Air pollution is one of the major environmental issues discussed lately due to its influence on human health. Particular attention is paid to air quality monitoring today and most developed societies have implemented ...
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Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often stru...
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Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using Locality-Constrained Linear Coding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based act
Modelling & Simulation (M&S) is broadly used in real scenarios where making physical modifications could be highly expensive. With the so-called Simulation Software-as-a-Service (SimSaaS), researchers could ta...
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ISBN:
(纸本)9789897581205
Modelling & Simulation (M&S) is broadly used in real scenarios where making physical modifications could be highly expensive. With the so-called Simulation Software-as-a-Service (SimSaaS), researchers could take advantage of the huge amount of resource that cloud computing provides. Even so, studying and analysing a problem through simulation may need several simulation tools, hence raising interoperability issues. Having this in mind, IEEE developed a standard for interoperability among simulators named High Level Architecture (HLA). Moreover, the multi-agent system approach has become recognised as a convenient approach for modelling and simulating complex systems. Despite all the recent works and acceptance of these technologies, there is still a great lack of work regarding synergies among them. This paper shows by means of a literature review this lack of work or, in other words, the sparse Cloud SimSaaS. The literature review and the resulting taxonomy are the main contributions of this paper, as they provide a research agenda illustrating future research opportunities and trends.
This paper investigates the formation tracking control of unmanned surface vehicles (USVs) swarm based on prescribed performance control and the event-triggered mechanism. A prescribed performance controller is design...
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Functionalized two-dimensional(2D)materials play an important role in both fundamental sciences and practical *** construction and precise control of patterns at the atomic-scale are necessary for selective and multip...
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Functionalized two-dimensional(2D)materials play an important role in both fundamental sciences and practical *** construction and precise control of patterns at the atomic-scale are necessary for selective and multiple *** we report the fabrication of monolayer pentasilver diselenide(Ag_(5)Se_(2)),a new type of intrinsically patterned 2D material,by direct selenization of a Ag(111)*** atomic arrangement is determined by a combination of scanning tunneling microscopy(STM),low-energy electron diffraction(LEED),and density-functional-theory(DFT)***-scale STM images exhibit a quasi-periodic pattern of stoichiometric triangular domains with a side length of~15 nm and apical *** boundaries between triangular domains are *** of different molecules on the patterned Ag_(5)Se_(2) exhibits selective adsorption *** molecules preferentially adsorb on the boundaries,while tetracyanoquinodimethane(TCNQ)molecules adsorb both on the boundaries and the triangular *** co-depositing pentacene and TCNQ molecules,we successfully construct molecular corrals with pentacene on the boundaries encircling TCNQ molecules on the triangular *** realization of epitaxial large-scale and high-quality,monolayer Ag_(5)Se_(2) extends the family of intrinsically patterned 2D materials and provides a paradigm for dual functionalization of 2D materials.
Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has be...
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Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks enge...
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Due to a limited coverage of the existing bilingual dictionary, it is often difficult to translate the Out-Of-Vocabulary terms (OOV) in many natural language processing tasks. In this paper, we propose a general casca...
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In recent times, road causalities caused by driving cars have increased gradually. These accidents result in severe injuries and deaths, evolving as a serious social and traffic issue. As a matter of fact, fatigue and...
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Support Vector Machine (SVM) is on one of its kind of data mining algorithm, tends to always give away the global optimum solution to any given problem because of its convex optimization problem solving approach. SVM ...
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