International Journal of Autonomous Robotics and Intelligent Systems (IJARIS)
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International Journal of Autonomous Robotics and Intelligent Systems (IJARIS) is an international scholarly, refereed research journal, seeks to promote original, innovative research that pushes the boundaries of robotics and intelligent systems, across multiple applications and industries. The journal aims to highlight cutting-edge advancements in both theoretical, design, simulation and practical aspects, for scientists, engineers, researchers, practitioners/industries, students and educators.
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PublicationDesign of an autonomous pipe-cleaning robot for small-scale hydroponic systems(Penerbit UniMAP, 2025-06)Hydroponic farming offers a sustainable solution to food insecurity, especially in developing countries, by enabling high-yield crop production with minimal land and water usage. However, one major challenge in hydroponic systems is the frequent clogging of PVC pipes due to debris from growing media and the buildup of algae, which thrives under high humidity and limited sunlight. These blockages not only reduce system efficiency but also increase the risk of plant diseases that can compromise entire harvests. To address this issue, this research focuses on the design and development of a compact mobile robot capable of cleaning hydroponic pipes. The robot is designed using SolidWorks and simulated in Tinkercad, targeting a 4-inch (110 mm) diameter PVC pipe with a length of 3 feet. It incorporates the HC-SR04 ultrasonic sensor to detect obstacles and uses acrylic as the structural material. The performance of the robot was tested against three types of common pipe obstructions: small stones, sand and grass roots. Experimental results show that the robot can navigate through the hydroponic pipe and partially remove debris, although complete cleaning remains challenging due to space constraints and the complexity of internal pipe conditions. This work demonstrates a low-cost, automated solution for maintaining hydroponic systems and highlights areas for further optimization in design and control.
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PublicationImplementation of music emotion classification using deep learning(Penerbit UniMAP, 2025-06)Music plays a crucial role in shaping emotions and experiences, making its classification an important area of research with applications in therapy, recommendation systems, and affective computing. This study develops a deep learning-based system to classify music into three emotional categories: "Angry," "Happy," and "Sad." The dataset, consisting of 22 audio files collected from YouTube, was manually labelled, segmented into 30-second clips, and augmented using pitch shifting and time stretching to enhance diversity. Features were extracted using Mel-Frequency Cepstral Coefficients (MFCC) and spectral contrast to analyse the harmonic and timbral characteristics of the audio. Three deep learning models, CNN, CNN-LSTM, and CNN-GRU, were evaluated. CNN-GRU achieved the highest weighted accuracy of 99.10%, demonstrating superior performance. Future work includes adding more emotion categories, diversifying the dataset, exploring advanced architectures like transformers, optimising hyperparameters, implementing real-time applications, and conducting user studies to assess effectiveness. This research successfully developed and evaluated a music emotion classification system, contributing to advancements in the field.
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PublicationDesign and development of a smart portable water purification system(Penerbit UniMAP, 2025-06)Access to clean drinking water remains a critical challenge for individuals in remote, off-grid, or temporary living situations such as travelers, campers, and emergency responders. This project presents the design and development of a Smart Portable Water Purification System that addresses this need through a compact, solar-powered, and IoT-integrated solution. The system utilizes a multi-stage filtration process including sediment, pre-carbon, reverse osmosis, post-carbon, and silver carbon filters to effectively remove physical impurities, chemical contaminants, and biological hazards. Real-time water quality monitoring is achieved using TDS and pH sensors, while a Raspberry Pi Pico W microcontroller enables wireless control and monitoring via a custom-built graphical user interface accessible through a local Wi-Fi access point. Performance evaluations showed a significant improvement in water quality, with pH values normalized to safe ranges. Despite low to moderate filtration speeds and unoptimized data handling, the results validate the system's effectiveness and usability in field conditions. Recommendations for future enhancements include improving power efficiency, filtration speed, communication protocols, sensor integration, and mechanical design for better portability and environmental durability. The final prototype demonstrates a reliable, user-friendly, and sustainable approach to decentralized water purification.
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