Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, tra...
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Accurately estimating of Retransmission TimeOut (RTO) in Content-Centric Networking (CCN) is crucial for efficient rate control in end nodes and effective interface ranking in intermediate routers. Toward this end, th...
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Accurately estimating of Retransmission TimeOut (RTO) in Content-Centric Networking (CCN) is crucial for efficient rate control in end nodes and effective interface ranking in intermediate routers. Toward this end, the Jacobson algorithm, which is an Exponentially Weighted Moving Average (EWMA) on the Round Trip Time (RTT) of previous packets, is a promising scheme. Assigning the lower bound to RTO, determining how an EWMA rapidly adapts to changes, and setting the multiplier of variance RTT have the most impact on the accuracy of this estimator for which several evaluations have been performed to set them in Transmission Control Protocol/Internet Protocol (TCP/IP) networks. However, the performance of this estimator in CCN has not been explored yet, despite CCN having a significant architectural difference with TCP/IP networks. In this study, two new metrics for assessing the performance of RTO estimators in CCN are defined and the performance of the Jacobson algorithm in CCN is evaluated. This evaluation is performed by varying the minimum RTO, EWMA parameters, and multiplier of variance RTT against different content popularity distribution gains. The obtained results are used to reconsider the Jacobson algorithm for accurately estimating RTO in CCN. Comparing the performance of the reconsidered Jacobson estimator with the existing solutions shows that it can estimate RTO simply and more accurately without any additional information or computation overhead.
Purpose - This project work shows a literature survey, clearly defines the mass growth factor, shows a mass growth iteration, and derives an equation for a direct calculation of the factor (without iteration). Definit...
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ISBN:
(纸本)9781713898436
Purpose - This project work shows a literature survey, clearly defines the mass growth factor, shows a mass growth iteration, and derives an equation for a direct calculation of the factor (without iteration). Definite values of the factor seem to be missing in literature. To change this, mass growth factors are being calculated for as many of the prominent passenger aircraft as to cover 90% of the passenger aircraft flying today. The dependence of the mass gain factor on requirements and technology is examined and the relation to Direct Operating Costs (DOC) is pointed out. Methodology - Calculations start from first principles. Publically available data is used to calculate a list of mass growth factors for many passenger aircraft. Using equations and the resulting relationships, new knowledge and dependencies are gained. Findings - The mass growth factor is larger for aircraft with larger operating empty mass ratio, smaller payload ratio, larger specific fuel consumption (SFC), and smaller glide ratio. The mass growth factor increases much with increasing range. The factor depends on an increase in the fixed mass, so this is the same for the payload and empty mass. The mass growth factor for subsonic passenger aircraft is on average 4.2, for narrow body aircraft 3.9 and for wide body aircraft (that tend to fly longer distance) 4.9. In contrast supersonic passenger aircraft show a factor of about 14. Practical implications - The mass growth factor has been revisited in order to fully embrace the concept of mass growth and may lead to a better general understanding of aircraft design. Social implications - A detailed discussion of flight and aircraft costs as well as aircraft development requires detailed knowledge of the aircraft. By understanding the mass growth factor, consumers can have this discussion with industry at eye level. Originality/value - The derivation of the equation for the direct calculation of the mass growth factor and the determination of the
E-learning environments represent digital platforms designed to facilitate online learning experiences. Recognizing the diverse learning preferences of individuals, the need for identifying and integrating multi-layer...
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In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approa...
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In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution ...
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Knowledge explosion is associated with the exponential growth of research literature production, triggering the need for new approaches to structure and synthesize knowledge. Traditional knowledge synthesis approaches...
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Current automatic segment extraction techniques for identifying target characters in videos have several limitations, including low accuracy, slow processing speeds, and poor adaptability to diverse scenes. This paper...
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The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research...
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The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve
This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neut...
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