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714 research outputs found
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Development of an Autonomous Vacuum Cleaner Robot: CASARICA
No full textThis article introduces CASARICA, an autonomous vacuum robot whose aim is to counteract the long process of manual household cleaning, particularly for Indonesia's urban regions. With the use of an Arduino Mega 2560 microcontroller, CASARICA pairs ultrasonic, infrared, and dust sensors to independently move around and effectively sweep away dirt. Powered by a 5000mAh power bank, it works for 8 hours, employing a roller brush and DC motors to suck in dust in an onboard waste bin. Even when in the process of testing, it has been known to survive environments both with smooth ceramics or wooden floor, navigate through obstacles, and find high-dust areas with little user effort. Tests showed strong performance in battery life, mobility, and vacuuming which makes CASARICA to be a low-cost option for easier and automated cleaning.
Manuscript received: 15 Jun 2025 | Revised: 29 Jul 2025 | Accepted: 10 Aug 2025 | Published: 30 Nov 202
Deep Learning-based Obstacle Detection for Human Interaction Robots: A Review
No full textObstacle detection is the foundation of autonomous robotics, enabling robots to perceive and understand the world around them to move safely. Deep learning has emerged as one of the driving forces in today’s research, with various algorithms employed for learning and making effective decisions based on vast and complex datasets. In recent years, numerous deep learning methods have been developed and studied to detect obstacles. This paper provides an end-to-end overview of over 40 state-of-the-art deep learning models (from 50 papers) for obstacle detection in human-interacting robots, with a focus on deployment viability, real-time running, and energy efficiency. We also delve into the architecture of deep learning, highlight key challenges in real-world deployment, offer a comparative analysis of basic and advanced deep learning approaches, and examine the trade-offs between accuracy, speed, and power consumption, providing insights into practical considerations. This review categorizes obstacle detection techniques into two groups: Core CNN-based methods and Advanced Deep Learning Methods. Comparisons were made between these two groups, concentrating on computational requirements, deployment feasibility, and hardware configuration. Several key findings emerged. It was determined that models with high accuracy were computationally expensive and unsuitable for embedded deployment. While some models experience accuracy-speed trade-offs, others are limited by hardware constraints and power limitations. Finally, this review concludes with a structured discussion of real-world deployment considerations, prioritizing model efficiency, scalability, and potential future research directions in deep learning-based obstacle detection.
Manuscript received: 30 Jun 2025 | Revised: 28 Jul 2025 | Accepted: 11 Aug 2025 | Published: 30 Nov 202
Development of an IoT-based Wireless Controlled Power Adapter
No full textEnergy waste issues in electrical appliances due to inefficient usage monitoring are commonly faced by almost every person. This project aims to develop a wireless controlled power adapter operates autonomously based on occupancy of a specific space. In this way, convenience is brought to the user, and energy waste could be prevented. This system provides two modes of operation: manual and automatic. Using the mobile phone user interface, the user can manually and wirelessly control the power adapter. When there are no occupants in the specific space, the system will automatically shut off the power adapter. In contrast, if a person is detected in a specific space, the power adapter will be automatically switched on. WIFI protocol is used for the entire communication system. Experimental demonstration has been conducted to show the functionality. This proposed system offers the user to control in a long-distance range as long as the system is connected to a random WIFI network.
