | Butterworth low pass filter equiripple‐pass‐band low pass filter Savitzky‐Golay low pass filter noise reduction power spectral density function signal‐to‐noise ratio |
| url | https://doi.org/10.1049/el.2016.3811 |
| work_keys_str_mv | AT rabbasikesbi denoisingmemsaccelerometersensorsbasedonl2normtotalvariationalgorithm AT anikfarjam denoisingmemsaccelerometersensorsbasedonl2normtotalvariationalgorithm |