Kazi Newaj Faisal

KAZI NEWAJ FAISAL, PhD

Assistant Professor

Electrical, Electronics and Communication Engineering (EECE) Dept.

Military Institute of Science and Technology (MIST), Bangladesh

Recent Updates

4th IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON 2025)

Details are available in:https://raaicon.org/

Two-Dimensional Successive Variational Mode Decomposition

2D extension of the successive variational mode decomposition (2D-SVMD) method is published, which decomposes 2D signals into band-limited intrinsic mode functions without requiring prior knowledge of the number of modes. The proposed 2D-SVMD addresses the limitations of existing 2D variational mode decomposition (2D-VMD) and 2D variational mode decomposition with total variation (2D-TV-VMD) methods, which need initialization of the number of modes and can produce redundant or mixed modes if the number is not accurately specified. 2D-SVMD decomposes in a sequential manner which enhances the convergence rate of the decomposition process and also mitigates the extraction of extraneous modes, collectively resulting in reduced computational overhead. The proposed 2D-SVMD is also evaluated on synthetic and real-world images and compared to 2D-VMD and 2D-TV-VMD, demonstrating its superior performance in achieving natural decomposition of 2D signals. [Details...]

EEG-based Unified Approach for Multiple Neurodevelopmental Disorders Detection in Children Using SVMD

Early age identification and prompt intervention play a crucial role in mitigating the severity of neurodevelopmental disorders in children. Traditional diagnostic approaches can be lengthy, but there is growing research potential in using electroencephalogram (EEG) signals to detect attention deficit hyperactivity disorder (ADHD) and intellectual developmental disorder (IDD). By recording the electrical activity of the brain, EEG has emerged as a promising technique for the early identification of these disorders. This research proposes a novel integrated method for identifying multiple neurodevelopmental disorders from the EEG signals of children. The approach combines successive multivariate variational mode decomposition (SMVMD) for analyzing multi-component non-stationary signals and a machine learning (ML)-based classifier, addressing the issue of inconsistent numbers of extracted features by introducing an energy-based feature integration approach. By integrating enhanced features from SMVMD with a K-nearest Neighbor (KNN) classifier, the unified approach successfully detects two separate neurodevelopmental disorders from normal subjects. [Details...]

Review of Automated Grasp Recognition using sEMG

Surface electromyography-based automated grasp recognition has emerged as a vital technology in various fields, such as automatic control, human-machine interfaces, prosthetics, and virtual reality. A review paper analyzing the available state-of-the-art methodologies for sEMG-based automated grasp recognition, covering sensing modalities, datasets, acquisition systems, pre-processing techniques, feature extraction, and identification systems has been published recently. The review also includes a detailed chronological analysis and comparison of grasp recognition techniques, focusing on the number of subjects and types of grasps. Additionally, the paper highlights open research issues and potential future prospects for these challenges, as well as several industry domains that can incorporate sEMG-based automated grasp recognition systems. [Details...]

Coronary Artery Disease Identification Using ECG

Coronary Artery Disease is a critical factor in various severe heart conditions. Early detection and treatment of CAD are crucial to prevent further disease progression. A novel approach utilizing improved energy estimation of electrocardiogram signals has been developed to automate the characterization of CAD conditions. The proposed method employs variational mode decomposition to overcome the challenges posed by the multi-component nature and nonlinear characteristics of ECG signals, enabling accurate energy estimation of ECG beats. Multiple statistical features extracted from the localized ECG beat regions were effectively classified with over 99.80% accuracy using an optimized ensemble classifier and 10-fold cross-validation. The findings demonstrate the effectiveness of the proposed methodology compared to contemporary approaches on identical datasets. [Details...]

EEG-based Brain-Computer Interface Development

Classifying Upper Limb Motor Imagery Tasks

Motor imagery research in brain-computer interfaces is critical for limb movement control. Also, cognitive robotics and engineering aim to develop brain-robot interfaces that enable effective collaboration between robots and humans. Advancements in neuroscience, robotics, and machine learning are expected to expand the scope of BRI applications, improving human-robot cooperation. Multiple approaches utilizing ratio of band power-based features, time-domain and band-power features to emphasize the inter-related information within EEG signals, combined with an optimized KNN and ensemble classifier, have been developed achieving better accuracy in categorizing MI tasks. [Details...]

Notices

I am actively looking for highly motivated BSc/MSc/PhD students (full-time/part-time/internship) to research on non-stationary signal processing, radar signal processing, biomedical signal processing, human-machine/computer interaction, brain-computer interfacing with application of artificial intelligence/machine learning for signal processing.

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