Manuscript received: 27 Dec 2024 | Revised: 21 Jan 2025 | Accepted: 13 Feb 2025 | Published: 31 Mar 202
AI-Assisted Analysis for Breast Cancer Imaging and Diagnostics
No full textBreast cancer cases have increased by 0.5% each year. X-ray, CT-Scan, and magnetic resonance imaging have been used to detect cancer without harming the patient. However, these methods usually used manual screening to process medical images, which leads to longer processing time and increases the burden on the expert. With the help of deep learning, automation-driven breast cancer detection, segmentation, and explanation can be performed in the process, which can greatly reduce the processing time and the burden on experts. This paper proposed a deep learning model, S-YOLOv11 by combining YOLOv11 with SimAM attention mechanism and a GUI with integration of a large language model. The model is trained with 624 images and tested with 156 images. Several YOLO architectures were compared, including YOLOv8, YOLOv9, YOLOv10, and YOLOv11. The proposed model has outperformed the other models. In the detection task, 0.806 precision, 0.635 recall, and 0.724 mAP were achieved. In the segmentation task, 0.833 precision, 0.65 recall, and 0.739 mAP were achieved. In addition, the study also improved the functionality of the GUI by accessing the ChatGPT API. It is possible to generate medical analysis for breast cancer tumors, with the use of GUI for visualization. However, current research is still in the development stage and it needs to be put into clinical trials before it can be used.
Manuscript received: 3 Jan 2025 | Revised: 20 Feb 2025 | Accepted: 27 Feb 2025 | Published: 31 Mar 202
Sustainable development of rural education tourism – the case of Dashu Town, Chun’an County
No full textThis study explores sustainable development and the design of rural educational tourism products. The integration of rural educational tourism may achieve win-win outcomes. Nevertheless, the method of integrating these two to achieve sustainable development is yet to be fully understood. Dashu Town, a rural area known for elements of patriotism and natural resources, is a highlight of Chun’an government in developing its tourism industry. Twelve tourism products were observed in this study, and twenty respondents were interviewed. A total of 45 concepts of rural educational tourism emerged and were categorised under four main themes: patriotism, agricultural, life skills training, and rural culture and art. The values of these four themes were defined and illustrated in this paper. To achieve sustainable development, suggestions to enhance the supporting elements, integrate the services, and bridge the administrative gaps were illustrated
Exploring students’ perceptions of mock job interviews as career preparation: a qualitative inquiry
No full textThis study explores undergraduate students’ perceptions of the effectiveness of mock job interviews in preparing them for future careers. With the transition from academia to the professional world posing challenges, simulated interviews offer a practical strategy for bridging this gap. Conducted at a private university in Kuala Lumpur, Malaysia, this study employed a qualitative design using semi-structured interviews and reflective responses from 17 students. Guided by social constructivism and the competence-based learning framework (CBLF), thematic revealed that mock interviews enhanced students’ confidence, communication skills, and understanding of professional expectations. Participants valued the constructive feedback they received, noting its role in fostering both personal and professional growth. However, some students expressed concerns about the lack of realism in the simulations and called for more structured support to apply these experiences to actual job interviews. The findings offer insights for educators by highlighting the importance of incorporating mock interviews into higher education curricula. Doing so can better equip students with the competencies and confidence needed for future employment. The study concludes with practical recommendations for improving the design and delivery of mock interview programs to ensure that they prepare students for real-world career challenges more effectivel
Modelling of Water Droplet Wettability on Anti-wetting Sheet of Sand/HDPE
No full textHigh-density polyethene (HDPE) is widely used in personal protective equipment due to its distinctive mechanical and physical properties. In medical applications, its hydrophobic properties are precious for repelling body fluids and maintaining cleanliness. For instance, HDPE is used in coveralls, which require a durable material that protects against liquids and droplets. To further improve HDPE's performance, fillers can be added to enhance its mechanical and surface properties. One important characteristic to investigate is its wettability, which affects how fluids interact with the surface. In this study, computational simulations were conducted to assess the wettability of HDPE by measuring contact angles. These results were validated against published experimental data. The findings show that the contact angles from the simulations closely matched the experimental values, indicating that the computational method can successfully predict the wettability of HDPE and HDPE reinforced with sand fillers without extensive experimentation.
