Browsing by Author "Silva, B. N"

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Open Access
Development of Computer-Aided Semi-Automatic Diagnosis System for Chronic Post-Stroke Aphasia Classification with Temporal and Parietal Lesions: A Pilot Study
(Multidisciplinary Digital Publishing Institute, 2020-01) Silva, B. N; Khan, M; Wijesinghe, R. E; Thelijjagoda, S
Survivors of either a hemorrhagic or ischemic stroke tend to acquire aphasia and experience spontaneous recovery during the first six months. Nevertheless, a considerable number of patients sustain aphasia and require speech and language therapy to overcome the difficulties. As a preliminary study, this article aims to distinguish aphasia caused from a temporoparietal lesion. Typically, temporal and parietal lesions cause Wernicke’s aphasia and Anomic aphasia. Differential diagnosis between Anomic and Wernicke’s has become controversial and subjective due to the close resemblance of Wernicke’s to Anomic aphasia when recovering. Hence, this article proposes a clinical diagnosis system that incorporates normal coupling between the acoustic frequencies of speech signals and the language ability of temporoparietal aphasias to delineate classification boundary lines. The proposed inspection system is a hybrid scheme consisting of automated components, such as confrontation naming, repetition, and a manual component, such as comprehension. The study was conducted involving 30 participants clinically diagnosed with temporoparietal aphasias after a stroke and 30 participants who had experienced a stroke without aphasia. The plausibility of accurate classification of Wernicke’s and Anomic aphasia was confirmed using the distinctive acoustic frequency profiles of selected controls. Accuracy of the proposed system and algorithm was confirmed by comparing the obtained diagnosis with the conventional manual diagnosis. Though this preliminary work distinguishes between Anomic and Wernicke’s aphasia, we can claim that the developed algorithm-based inspection model could be a worthwhile solution towards objective classification of other aphasia types
Embargo
Environmental forensics of the X-press pearl disaster: Uncovering the internal micro-structural transformations in marine microplastics
(Elsevier B.V., 2025-07-15) Jayasekara, P.M; Abhishek, P; Kahatapitiya, N. S; Weerasinghe, M; Kahandawala, B. S; Silva, B. N; Wijenayake, U; Rajapaksha, A.U; Wijesinghe, R. E; Vithanage, M
The MV X-Press Pearl (XPP) maritime disaster on May 25, 2021, released approximately 75 billion microplastic (MP) nurdles into the Indian Ocean and degraded due to the elevated temperatures, a cocktail of chemicals, physical abrasions, and environmental factors. While degradation-induced surface-level chemical and morphological changes were well documented, internal degradation remains largely unexplored. This study highlights the utilization of high-resolution optical coherence tomography (OCT) as a purely non-destructive imaging modality to discover profound internal alterations in the micrometer range, such as internal hollow regions, cracks, and voids in MP nurdles subjected to different degrees of degradation. The dark pixel intensity probability density corresponds to the degraded areas, increased from 0.0019 (pristine nurdle) to 0.0135–0.5252 for thermal degradation, 0.0878–0.3134 for chemical degradation, and 0.1291–0.2179 for mechanical degradation, indicating progressive internal degradation. Attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy analysis confirmed that all the nurdles are polyethylene (PE) and revealed that extreme conditions lead to the formation of new functional groups, including hydroxyl bands and carbonyl bands, even though PE is highly resistant to degradation. The integration of high-resolution OCT imaging with FTIR analysis provides novel insights into the interconnection between micrometer-scale internal physical alterations and associated chemical modifications of MP nurdles resulting from environmental degradation. These findings highlight the potential of this OCT-FTIR integrated approach for advancing the understanding of MP degradation and its long-term environmental impacts.
Open Access
Optical Coherence Imaging Hybridized Deep Learning Framework for Automated Plant Bud Classification in Emasculation Processes: A Pilot Study
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-09-25) Tharaka, D; Withanage, A; Kahatapitiya, N. S; Abhayapala, R; Wijenayake, U; Wijethunge, A; Ravichandran, N. K; Silva, B. N; Jeon, M; Kim, J
A vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows.
Open Access
Real-Time Coordinate Estimation for SCARA Robots in PCB Repair Using Vision and Laser Triangulation
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-04-07) Sanjeewa, N; Wathudura, V. M; Kahatapitiya, N. S; Silva, B. N; Subasinghage, K.; Wijesinghe, R.E
The Printed Circuit Board (PCB) manufacturing industry is a rapidly expanding sector, fueled by advanced technologies and precision-oriented production processes. The placement of Surface-Mount Device (SMD) components in PCB assembly is efficiently automated using robots and design software-generated coordinate files; however, the PCB repair process remains significantly more complex and challenging. Repairing faulty PCBs, particularly replacing defective SMD components, requires high precision and significant manual expertise, making automated solutions both rare and difficult to implement. This study introduces a novel real-time machine vision-based coordinate estimation system designed for estimating the coordinates of SMD components during soldering or desoldering tasks. The system was specifically designed for Selective Compliance Articulated Robot Arm (SCARA) robots to overcome the challenges of repairing miniature PCB components. The proposed system integrates Image-Based Visual Servoing (IBVS) for precise X and Y coordinate estimation and a simplified laser triangulation method for Z-axis depth estimation. The system demonstrated accuracy rates of 98% for X and Y axes and 99% for the Z axis, coupled with high operational speed. The developed solution highlights the potential for automating PCB repair processes by enabling SCARA robots to execute precise picking and placement tasks. When equipped with a hot-air gun as the end-effector, the system could enable automated soldering and desoldering, effectively replacing faulty SMD components without human intervention. This advancement has the potential to bridge a critical gap in the PCB repair industry, improving efficiency and reducing dependence on manual expertise.