The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used t...
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The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used to construct the IoMT architecture for remote patient monitoring. All the sub-systems have undergone performance tests as well as clinical validation. Clinical validation of IoMT software on 100 patients was successful. Digital representations' size and complexity are reduced by up to 80%, making them appropriate for use in developing narrow-band IoT networks. In particular for low-power devices, performance measurement revealed that bandwidth and energy were reduced to 97% and 95%, respectively.
CONTEXT: Versioning allows users to efficiently create and evolve artifacts, not only but especially in the domain of model-driven engineering. Due to collaboration tools, the place and time where users jointly work a...
CONTEXT: Versioning allows users to efficiently create and evolve artifacts, not only but especially in the domain of model-driven engineering. Due to collaboration tools, the place and time where users jointly work at their artifacts do not play an important role anymore. Objective: We systematically elaborated a classification framework for collaboration and versioning tools. The intention is threefold: First, it should be possible to classify existing approaches. Second, deriving from user goals requirements to be met, and capabilities to be supported by a new tool. Third, given a set of capabilities, highlight which user goals can be achieved. Method: According to Kang and Lee, we systematically elaborated a problem space and a solution space and created mappings between both spaces and their sub-spaces. To demonstrate the applicability, we classified existing VCS like git, Google Docs, and several MDE-specific approaches. Results: The created feature diagram covers 238 features and contains about 87 constraints. Conclusion: The developed framework supports researchers and developers in classifying their tools, revealing new opportunities to improve their tools, and guiding the development of new tools.
With the rapid development of smart grid technology, a new challenge has been brought about by the massive data generated from distributed devices. Data aggregation technology plays a crucial role in smart grid scheme...
With the rapid development of smart grid technology, a new challenge has been brought about by the massive data generated from distributed devices. Data aggregation technology plays a crucial role in smart grid schemes, in terms of energy data statistics and resource allocation. However, achieving fast error-checking and improving device fault-tolerance are still open issues in the smart grid data aggregation. To address these issues, this paper proposes an error-tolerant data aggregation scheme integrated with error-checking mechanisms. The scheme proposes a tree-based aggregation structure, facilitating swift error-checking of data. We combine masking values with user anonymity in the ElGamal cryptographic scheme to ensure the security of sensitive data and resist collusion attacks. Both theoretical and experimental analyses demonstrate that this scheme enables a lightweight, secure data aggregation, ensuring enhanced reliability and error-checking.
To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existi...
To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.
Creating a design from modular components necessitates three steps: Acquiring knowledge about available components, conceiving an abstract design concept, and implementing that concept in a concrete design. The third ...
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As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. ...
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As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data pro...
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The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject an...
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The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.
As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we pr...
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As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.
Technology has increased the interest and demand for pervasive systems which require contextual information to function at optimal capacity. There have been numbers of research in context-aware systems that has limite...
Technology has increased the interest and demand for pervasive systems which require contextual information to function at optimal capacity. There have been numbers of research in context-aware systems that has limited focused on the semantic-based approach in the crowdsourcing domain. Thus, it promotes challenges in the context and service acquisition and representation for reasoning control mechanism. This paper aims to review semantic-based reasoning framework with a focus on the mobile crowdsourcing domain. Different domains acquire different contextual information, either extrinsic or intrinsic. The review of the frameworks has help to formulate a process framework that applied semantic approach that has the important component for context-aware reasoning process. The framework can be used in the context-aware mobile crowdsourcing domain or can be generalized to other domain to aid reasoning control. Its advantage over other crowdsourcing frameworks which is focuses not only on context and service acquisition but also the representation on the acquired information.
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