Manuscript Received: 5 January 2025, Accepted: 13 February 2025, Published: 15 March 2025, ORCiD: 0000-0002-9700-071
Independently Identifying Noise Clusters in 2D LiDAR Scanning with Clustering Algorithms
No full textLight Detection and Ranging (LiDAR) refers to a range imaging method for distance objects based on the principle of laser ranging. LiDAR environmental mapping technology is often highly praised for its precise mapping information with intricate features for various detection or tracking based applications. The research proposes a novel method for independently identifying and filtering noise clusters in 2-Dimensional (2D) LiDAR scans based on 2 distinct clustering algorithms of K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Results show DBSCAN to be the better choice as it is more robust and resistance to noise and outliers in the dataset and is capable of identifying clusters of any shape making it more versatile. Furthermore, to address the issue of dead zones present in LiDAR scanning, an innovative solution based on interpolating the discontinuous spatial results of the LiDAR scanning result to further reconstruct a 3-Dimensional (3D) viewing model by stacking multiple copies of 2D LiDAR scanning results with varying elevation is demonstrated by the results of the study to be a viable economical alternative for 3D LiDAR mapping.
Manuscript Received: 1 August 2024, Accepted: 11 October 2024, Published: 15 March 2025, ORCiD: 0000-0003-4121-733
Site Investigations on the Lateral Movement of Skewed Prestressed Concrete Sleepers (PCS) Subjected to Rail Dynamic Loading
No full textPrestressed concrete sleepers (PCS) are crucial for preserving track stability under dynamic train loads. This study compares the dynamic load behavior of a skewed PCS (placed at a minor angle with one rail seat spacing of 0.67 m instead of the conventional 0.70 m) to a non-skewed PCS. Vibration measurements were taken on an operational Malaysian railway line during commuter train passages (six-coach trains traveling at 30-40 km/h) at a curve track. The Integrated Electronic Piezo-Electric (IEPE) sensor installed on the edges of PCS recorded accelerations in three axes, which were then converted to movement (mm). The lateral movement derived from the skewed sleeper is compared against that from an adjacent normal sleeper. The results show that under identical strain, the skewed PCS has larger lateral movements than the non-skewed PCS. This larger movement indicates that lateral movement (skew) has a stronger influence on load distribution and track compliance. The findings shed light on how non-uniform sleeper placement influences dynamic track response, contributing essential data that is useful for railway maintenance and design in ensuring safety and performance on tracks with uneven sleeper alignment
Achieving High Performance in Silicone Rubber Dielectric Elastomers via Synergistic Layer System
No full textPolydimethylsiloxane (PDMS) elastomers are attractive for soft actuation but their intrinsically low permittivity demands high electric fields and promotes premature electrical failure. To address this limitation, this study implements a Synergistic Layer System (SLS) in which a hard filler (TiO2) and soft fillers (high-permittivity silicone oil (HPSO) and glycerol (Gly)) are co-embedded in PDMS to raise dielectric response while moderating stiffness. A commercial PDMS A/B (1:1) pre-blend was formulated as single-filler films (TiO2, HPSO, Gly; 15 wt%) and SLS hybrids (TiO2 + Gly and TiO2 + HPSO; 1:1, total 15 wt%), then degassed, cast in glass Petri dishes, and oven-cured at 80 °C (~ 40–80 µm). Mechanical properties (Young’s modulus, tensile strength, elongation at break) were obtained by quasi-static uniaxial tensile testing on a universal testing machine. Breakdown strength followed IEC 60243-1/-2 using a step-up high-voltage setup with semi-spherical electrodes. Relative permittivity (Er) was measured on an impedance analyzer (20 Hz – 30 MHz) using carbon-grease circular electrodes. Relative to pure PDMS, single-filler films improved either dielectric response or compliance but introduced clear trade-offs. In contrast, the SLS hybrids delivered balanced gains- TiO2 + Gly increased Er while tempering stiffness and TiO2 + HPSO provided the most balanced combination of Er, modulus, and breakdown strength. These results show that co-embedding a hard filler with a soft filler in a single layer complements interfacial polarization and plasticization, enabling higher-performance PDMS actuators without excessive stiffness