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Author: search.filters.author.Nalmi, R

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    PublicationOpen Access
    Vision incorporated MUltichannel Feature Fusion Template Matching (MUFF-TM) and real-time sub-pixel coordinate localization for 2D textile surface in ultrasonic tacking systems
    (Elsevier B.V., 2026-09) Kahandawala, B, S; Nalmi, R; Sodige, B, A.K; Subasinghage, K; Silva, B, N; Wijesinghe, R,E; Woo, S, T
    Temporary stitches are essential in apparel manufacturing as they temporarily secure fabric pieces to prevent misalignment during machine sewing and ensure high-quality results. Manual ultrasonic tacking machines were introduced to enhance the precision of this process; however, the necessity for expert operators remains a major constraint. This work introduces a real-time system for ultrasonic tacking machines that combines vision-guided single-modal MUltichannel Feature Fusion Template Matching (MUFF-TM) to autonomously identify and align tacking points on textiles with sub-pixel spatial accuracy. To overcome the limitations of classic feature-based algorithms on smooth and deformable fabrics, the proposed method utilizes macro-contour extraction and equidistant boundary sampling rather than relying on unstable local textures. Experimental results demonstrate that MUFF-TM achieves a 100% target detection rate with a highly stable Mean Absolute Error (MAE) of under 5 pixels across various dynamic conditions, including changes in orientation, illumination, scale, and non-rigid deformation. By significantly outperforming traditional algorithms (SIFT, SURF, and ORB) in spatial precision, the developed software interface offers a robust, versatile, and scalable solution for advancing automated precision in the apparel industry. Copyright
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    PublicationEmbargo
    Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms
    (IEEE, 2022-10-04) Nalmi, R; Rathnayake, N; Mampitiya, L.I
    In the domain of autonomous underwater vehicles, the classification of objects underwater is critical. The hazy effect of the medium causes this obstacle, and these effects are directed by the dissolved particles that lead to the reflecting and scattering of light during the formation process of the image. This paper mainly focuses on exploring the best possible image classifier for the underwater images of the different fish species. The classifications were carried out by different hybrid and supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NN), Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). This study compares the algorithms’ accuracy and time and analyzes crucial features to decide the most optimal algorithm. Furthermore, the results of this paper depict that using dimension reduction methods such as PCA and LDA increases the accuracy of some algorithms. Random Forest was able to outperforms with a higher accuracy of 99.89% with the proposed feature extraction methods.
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    PublicationOpen Access
    Real-Time Embedded System for Inattentive Driver Monitoring
    (SLIIT, 2022-02-11) Nalmi, R; Clerence, A.; Buddhika, P; Saranyan
    One of the causes of motor vehicle accidents in Sri Lanka is driver inattention or drowsiness. In the field of intelligent transportation systems, continuous research and development are conducted to address this contemporary issue. Many approaches, such as driver assistance and drowsiness detection systems, have been proposed to overcome this fatality. The purpose of this research was to implement a product that can maximise road safety while improving the transport sector's efficiency and reliability of the logistics chain to reinforce the country's economic growth. In this paper, the correlation between the preprocessed vehicular parameters and visual features are used to analyse the driver state and make predictions of the driver's perfomance. The proposed system uses computer vision and fuzzy logic inference implemented on the singleboard computer Raspberry Pi to detect facial features and to determine the driver's drowsiness state, an ELM327 is used to read the vehicle parameters from the Electronic Control Unit (ECU) and motion sensors were used to obtain the steering angle. The data acquired is stored in a cloud platform using REST API. The database also contains driver details. The system uses a fingerprint scanner to identify the driver. An actuator was installed in the vehicle to alert the driver when the system detects inattentiveness. Overall the proposed project provided satisfying experimental results. It can be used as a solution to improve road safety and a supporting tool for the logistics sector to monitor vehicles and driver performance.
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    PublicationEmbargo
    Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals
    (IEEE, 2021-04-13) Mampitiya, L. I; Nalmi, R; Rathnayake, N
    This study is based on EEG brain wave classification of a well-known dataset called the EEG Brainwave Dataset. The dataset combines three classes such as positive, negative, and neutral. The classification is performed using an ensemble classifier that combines RF, KNN, DT, SVM, NB, and LR. The meta classifier is LR, while the other five algorithms work as the base classifiers. Furthermore, PCA is used as the dimension reduction method to increase the accuracy of the final output. The results are evaluated under 11 different parameters. Moreover, the accuracy of this study is compared with the seven other EEG emotion classification methods. The proposing method attained 99.25% of accuracy, outperforming the other state-of-the-art algorithms.