Faculty of Computing

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Subject: search.filters.subject.Autonomous

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Now showing 1 - 5 of 5
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    Autonomous Hydroponic Environment with Live Remote Consulting System for Strawberry Farming
    (IEEE, 2022-12-09) Samaranayake, S.L; Krishmal, S; Cooray, P.L.R.K; Senatilaka, T; Rajapaksha, S; Nuwanthika, W.S
    Strawberries are a very popular fruit and are widely consumed all over the world. Due to its nutritional value, its consumption has increased tremendously in recent times. Strawberry, which has such high health and economic value, is grown in only one area in Sri Lanka. This is since the climate in those areas is favorable for strawberries. Using the Internet of Things, image processing, and machine learning, this research proposed a design for a closed environment with automatic monitoring and controlling of environmental factors and nutrition required for strawberry cultivation with the capability of remote live monitoring and analysis of each plant. Also, the proposed system captures the images of each strawberry plant using a camera navigation system and analyses those images using a machine learning algorithm to identify the growing stage. This decision making process was verified using strawberry pictures acquired from a strawberry farm. In addition, current capturing images can use in the next growth cycle to increase accuracy. The proposed system can be easily expanded by increasing the height of the tower and refrigeration power. Through this, strawberry cultivation can be expanded to all parts of Sri Lanka by overcoming climatic and geographical limitations.
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    E-Medic – Autonomous Drone for Healthcare System
    (IEEE, 2021-04-12) Abeygunawaradana, p.; Gamage, N; De Alwis, L; Ashan, S; Nilanka, C; Godamune, P
    This paper presents the implementation of a platform for the delivery of medicine using an autonomous drone. The platform consists of a Healthcare platform that connects doctors and patients and an autonomous drone that handles the delivery of medicine to the patients. This platform mainly contains several functionalities for the E-prescribing and delivery management of a drone. For patient management, the E-medic system uses a mobile application with facial recognition-based Authentication. Also, this platform is developed with separate web-applications to handle prescriptions, orders, and delivery management. Since this system uses an autonomous drone for deliveries, this platform also has functionalities to operate the delivery drone using a web application. the system uses REST APIs to operate the drone regardless of the distance to the ground station. The delivery drone can discover the shortest path to the destination and fly to that destination autonomously with the help of the computer vision-based obstacle avoidance system.
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    E-Medic–Autonomous Drone for Healthcare System
    (IEEE, 2021-02-19) Abeygunawaradana, P; Gamage, N; De Alwis, L; Ashan, S; Nilanka, S; Godamune, P
    This paper presents the implementation of a platform for the delivery of medicine using an autonomous drone. The platform consists of a Healthcare platform that connects doctors and patients and an autonomous drone that handles the delivery of medicine to the patients. This platform mainly contains several functionalities for the E-prescribing and delivery management of a drone. For patient management, the E-medic system uses a mobile application with facial recognition-based Authentication. Also, this platform is developed with separate web-applications to handle prescriptions, orders, and delivery management. Since this system uses an autonomous drone for deliveries, this platform also has functionalities to operate the delivery drone using a web application. the system uses REST APIs to operate the drone regardless of the distance to the ground station. The delivery drone can discover the shortest path to the destination and fly to that destination autonomously with the help of the computer vision-based obstacle avoidance system.
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    Autonomous Cyber AI for Anomaly Detection
    (IEEE, 2021-12-09) Madhuvantha, K. A. N; Hussain, M. H; De Silva, H. W. D. T; Liyanage, U. I. D; Rupasinghe, L; Liyanapathirana, C
    Since available signature-based Intrusion Detection systems (IDS) are lacking in performance to identify such cyber threats and defend against novel attacks. It does not have the ability to detect zero-day or advanced malicious activities. To address the issue with signature-based IDS, a possible solution is to adopt anomaly-based detections to identify the latest cyber threats including zero days. We initially focused on network intrusions. This research paper discusses detecting network anomalies using AI-based technologies such as machine learning (ML) and natural language processing (NLP). In the proposed solution, network traffic logs and HTTP traffic data are taken as inputs using a mechanism called beats. Once relevant data has been extracted from the captured traffic, it will be passed to the AI engine to conduct further analysis. Algorithms such as Word2vec, Convolution Neural Network (CNN), Artificial Neural networks (ANN), and autoencoders are used in order to conduct the threat analysis. HTTP DATASET CSIC 2010, that NSL-KDD, CICIDS are the benchmarking datasets used in parallel with the above algorithms in order to receive high accuracy in detection. The outputted data is integrated and visualized using the Kibana dashboard and blockchain model is implemented to maintain and handle all the data.
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    Autonomous cloud robotic system for smart agriculture
    (IEEE, 2019-07-03) Dharmasena, T; De Silva, R; Abhayasingha, N; Abeygunawardhana, P. W. K
    Agriculture sector occupies 25.9% of the world employment. The demand for food production is rapidly increasing with the increase of world population. Developing the existing agricultural infrastructure by incorporating modern technologies will help to match this increasing demand. This paper proposes a automated system to optimally control the climate and irrigation in a greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect the unhealthy plants using image processing. A fuzzy controller will control the heating and cooling system, irrigation system and humidifiers installed in the greenhouse based on the sensor readings. The mobile robot navigates through a predefined map of the greenhouse and collect soil samples to perform measurements while onboard sensors will collect the ambient climate data. A camera mounted on the mobile robot will capture the plant and detect unhealthy crops based on the colour and the texture of the leaves.