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Autonomous mobile robots path planning with integrative edge cloud-based ant colony optimization
Full text linkIn recent years, Automated Mobile Robots (AMRs) have gained significant attention in industry and research applications, requiring efficient path-planning algorithms to optimize task performance. While widely adopted,
conventional Ant Colony Optimization (ACO) algorithms suffer from low convergence rates and delays in task execution, particularly in dynamic environments due to insufficient exploration of this context. However, traditional Ant Colony Optimization (ACO) algorithms, widely used for AMR path planning, exhibit limitations such as low convergence rates and redundant recalculations, particularly in environments with frequently changing obstacles. To address these challenges, this study proposes an Integrative Edge Cloud-Based Ant Colony Optimization (IECACO) algorithm. IECACO incorporates a novel path retrieval mechanism and
edge cloud computing infrastructure to minimize redundant path computation and improve convergence efficiency. The proposed algorithm is tested within a simulated 2D occupancy grid environment using both a 4×4 map for controlled experiments and a 20×20 map for comparative
evaluation against a prior Improved ACO (IACO) study. Experimental simulation results, based on 50 independent runs in settings, demonstrate that IECACO achieves at least 4.76% reduction compared to traditional ACO. Based on the observation of 10 independent runs between IECACO
and IACO, IECACO leading a significant reduction in both static and dynamic settings. Although this study is conducted in a simulated environment, the findings lay a foundation for future real-world implementations
Stochastic geometry analysis of reconfigurable intelligent surface-assisted millimeter-wave energy harvesting networks
Full text linkEnergy harvesting (EH) in millimeter-wave (mm-wave) cellular networks has gained significant attention due to the widespread use of large antenna arrays and dense base station (BS) deployments. However, mm-wave signals are highly susceptible to path attenuation caused by significant
atmospheric and obstacle-induced absorption, which can limit coverage and degrade the performance of EH systems due to high path losses. This paper considers the use of reconfigurable intelligent surface (RIS)-assisted mm-wave networks as a solution to enhance EH performance. We propose an analytical framework based on stochastic geometry to evaluate the energy coverage probability (ECP) performance of user equipment (UE) in these networks, deriving a closed-form expression for the ECP. The analytical
formula for average harvested energy (AHE) is also provided to help characterize system performance. The findings show that deploying RISs can significantly improve EH performance in mm-wave networks, even in challenging urban areas with significant path loss. The findings also show that dense deployment of passive RISs significantly improves EH comparable to active BS deployment. Furthermore, the findings indicate that adding passive reflectors is as effective as equipping the BS with additional active antenna elements to enhance ECP and AHE. This study provides valuable insights for designing future EH strategies tailored to end-UEs in RIS-assisted mm-wave networks, emphasizing the efficacy of RISs in improving network performance and EH capabilities
A rigorous examination of electromyography forearm muscle response in grasping and swinging scenarios
Full text linkThis study examines the use of electromyography (EMG) in analyzing forearm muscle responses in hand grasping force with swinging motions. We start by establishing the basics of hand grasping force and swinging motions, laying the groundwork for subsequent discussions. The paper critically assesses various EMG techniques, highlighting how they reveal muscle activity during hand grasping in dynamic situations. We explore how swinging motions affect hand grasping force biomechanics, emphasizing the role of EMG in capturing dynamic muscle activity. A thorough examination of methodologies used in EMG studies provides insights into current practices and emerging trends. Practical applications across fields like rehabilitation and robotics underscore the relevance of this research. The study concludes by addressing current challenges and suggesting future research directions. This synthesis provides a straightforward resource for researchers, practitioners, and technologists seeking a deeper understanding of EMG indices in hand-grasping force analysis with swinging action
A regression model of hip flexion force of the dominant leg among Malaysian adults in standing posture
Full text linkIntroduction: The disregard for hip flexion force when designing foot-operated equipment poses a potential threat to non-compliance with ergonomics principles, ultimately impacting occupational health. Nevertheless, there is a noticeable lack of studies focusing on the hip flexion strength of Malaysian adults in a standing position. This paper aimed to measure the maximum force of hip flexion strength and formulate a regression model for Malaysian young adults in a standing posture. Materials and methods: The experiment invited sixty Malaysian adults aged 20 to 26 years old. A digital force gauge (Mark-10, USA) was used to measure the hip flexion force. A regression model was developed to determine the influence of gender, body mass, body height, thigh length, and thigh circumference on the hip flexion force. Results: The results of this study found that the means of hip flexion force for the male and female participants were 192.8 N and 126.0 N, respectively. The regression model concluded that gender is the most significant factor influencing hip flexion force (p0.05). Conclusion: This study concluded that the relationship between anthropometric parameters and hip flexion force is not always straightforward and can be influenced by various factors. To gain a more comprehensive picture of hip flexion, it is essential to consider other potential factors such as muscle mass, neuromuscular control, and joint mechanics
