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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Zainuddin, Suraya
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Spectral estimator effects on accuracy of speed-over-ground radar Mohd Shariff, Khairul Khaizi; Zainuddin, Suraya; Abdul Aziz, Noor Hafizah; Abd Rashid, Nur Emileen; Zalina Zakaria, Nor Ayu
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3900-3910

Abstract

Spectral estimation is a critical signal processing step in speed-over-ground (SoG) radar. It is argued that, for accurate speed estimation, spectral estimation should use low bias and variance estimator. However, there is no evaluation on spectral estimation techniques in terms of estimating mean Doppler frequency to date. In this paper, we evaluate two common spectral estimation techniques, namely periodogram based on Fourier transformation and the autoregressive (AR) based on burg algorithm. These spectral estimators are evaluated in terms of their bias and variance in estimating a mean frequency. For this purpose, the spectral estimators are evaluated with different Doppler signals that varied in mean frequency and signal-to-noise ratio (SNR). Results in this study indicates that the periodogram method performs well in most of the tests while the AR method did not perform as well as these but offered a slight improvement over the periodogram in terms of variance.
Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches Razman, Nur Fatin Shazwani Nor; Nasir, Haslinah Mohd; Zainuddin, Suraya; Brahin, Noor Mohd Ariff; Ibrahim, Idnin Pasya; Mispan, Mohd Syafiq
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6929-6943

Abstract

Respiration-related disease refers to a wide range of conditions, including influenza, pneumonia, asthma, sudden infant death syndrome (SIDS) and the latest outbreak, coronavirus disease 2019 (COVID-19), and many other respiration issues. However, real-time monitoring for the detection of respiratory disorders is currently lacking and needs to be improved. Real-time respiratory measures are necessary since unsupervised treatment of respiratory problems is the main contributor to the rising death rate. Thus, this paper reviewed the classification of the respiratory signal using two different approaches for real-time monitoring applications. This research explores machine learning and deep learning approaches to forecasting respiration conditions. Every consumption of these approaches has been discussed and reviewed. In addition, the current study is reviewed to identify critical directions for developing respiration real-time applications.
Doppler radar-based pothole sensing using spectral features in k-nearest neighbors Aiman Dani Asmadi, Muhammad; Zainuddin, Suraya; Mohd Nasir, Haslinah; Syafiza Md Isa, Ida; Emileen Abd Rashid, Nur; Pasya, Idnin
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8398

Abstract

Potholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning.
Signal processing for abnormalities estimation analysis Razman, Nur Fatin Shazwani Nor; Nasir, Haslinah Mohd; Zainuddin, Suraya; Brahin, Noor Mohd Ariff; Ibrahim, Idnin Pasya; Mispan, Mohd Syafiq
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp600-610

Abstract

Pneumonia, asthma, sudden infant death syndrome (SIDS), and the most recent epidemic, COVID-19, are the most common lung diseases associated with respiratory difficulties. However, existing health monitoring systems use large and in-contact devices, which causes an uncomfortable experience. The difficulty in acquiring breathing signals for non-stationary individuals limits the use of ultra-wideband radar for breathing monitoring. This is due to ineffective signal clutter removal and body movement removal algorithms for collecting accurate breathing signals. This paper proposes a breathing signal analysis for non-contact physiological monitoring to improve quality of life. The radar-based sensors are used for collecting the breathing signal for each subject. The processed signal has been analyzed using continuous wavelet transform (CWT) and wavelet coherence with the Monte Carlo method. The finding shows that there is a significant difference between the three types of breathing patterns; normal, high, and slow. The findings may provide a comprehensive framework for processing and interpreting breathing signals, resulting in breakthroughs in respiratory healthcare, illness management, and overall well-being.
Co-Authors Abd Rashid, Nur Emileen Abdul Aziz, Noor Hafizah Aiman Dani Asmadi, Muhammad Brahin, Noor Mohd Ariff Emileen Abd Rashid, Nur Ibrahim, Idnin Pasya Mispan, Mohd Syafiq Mohd Nasir, Haslinah Mohd Shariff, Khairul Khaizi Nasir, Haslinah Mohd Pasya, Idnin Razman, Nur Fatin Shazwani Nor Syafiza Md Isa, Ida Zalina Zakaria, Nor Ayu