Annual Research Conference of SLIIT CITY UNI [ARCSCU]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4159

The Annual Research Conference of SLIIT City Uni (ARCSCU), organized by the academic departments of SLIIT City Uni, which provides a dynamic platform for undergraduate and postgraduate researchers, scholars, and professionals to share their work, engage in academic discourse, and foster innovation. With a focus on encouraging student participation, the conference features paper presentations, poster sessions, interactive workshops, and publication of selected research in conference proceedings

https://arcscu.sliitcityuni.lk

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Author: search.filters.author.Benorith, LHas files: Yes

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Smart Sorting and Grading Fruits based on Image Processing Techniques
    (SLIIT City UNI, 2025-07-08) Ahamed, A J S; Benorith, L
    This paper presents the design and implementation of an automated apple sorting system that integrates machine vision techniques with embedded control for real-time classification and sorting of apples. The system employs a Raspberry Pi 4 as the primary processing unit, using a YOLOv11 model for fruit detection and classification, while an Arduino Nano manages weight measurement via a load cell. Real-time images of apples on a conveyor belt are captured, processed, and classified into four categories: Good Red, Good Green, Bad Red, and Bad Green. Sorting mechanisms, including servos and actuate based on classification results, with an integrated LCD and cloudbased Google Sheets providing monitoring and logging. The system demonstrates high classification accuracy and reliable sorting performance, offering a cost-effective solution for small to mid-scale agricultural applications
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    PublicationOpen Access
    AI-Based Smart Traffic Management System for Emergency Vehicles
    (SLIIT City UNI, 2025-07-08) Amarasinghe, D P S V; Benorith, L
    Modern cities' main traffic congestion problem delays emergency vehicles like ambulances and firetrucks and police cars where every second counts. Fixed signal traditional traffic systems lack real-time adaptability, hence delays and risks are raised. This paper suggests an AI-driven smart traffic management system to priorities emergency vehicles and enhance general traffic flow by means of Raspberry Pi, YOLOv8, and OpenCV. Strategically positioned cameras provide video to a Raspberry Pi, which detects emergency vehicles by using OpenCV and YOLOv8. Dynamic control of traffic lights on detection helps to clear the path, so reducing response times and improving safety. The technology also maximizes road use and helps to ease traffic. For cities with limited infrastructure, using reasonably priced, open-source tools are scalable and ideal.
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    PublicationOpen Access
    Computer Vision Controlled Humanoid Robotic Arm
    (SLIIT City UNI, 2025-07-08) Firdouse, M S; Benorith, L
    This paper presents the design and implementation of a low-cost, vision-based gesture-controlled humanoid robotic arm that mimics human hand and wrist movements in real time. The system uses a USB webcam and MediaPipe for hand landmark detection, OpenCV for image processing, and a Raspberry Pi 4 to compute landmark vectors and control servo motors via a PCA9685 driver. Calibration modes were introduced for each joint to ensure accurate servo mapping. The solution supports full gesture-based manipulation of a five-fingered robotic hand, including wrist orientation, with minimal latency and no physical contact. The system provides a more intuitive and natural method for robotic arm control compared to traditional input devices and has potential applications in prosthetics, automation, and human-robot interaction.