Technical University of Malaysia Malacca
Universiti Teknikal Malaysia Melaka (UTeM) RepositoryNot a member yet
10990 research outputs found
Sort by
A low-profile UWB monopole antenna and high-isolated UWB-MIMO antenna for wireless communications networks
Full text linkThis study proposes a space-efficient ultra-wideband (UWB) monopole antenna engineered for superior gain and performance. The innovative design, modeled and analyzed using HFSS software, involves etching the resonator onto one side of an affordable FR4 substrate. The manufactured antenna features an extended impedance bandwidth, achieved by incorporating ``E'' and ``inverted E'' shaped slots on the patch, an irregular hexagonal substrate structure, and a slotted partial ground plane. Covering a frequency range from 2.5 to 11.1 GHz, the patch achieves a maximum gain exceeding 7.9 dB and an efficiency of 98%. Parametric analyses based on numerical simulations evaluate the impact of design elements, such as slots on the resonator and ground plane, and cuts in the substrate. The excellent match between simulated and measured data verifies the antenna's performance across multi-band environments. The article concludes by introducing a second antenna, designed through the symmetrical integration of four prototypes of the suggested antenna. Mutual coupling between elements is reduced through the use of an orthogonal, four-directional staircase structure, and a defective ground is intentionally left unconnected. This new antenna covers an impedance spectrum from 2.42 to 12 GHz, with a gain of 12.77 dB, an efficiency of up to 98%, and a voltage standing wave ratio (VSWR) ranging between 1 and 2. Overall, the article emphasizes the design, optimization, and application of UWB antennas, highlighting their performance and suitability for various wireless communication scenarios
A comprehensive analysis of waterwheel technologies for pico hydropower: Evolution, performance, and optimization strategies
Full text linkSmall-scale hydropower systems, particularly pico hydro, are emerging as viable and sustainable renewable energy
solutions with significant potential for future power generation. These systems offer economic, social, and
environmental benefits, making them ideal for rural electrification, especially in regions with low-head and low-flow water resources. Besides, pico hydro primarily relies on the natural flow of water to generate electricity, requiring
minimal or zero water storage, thereby reducing environmental impact and preserving local ecosystems. This paper explores the design and development of an overshot waterwheel turbine specifically designed for pico hydro
applications in areas with minimal water resources. A comprehensive review of existing waterwheel technologies, such as undershot, breast shot, pitch back, and overshot, is conducted to understand their historical evolution, fundamental working principles, system designs, and key components. The performance and application of these waterwheel types are analysed, along with the challenges associated with their real-world operation. Additionally, this research addresses critical issues related to waterwheel efficiency and operational limitations, highlighting areas for further improvement and innovation. By comparing different waterwheel designs, the study provides insights into optimizing turbine performance and proposes recommendations for enhancing efficiency, reliability and sustainability in low-resource hydropower applications, particularly in remote or off-grid areas with limited infrastructure access
Investigation of graphene dopant concentration on europium oxide thick film using screen-printed method for carbon dioxide gas sensing
Full text linkThe development of gas sensing devices that operate effectively at room temperature is crucial for improving environmental monitoring systems, particularly the sensitive detection of carbon dioxide (CO2). Europium oxide (Eu2O3) has potential as a sensing material but lacks sensitivity, stability, and response time at room temperature, making it unsuitable for real-world application. The objective of this research is to improve CO2 detection capabilities under ambient conditions by systematically incorporating graphene dopants into Eu2O3 thick films. In addition to an undoped Eu2O3 gas sensor, thick film sensors with different graphene concentrations of 0.1%, 0.5%, 1%, 2%, and 5% by weight were fabricated using the screen-printing method on Kapton substrates. The gas sensors were characterised using Field Emission Scanning Electron Microscopy (FESEM) for morphological assessment, Energy Dispersive X-ray (EDX) for compositional analysis, Raman spectroscopy for structural evaluation, and X-ray Diffraction (XRD) for crystallographic analysis. Their performance was evaluated in a controlled laboratory environment, with CO2 detection carried out at concentrations of 30, 50, and 70 sccm at room temperature. The aim of this study was to determine the optimum graphene