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Uda Hashim
Universiti Malaysia Perlis
Computer Science & IT Electrical & Electronics Engineering Engineering

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Performance analysis of low-complexity welch power spectral density for automatic frequency analyser Teh Yi Jun; Asral Bahari Jambek; Uda Hashim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.934 KB) | DOI: 10.11591/eei.v8i1.1393

Abstract

The aim of this paper is to investigate the performance of the Low Complexity Welch Power Spectral Density Computation (PSDC). This algorithm is an improvement from Welch PSDC method to reduce the computational complexity of the method. The effect of the sampling rate and the input frequency toward to accuracy of frequency detection is being evaluated. From the experiment results, sampling rate nearest to the twice of the input frequency provides the highest accuracy which achieved 99%. The ability of the algorithm to perform complex signal also has been investigated.
Performance comparison of automatic peak detection for signal analyser Teh Yi Jun; Asral Bahari Jambek; Uda Hashim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.888 KB) | DOI: 10.11591/eei.v8i1.1394

Abstract

The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD.  For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Performance analysis of low-complexity welch power spectral density for automatic frequency analyser Teh Yi Jun; Asral Bahari Jambek; Uda Hashim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.858 KB) | DOI: 10.11591/eei.v8i1.1393

Abstract

The aim of this paper is to investigate the performance of the Low Complexity Welch Power Spectral Density Computation (PSDC). This algorithm is an improvement from Welch PSDC method to reduce the computational complexity of the method. The effect of the sampling rate and the input frequency toward to accuracy of frequency detection is being evaluated. From the experiment results, sampling rate nearest to the twice of the input frequency provides the highest accuracy which achieved 99%. The ability of the algorithm to perform complex signal also has been investigated.
Performance comparison of automatic peak detection for signal analyser Teh Yi Jun; Asral Bahari Jambek; Uda Hashim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.671 KB) | DOI: 10.11591/eei.v8i1.1394

Abstract

The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD.  For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Performance analysis of low-complexity welch power spectral density for automatic frequency analyser Teh Yi Jun; Asral Bahari Jambek; Uda Hashim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.858 KB) | DOI: 10.11591/eei.v8i1.1393

Abstract

The aim of this paper is to investigate the performance of the Low Complexity Welch Power Spectral Density Computation (PSDC). This algorithm is an improvement from Welch PSDC method to reduce the computational complexity of the method. The effect of the sampling rate and the input frequency toward to accuracy of frequency detection is being evaluated. From the experiment results, sampling rate nearest to the twice of the input frequency provides the highest accuracy which achieved 99%. The ability of the algorithm to perform complex signal also has been investigated.
Performance comparison of automatic peak detection for signal analyser Teh Yi Jun; Asral Bahari Jambek; Uda Hashim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.671 KB) | DOI: 10.11591/eei.v8i1.1394

Abstract

The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD.  For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Co-Authors Asral Bahari Jambek Teh Yi Jun