Impact of geometries on performances and surface morphology of SLS 3D-printed thrust and roller bearings
Full text linkBearings are among the most prevalent elements in civil engineering buildings and mechanical machinery, with numerous applications. The global bearings market has experienced considerable growth in recent years, fuelled by rising demand in the automotive, aerospace, and manufacturing sectors. Bearings were used as early as 40 Before Christ (BC) and commonly have a solid geometrical design. Lately, there have been limited studies to predict the effects of different geometries on the behaviour of bearings. In this study, different geometrical models were designed using CatiaV5 software and manufactured using selective laser sintering (SLS) three-dimensional (3D) printing. The printed geometrical bearing samples were subjected to vibration analysis, performance testing, and surface validation using a scanning electron microscope (SEM) to evaluate their tribological behaviour. The findings indicated that the samples with a triangular geometry exhibited a remarkably smooth surface texture. This smoothness surpassed that of the samples with a square geometry, and this was attributed to the shorter spacing between the melted particles, extensive coverage of the particle area and reduced presence of independent particles. These findings highlight the intricate interplay between geometry and surface texture in bearing fabrication, offering valuable insights for further research and development
A systematic review of web-based learning in enhancing visualization skill
Full text linkIn the digital era, visualization skills are critical for academic and professional success. This systematic review examines how web-based learning enhances these skills across
various disciplines. This study focuses on addressing the challenges of developing effective visualization capabilities and the role of web-based learning in providing innovative solutions. This research utilized the PRISMA methodology to identify primary data utilizing specific keywords. Through extensive searches on Scopus, Web of Science, and ProQuest, 40 relevant studies were identified. The findings are organized into three themes: (1) Learning Innovations, (2) Digital Visual Education, and (3) Creative Visual Pedagogy. Results highlight web-based learning as a pivotal strategy
in applied science and technology education, emphasizing collaborative and interactive technologies in enhancing spatial visualization and contributing to advanced
educational practices
Design and measurement of a tiny wideband antenna for deeply embedded biomedical devices
Full text linkThe increasing demand for compact and efficient implantable medical devices has driven the development of advanced antenna solutions for biomedical applications. This study presents a novel wideband implantable antenna specifically tailored for scalp implantation, operating across two critical frequency ranges: the Industrial, Scientific, and Medical (ISM) band (2.4–2.48 GHz) and the midfield frequency range (1.45–1.6 GHz). The antenna’s compact design, with overall dimensions of 3 × 4 × 0.5 mm3, features a 0.25 mm thick dielectric layer constructed from Rogers 4350B (εr= 3.66,
tanδ = 0.0031)for both the substrate and superstrate. Innovative design elements, including openended slots in the radiating patch and closed-ended slots in the ground plane, contribute to its compact size, enhanced impedance matching, and improved bandwidth performance. The antenna achieves a peak gain of −19.92 dBi at 2.45 GHz and delivers an ultra-wide bandwidth of 1836.8 MHz, spanning from 1.0602 GHz to 2.8970 GHz. These characteristics ensure reliable operation in diverse implantation scenarios within the human body, while adhering to IEEE C95.1-2005 safety standards for specific absorption rate (SAR) compliance. Comprehensive performance evaluations were conducted using finite-element simulations in homogeneous tissue environments, employing HFSS and CST software. The simulated results aligned closely with experimental measurements, validating the design’s accuracy and manufacturability. Additionally, a link budget analysis confirmed the antenna’s ability to maintain a robust and reliable wireless telemetric connection, demonstrating its
suitability for medical applications and ensuring safe, efficient communication
A new 13N-complexity memory built-in self-test algorithm to balance static random access memory static fault coverage and test time
