Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Ibrahim, Idnin Pasya
Unknown Affiliation
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Clutter evalution of unmanned surface vehicles for maritime traffic monitoring Nadiy Zaiaami, Muhammad; Abd Rashid, Nur Emileen; Ismail, Nor Najwa; Ibrahim, Idnin Pasya; Enche Ab Rahim, Siti Amalina; Zalina Zakaria, Nor Ayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6836

Abstract

A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.
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 Brahin, Noor Mohd Ariff Enche Ab Rahim, Siti Amalina Ismail, Nor Najwa Mispan, Mohd Syafiq Nadiy Zaiaami, Muhammad Nasir, Haslinah Mohd Razman, Nur Fatin Shazwani Nor Zainuddin, Suraya Zalina Zakaria, Nor Ayu