Browsing by Author "Madushan, K"

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Open Access
IOT Based Smart Microgreen Sprouter
(Springer, Cham, 2022-01) Rankothge, V; Kehelella, P; Perera, D; Kanchana, B. C; Peiris, R; Madushan, K
This research paper focuses on enhancing indoor farming technologies with an emerging technology: Internet of Things (IoT). The proposed approach creates a microgreen sprouter unit that automates the process of monitoring and providing optimum growing conditions with the minimum human supervision. This model can be used to achieve healthy growth of microgreen by providing ideal ventilation, moisture, humidity, light, and temperature levels, which prevents sprouts from ultraviolet radiation and pest attacks. Users can track the growth rate of sprouts and change the moisture, humidity, light and temperature levels. Our prototype implementation has been tested for mung-beans sprouts and validated for its accuracy and efficiency
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Review On Hand Gesture Recognition for Bengali Sign Language
(IEEE, 2022-02-23) Perera, D; Kanchana, B. M; Peiris, R; Madushan, K; Kasthurirathna, D
Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.
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Review On Hand Gesture Recognition for Bengali Sign Language
(IEEE, 2022-04-14) Perera, D; Kanchana, B; Peiris, R; Madushan, K; Kasthurirathna, D
Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.