concentration that maximises sensor response time, recovery characteristics, detection sensitivity, repeatability, hysteresis, and stability. Based on the experimental results, the 2% Eu2O3/Gr gas sensor exhibited the best performance, with a low resistance of 0.0874 GΩ and enhanced sensitivity towards CO2 at concentrations of 30, 50, and 70 sccm, with values of 2.40, 2.37, and 2.34, respectively. The 2% Eu2O3/Gr sensor demonstrated a 2.1-fold gain in sensitivity (26.50 pA/sccm), a 4.5-fold improvement in resolution, and a 2.2-fold decrease in standard deviation, along with a linearity of 98.04% compared to the undoped Eu2O3 sensors. Graphene’s large surface area and high conductivity facilitate CO2 adsorption and charge transfer between Eu2O3 and CO₂ molecules, resulting in enhanced production of carbonate species through redox reactions with Eu3+ ions. The ideal graphene doping level was found to be 2%, which maintained the structural integrity of the Eu2O3 gas sensors while increasing conductivity. In summary, this research demonstrates that graphene-doped Eu2O3 thick films offer a viable approach for room-temperature CO2 gas detection, with enhanced stability, sensitivity, and response times. Further research into graphene concentration and fabrication methods may provide deeper insight into the relationship between dopant concentration and sensing performance, supporting the development of effective CO2 sensors for industrial and environmental applications
Transformer-based sentiment analysis classification in natural language processing for Bahasa Melayu
Full text linkSentiment analysis in Bahasa Melayu leverages Natural Language Processing (NLP) to interpret opinions and emotional tone expressed in Malay texts. This research investigates the application of transformer-based deep learning models, Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, BERT-multilingual, ALBERT, and BERT-CNN, for sentiment classification into positive, negative, and neutral categories. The study addresses challenges in Bahasa Melayu sentiment analysis, including limited annotated resources, linguistic nuances, and common mixed-language usage on platforms like social media.To train and evaluate the models, a large-scale Malay dataset (Malaya dataset) was used. Pretrained models from HuggingFace were fine-tuned using 10-fold cross-validation to improve generalization. Optimization methods such as data augmentation were also implemented. The evaluation considered not just accuracy but also precision, recall, F1 score, and computational efficiency. Among the models, BERT-CNN achieved the best performance, with 96.30% accuracy and consistently high scores across all sentiment classes. BERT also performed well, especially for neutral sentiment, reaching 89.5% accuracy but showed slightly lower recall in the positive class. DistilBERT offered competitive performance (88.96% accuracy) while being faster and more lightweight, making it suitable for deployment in resource-limited environments. BERT-multilingual showed balanced results with a peak accuracy of 89.84%, and ALBERT, despite having fewer parameters, reached 88.76% accuracy but underperformed in positive sentiment recall. The results demonstrate that transformer-based models outperform traditional machine learning and lexicon-based approaches, particularly in handling informal, mixed-language Malay text. The proposed models can support real-world applications such as analyzing consumer sentiment, public opinion, or social response to policies. This study contributes to advancing sentiment analysis for low-resource languages by offering comparative insights and effective model configurations, setting a solid foundation for further research and practical deployment
Self-recoverable element failure correction in circular antenna array using artificial rabbit optimization for space applications
No full textPurpose – Phased array antennas utilized for different space applications, such as deep space vehicles, satellite
communication, telemetry tracking and control communication systems, may suffer from single or multiple
element failure because of the harsh space environment or long period operation of amplifiers present in them.
Design/methodology/approach – Phased array antennas are capable of generating highly directed signals, rapid beam scanning and high gain by using multiple antenna steering in a desirable direction. However, this could also lead to a higher probability of defective elements in the array that are difficult to replace if the application is distant or space-borne. Faulty elements in a circular antenna array (CAA) can rigorously distort the radiation pattern, which results in degrading the array efficacy by increased sidelobe levels (SLL), beam broadening and gain reduction.
Findings – In this research article, the issue of recovering the desired side lobe power pattern from the remaining
active elements can be achieved successfully by reoptimizing the array excitation parameters.
