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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Abdul Kader, Mohammed
International Islamic University Chittagong
Computer Science & IT Electrical & Electronics Engineering

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Wireless Need Sharing and Home Appliance Control for Quadriplegic Patients Using Head Motion Detection Via 3-Axis Accelerometer Abdul Kader, Mohammed; Orna, Sadia Safa; Tasnim, Zarin; Hassain, Md Mehedi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5593

Abstract

Patients who are quadriplegic are immobile in all four limbs. Quadriplegic patients with low voices struggle to communicate their needs to family members or caregivers, requiring assistance to use household items like fans and lights. This paper presents an electronic system designed to enhance the quality of life of quadriplegic patients by enabling them to share needs, manage household items, and monitor their health. The quadriplegic patient can move their head. In the proposed system, an accelerometer sensor placed on the patient’s forehead to record head movement, which is processed to detect and share needs or operate home appliances. The system consists of two units: one in the patient’s bed and another in a common place at home. Both communicate through Bluetooth. By moving head in the right direction, patients can share needs like water, rice, snacks, sickness or washroom. The common unit notifies caregivers through a matrix display and makes sounds with a buzzer. Patients can also control specific household appliances through left-head movements. The system also features a pulse oximeter sensor for monitoring heart rate and oxygen saturation. A prototype of the system has been developed and tested, and it is functioning smoothly. This system will free the quadriplegic patients from dependence on others and make their lives easier.
Webcam Based Robust and Affordable Optical Mark Recognition System for Teachers Somaiya, Effat; Sun Mim, Alifa; Abdul Kader, Mohammed
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5856

Abstract

The growing need for efficient automated grading solutions has driven advancements in optical mark recognition (OMR) systems for multiple-choice assessments. This paper introduces a novel webcam-based OMR system that employs advanced image processing and computer vision techniques to eliminate the dependency on specialized hardware. The proposed system enhances image quality, extracts relevant data, and accurately processes marked responses through a robust pipeline of preprocessing, segmentation, and recognition algorithms. Addressing challenges such as inconsistent handwriting styles and varying lighting conditions, the system demonstrates high accuracy and reliability, achieving an impressive accuracy rate of 100%. Experimental validation highlights significant improvements in grading efficiency, reduced human error, and enhanced consistency when compared to manual grading methods. The scalability of the system makes it applicable to remote learning environments, online exams, and large-scale assessment scenarios. Future research directions include integrating machine learning techniques to extend the system’s capabilities to subjective assessments and potential collaborations with educational institutions and online platforms. This research contributes to the field by providing an accessible and scalable automated grading solution that optimizes assessment workflows and improves the educational experience.
The Effect of Noise on Speaker Identification and Finding a Noise that Improves Accuracy Islam, Md Atiqul; Abdul Kader, Mohammed
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.5867

Abstract

Conventional Speaker Identification (SID) systems accurately identify speakers if their speech is noiseless. However, their classification accuracies reduce substantially when speech is corrupted by noise. SID systems would be more practical and applicable if they were more noise-robust. We introduce an SID system that can accurately classify speakers, even when their speech is corrupted by various types of noise at different noise levels. We investigate the impact of noisy training data on the performance of an SID system and the noise that may enhance the performance of an SID system. In this paper, we compare two front-end feature extractors: a cochlea model called the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) and an FFT-based Gammatone Frequency Cepstral Coefficient (GFCC). We use the Gaussian Mixture Model with the Universal Background Model (GMM-UBM) and a Extreme Learning Machine (ELM) as classifiers to focus on the influence of the front-ends on performance. We train the GMM-UBM and the neural network with noisy data under various conditions to investigate the impact of noise on the classifier. Our results suggest that noisy training data make an SID system noise-robust while the performance under clean conditions remains almost the same. More interestingly, training with speech-shaped noise (cocktail party) enhances SID accuracy more than white noise.
Co-Authors Hassain, Md Mehedi Islam, Md Atiqul Orna, Sadia Safa Somaiya, Effat Sun Mim, Alifa Tasnim, Zarin