Full text linkAs memories dominate the system-on-chip (SoC), their quality significantly impacts the chip manufacturing yield. There is a growing need to reduce the chip production time and cost, which mainly depends on the testing phase. Hence, a memory built-in self-test (MBIST) utilizing a low-complexity, high-fault-coverage test algorithm is essential for efficient and thorough memory testing. The March AZ1 algorithm, with 13N complexity, was created earlier to balance the test length and fault coverage. However, poor positioning of a write operation in its test sequence caused the reduction of the transition coupling fault (CFtr) detection. This paper presents the creation of the March AZ algorithm, modified from the March AZ1 algorithm, to increase CFtr coverage while preserving the same complexity. It was accomplished by analyzing the fault coverage offered by the March AZ1 algorithm and then reorganizing its test sequence to address the limitation in detecting CFtr. The newly produced March AZ1 algorithm was successfully implemented in an MBIST controller. The simulation tests validated its functionality and demonstrated that the CFtr coverage was enhanced from 62.5% to 75%, achieving an overall fault coverage of 83.3%. Therefore, with 13N complexity, it offers the best fault coverage among all the existing test algorithms with a complexity below 18N
Enhanced multi-agent approaches for efficient evacuation and rescue operations in managing disasters
Full text linkThis study addresses disaster management within Multi-Agent System (MAS) environments, focusing on two critical phases: evacuation and rescue. The study tackles two primary challenges: the Emergency Route Planning (ERP) problem, which involves determining optimal evacuation routes within capacity-constrained transportation networks, and the Winner Determination Problem (WDP) in reverse combinatorial auctions, which pertains to effective task allocation and coordination among rescue agents. The research progresses through four stages: problem definition, approach design, implementation and evaluation, and simulation. For the evacuation phase, a Dynamic Real-Time Capacity Constrained Routing (DRTCCR) algorithm is introduced to address ERP challenges. The algorithm aims to generate optimal evacuation routes considering the complexity, capacity constraints, and scale of evacuees in the transportation network. Analytical evaluation against existing algorithms, specifically Multiple-Route Capacity Constrained Planner (MRCCP) and Max-Flow Rate Priority (MFRP), demonstrated that the DRTCCR significantly improves performance in terms of Total Evacuation Time (TET) and Weighted Average Time (WAT). Compared to MRCCP, DRTCCR reduced TET by 14.95% and WAT by 1.7%, while against MFRP, it decreased TET by 17.25% and WAT by 9.18%. In the rescue phase, two innovative approaches are proposed to enhance task allocation for WDP in reverse combinatorial auctions. These approaches were rigorously evaluated against Andrea’s algorithm and a Genetic Algorithm, revealing competitive advantages. Notably, as the number of bidders increased, the execution time of competing approaches escalated exponentially, while the proposed approaches exhibited a steady increase. Building on the proposed algorithm and approaches, Agent-Based Simulation (ABS) models were developed to evaluate both evacuation and rescue operations in Al-Aqsa Mosque (AM) scenarios in Palestine. The ABS evacuation model demonstrated superior performance compared to the Random, Kasereka, and Nearest Neighbor Search (NNS) models, achieving a 0% Total Deaths (TD) rate, outperforming Kasereka 1%, Random 5.5%, and NNS 14%. It also achieved a 99.5% Total Alive Evacuees (TA) rate, compared to 98.7% for Kasereka, 94.9% for Random, and 87.6% for NNS, along with an Average Health of Alive Agents (ATA) improvement of 52.4% over Kasereka, 82.1% over Random, and 93% over NNS. Similarly, the ABS rescue model outperformed both the Nearest Neighborhood Rescuing (NNR) model and the Hooshangi and Alesheikh model, reducing the duration of rescue operations by 49.2% compared to NNR and 32.6% compared to the Hooshangi and Alesheikh model, while also decreasing the number of casualties by 10.6% relative to NNR and 2.4% relative to the Hooshangi and Alesheikh model. These results highlight the model's significant improvements in both efficiency and effectiveness in managing evacuation and rescue scenarios
Comparative analysis of 1D – CNN, GRU, and LSTM for classifying step duration in elderly and adolescents using computer vision
No full textDeveloping a classification system that can predict the onset of neurodegenerative diseases or gait-related disorders in elders is vital for preventing incidents like falls. Early detection allows reduction in symptoms and treatment cost for the elderly. In this study, step duration data from five healthy adolescents with age range of 23 – 29 years old and five healthy elderly individuals with age range of 71 – 77 years old were sourced from PhysioNet. To ensure proper training of the deep learning models, synthetic data was generated from the original dataset using a noise jittering technique with random noise of a range between -0.01 and 0.01 added to the original data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (1D-CNN) are used for training the data since the data is available in the form time series data. LSTM and GRU are advanced forms of Recurrent Neural Network (RNN) while 1D – CNN can capture temporal dependencies in sequential data. 1D – CNN has the advantages over GRU and LSTM of being more robust to noise and can capture complex patterns behind the data. These methods will be compared in terms of processing time and accuracy. Results show that 1D – CNN outperforms both LSTM and GRU with accuracy of 1.000 in less than 60 seconds. The novelty and contribution of this research shows that healthy old people and healthy young people can be classified with deep learning. Further direction of the research can explore the deep learning in classification of Parkinson’s disease