Research limitations/implications – While the results are promising, the study is limited to simulations and
theoretical modelling, which might not capture all real-world variables affecting space-borne antenna systems.
Future research should focus on empirical testing in actual space conditions to validate the effectiveness of the
artificial rabbits optimization (ARO) technique. Additionally, the study concentrates on a specific type of antenna array (CAA), and the generalizability of ARO to other array geometries like linear, rectangular and concentric circular arrays remains to be explored. Practical implementation may also reveal unforeseen challenges in the scalability and adaptability of the ARO technique in diverse operational scenarios.
Practical implications – The practical implications of this study are significant for the design and maintenance
of antenna systems in space environments. By implementing artificial rabbits optimization (ARO), engineers can enhance the fault tolerance of circular antenna arrays without necessitating physical replacements, thus reducing maintenance costs and extending the operational lifespan of space-borne equipment. ARO’s ability to adaptively optimize the array’s performance post-fault occurrence offers a reliable method for maintaining communication integrity in critical missions. This approach can be integrated into existing systems, providing a robust solution for optimizing performance amidst the challenging conditions of space operations.
Social implications – The application of artificial rabbits optimization (ARO) in maintaining the integrity of
space-borne communication systems has broader social implications. By ensuring reliable and uninterrupted
communication capabilities in space missions, ARO contributes to enhanced safety for spacecraft and satellites,
which is crucial for manned missions and high-stakes satellite operations. Furthermore, improved communication reliability supports better data transmission from space exploration, impacting scientific research and global communication networks. This technology could lead to more sustainable and cost-effective space missions, potentially making space more accessible and fostering greater international collaboration in space exploration and satellite deployment.
Originality/value – The mechanism of self-recoverable CAA with the prior knowledge of faulty elements can be attained effectively by implementing a novel metaheuristic artificial rabbits optimization (ARO) technique. This proposed approach also compares some of the state-of-the-art metaheuristics in element failure correction of faulty CAA
Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ
Full text linkThe occupancy rate of a hotel is an important factor to see the development of providers business performance. By forecasting occupancy rate, the hotel can identify business opportunities or adjust room prices, determine hotel operations, and take this into consideration for strategic decision making. In this study, occupancy rate forecasting for Hotel XYZ was carried out by comparing the k-nearest
neighbor (k-NN) and neural network methods. The dataset used in this study included rooms available, rooms sold out, and available occupancy percentage data in Hotel XYZ from April 2018 to June 2023. The simulation was carried out by dividing the data into training data and testing data with a ratio of 70:30 and 80:20. Model creation was carried out by applying the k-NN and neural network methods to the
Hotel XYZ data set. Forecasting results that were obtained using k-NN showed an optimal RMSE at 70%:30% split of data with an RMSE of 0.080 at k-value 3, while forecasting results obtained using the neural network showed an optimal RMSE at 70%:30% data split with an RMSE of 0.007 for two hidden layers. The comparison of results of forecasting by k-NN and neural network showed an optimal RMSE when using neural network method with an RMSE of 0.004, a GAP of 0.076 compared to using k-NN. The results of this study can be used by Hotel XYZ to make better decisions in determining hotel policies in the future and goals set by the hotel
Graphene-based terahertz antenna with enhanced backscatter sensitivity for early breast cancer localization
No full textTerahertz (THz) imaging is emerging as a promising technique for early breast cancer detection due to its high sensitivity to tissue property variations. This paper presents a compact graphene-based patch antenna designed for broadband operation, occupying only 18 µm × 23 µm. The antenna achieves a wide bandwidth of 5.9 THz (3.1–9 THz) and a peak gain of 6.47 dBi without tissue loading, enabled by a full ground plane that ensures unidirectional radiation. To evaluate its performance for cancer detection, numerical breast phantoms with and without tumors were modeled. A significant S11 deviation of up to 16 dB was observed between healthy and cancerous tissue, indicating strong sensitivity to dielectric changes. Additionally, tumor localization was achieved by analyzing the spatial variation of the backscattered signal along the X and Y axes. The results confirm that the proposed antenna can detect minute tumors (∼10 µm radius) and effectively differentiate between malignant and healthy tissues, highlighting its potential for early-stage breast cancer screening
Optimization of methane sensing response of a ZnO-graphene composite using the response surface method: sensing area and annealing temperature TEMPERATURE
Full text linkMethane traps heat 25 times more than carbon dioxide and is highlighted as the second most potent greenhouse gas, contributing to climate change. As the methane level grows, the impact on the Earth's climate becomes more severe, and exposure to high levels can lead to adverse consequences for the human health, causing symptoms like changes in breathing and heart rate, numbness, and death in case of prolonged and high exposure. To address these concerns, this study focuses on the optimization of a ZnO-graphene composite in gas sensors for methane sensing at room temperature using the response surface method (RSM). RSM was conducted using the Design Expert 13 software by optimizing two parameters: sensing area and annealing temperature. Ten samples of ZnO-graphene gas sensors were fabricated based on the sensing layer area (1 cm2–4 cm2) and annealing temperature (100–200 °C). The ZnO-graphene gas sensor was fabricated using a screen-printing technique on a Kapton film by applying silver paste (Ag) as the interdigitated electrode and ZnO-graphene as the sensing layer. The optimization using the RSM highlighted that the experimental model was significant with an R2 value of 0.8871. Results revealed that the sensing layer area has more influence on the gas sensor sensitivity than the annealing temperature. The optimized model showed that an area of 4 cm2 and an annealing temperature of 100 °C are the optimal parameters, with a sensitivity value of approximately 0.968167 × 10−3 for 5000–7000 ppm of methane
Non-verbal cues in interactive systems: Enhancing proactivity through winking and turning gestures
No full textThis investigation investigates the extent to which proactive behaviours in interactive objects—specifically animated eyes that exhibit behaviours such as blinking and turning—improve user interaction. Through a two-phase process, we investigate the influence of these behaviors on users’ perceptions of proactivity in both physical and virtual environments. In Phase I, we conducted a real-world study using a tangible box with animated eyes to evaluate user responses to expressive behaviours in single-and multi-person interactions. The results indicate that blinking significantly improves perceptions of the box’s intentionality and engagement, thereby fostering a more robust sense of proactivity. Phase II expands this investigation to a virtual environment, where 240 participants on Amazon Mechanical Turk (MTurk) participated, thereby validating the real-world findings. The online study confirms that perceived proactivity is consistently increased across contexts by blinking and turning. These findings indicate that integrating basic, human-like behaviors into interactive systems can enhance user engagement and provide practical advice for the development of sustainable, low-complexity interactive technologies. These discoveries facilitate the future development of resource-efficient and accessible human-computer interaction and robotic systems by simulating intentionality through minimal behavior
Low-profile four-port MIMO antenna realizing penta-band notches for UWB systems
No full textThis paper presents a low-profile, four-port Ultra-Wideband (UWB) Multiple-Input MultipleOutput (MIMO) antenna with integrated penta-band notch functionality to suppress interference from coexisting narrowband systems. The antenna employs a combination of mushroom-type
Electromagnetic Bandgap (EBG) structures and inverted L-shaped slots to realize five distinct notched bands at 3.5, 4.1, 5.0, 6.7, and 7.8 GHz, each exhibiting rejection levels better than − 4 dB. This effectively mitigates interference from WiMAX, INSAT, Wi-Fi (5 G), Wi-Fi-6E (6 G), and X-band uplink services. The four antenna elements are arranged face-to-face to minimize mutual coupling and ensure improved isolation. Notching is achieved using two square mushroom EBG structures, a spiral-slot EBG near the feedline, and three inverted L-shaped slots on the radiating surface. To enhance compactness further, a spiral-shaped defect is incorporated into one of the EBGs. Additionally, two square EBGs placed symmetrically beside the feedline enable the 5.5 GHz notch. The antenna occupies a compact footprint of 45.3 × 69.8 × 1.6 mm³ and demonstrates excellent diversity performance, with Envelope Correlation Coefficient (ECC) values below 0.02 and Diversity Gain (DG) near 10 dB. Performance validation includes frequency response, surface current analysis, ECC, DG, and radiation patterns, confirming the design’s suitability for compact UWB wireless devices requiring multi-band interference suppression