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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization
Enche Ab Rahim, Siti Amalina
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Computer Science & IT Electrical & Electronics Engineering

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Human movement detection and classification capabilities using passive Wi-Fi based radar Razali, Hidayatusherlina; Abd Rashid, Nur Emileen; Nasarudin, Muhammad Nazrin Farhan; Ismail, Nor Najwa; Ismail Khan, Zuhani; Enche Ab Rahim, Siti Amalina; Megat Ali, Megat Syahirul Amin; Zakaria, Nor Ayu Zalina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3545-3556

Abstract

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and human-computer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%.
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.
Hand Gesture Recognition Based on Continuous Wave (CW) Radar Using Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) Methods Rahman, Muhammad Khairani Abdul; Abdul Rashid, Nur Emileen; Ismail, Nor Najwa; Zakaria, Nor Ayu Zalina; Khan, Zuhani Ismail; Enche Ab Rahim, Siti Amalina; Mohd Isa, Farah Nadia
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.926

Abstract

Human-computer interaction (HCI) is a field of study studying how people and computers interact. One of the most critical branches of HCI is hand gesture recognition, with most research concentrating on a single direction. A slight change in the angle of hand gestures might cause the motion to be misclassified, thereby degrading the performance of hand gesture detection. Therefore, to improve the accuracy of hand gesture detection, this paper focuses on analyzing hand gestures based on the reflected signals from two directions, which are front and side views. The radar system employed in this paper is equipped with two sets of 24 GHz continuous wave (CW) monostatic radar sensors with a sampling rate of 44.1 kHz. Four different hand gestures, namely close hand, open hand, OK sign, and pointing down, are collected using SignalViewer software. The data is stored as a waveform audio file format (WAV) where one data consists of 20 segments, and the data is then examined by using MATLAB software to be segmented. To evaluate the effectiveness of the classification system, principal component analysis (PCA) and k-nearest neighbor (KNN) are integrated. The PCA findings are depicted in Pareto and 2-D scatter plot for both radar directions. The Leave-One-Out (LOO) method is then used in this analysis to verify the accuracy of the classification method, which is represented in the confusion matrix. At the end of the analysis, the classification results indicated that both angles achieved near-perfect accuracy for most hand gestures.
Co-Authors Abd Rashid, Nur Emileen Abdul Rashid, Nur Emileen Ibrahim, Idnin Pasya Ismail Khan, Zuhani Ismail, Nor Najwa Khan, Zuhani Ismail Megat Syahirul Amin Megat Ali Mohd Isa, Farah Nadia Nadiy Zaiaami, Muhammad Nasarudin, Muhammad Nazrin Farhan Rahman, Muhammad Khairani Abdul Razali, Hidayatusherlina Zakaria, Nor Ayu Zalina Zalina Zakaria, Nor Ayu