Journal of Advances in Engineering and Technology [JAET]

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The Journal of Advances in Engineering and Technology (JAET) is an international, open access, double blind peer-reviewed journal. It is published by the Faculty of Engineering of Sri Lanka Institute of Information Technology (SLIIT). The JAET aims at fostering research and development work in Engineering and Technology and bringing researchers on to a common platform. Furthermore, JAET will also accept review articles on appropriate subject areas including concept papers of academic opinions, book reviews, etc. for publication therein.

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Author: search.filters.author.Malasinghe, L

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    PublicationOpen Access
    Smart Health Monitoring System
    (SLIIT, Faculty of Engineering, 2024-03) Gajanayake, G. M. T. S.; Ekanayake, W. E. M. K. D. D; Malinda, G. D. C.; Malasinghe, L; De Silva, S
    Due to the high inpatient population in hospitals, regular monitoring of inpatients' vital signs is currently a practical concern. As a solution, our proposed system manages the continuous analysis of the vital signs of every inpatient in the general wards, and informs medical professionals in any location at any time about their inpatients' current states in real-time to improve inpatients' health. The suggested system consists of the following arrangements; arrangement for acquiring health readings, identifying the on-duty reported doctors in charge of wards, arrangement for health data exhibiting unit, fall detection, and ECG acquisition. In addition to these arrangements, a website, and an android mobile application were designed to publish measured inpatient vital signs. This proposed product is both novel and different from the existent products because, it comprises of collective arrangements, and is developed in order to assess hospital wards’ inpatients, whereas other systems are designed for remote health monitoring of patients at home. This paper describes the system that was developed and tested successfully.
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    PublicationOpen Access
    Machine Learning-Based Indoor Localization System with Human- Computer Interaction System
    (SLIIT, Faculty of Engineering, 2023-10) Jayasundara, A; Malasinghe, L
    Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods.