In general, parallel applications require lots of computer power and grid computing. Efficient Resource Discovery (RD) algorithms determine grid resource allocation and execution time. To improve the resource distribu...
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Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due...
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Head-based Totally Clustering is a technique of grouping facts and factors with similar traits using a rooted tree structure. The research paper Deriving an electricity-law version for Aggregating facts with Head prim...
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With the main application background of using and establishing a scientific and objective hospital infection management quality monitoring system in line with the actual specifications of the hospital, We try to explo...
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In this paper, we propose an edge computing system where an edge server (ES) gathers multi-media sensing data from collaborative sensing devices (SDs) in upstream and broadcasts the sensing information to downstream u...
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ISBN:
(纸本)9798350378412
In this paper, we propose an edge computing system where an edge server (ES) gathers multi-media sensing data from collaborative sensing devices (SDs) in upstream and broadcasts the sensing information to downstream user devices that perform different tasks, e.g., image segmentation and saliency detection. In particular, we leverage the wisdom of semantic communication that applies joint source-channel coding (JSCC) techniques to extract and transmit only the task-relevant information contained in the sensing data to reduce the communication and computation workload. To efficiently train the upstream and downstream encoders/decoders involved in the system, instead of performing end-to-end training, we propose a task-oriented training strategy that decouples the training into two separate steps without compromising the performance. We conduct experiments using open-source multi-task datasets under varying channel signal-to-noise power ratios (SNRs). The simulation results show that, compared to the representative benchmark approaches, the proposed method can effectively reduce 92.6%/75% uplink/downlink data workload and elegantly balance the performance of different tasks.
Clinical image segmentation is a challenge that identifies a selected organ or anatomical structure in a given scientific image which can be of radiographic or other modality. Expertise switch is an essential side of ...
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information compression is a vital approach for optimization and optimization of the scale of a digital record without affecting its content material. This paper offers an entropy-based analysis of two distinct levels...
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Digital products copyright protection is a severe issue in the growth of network technology. The security of data, especially medical data, is of utmost importance. The growth of the internet gives easy access to tran...
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As the standard of privacy and security of data cross-domain circulation improves, a large number of ciphertext computation puts high demands on the communication delay and arithmetic power on the performance of the e...
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Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) mod...
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ISBN:
(纸本)9798350387452;9798350387445
Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) modules alongside cutting-edge communication and networking architectures, DTs will be able to develop cognitive and social skills and build relationships with each other, thus facilitating the sharing of services and experience. However, in current implementations, DTs typically engage with their physical counterpart only, for predictive maintenance and optimization, while protocols for inter-twin communications and service discovery are still unexplored. In this paper, we focus on service discovery and provisioning in DT networks hosted at the network edge. In our design, the Social Internet of Things (SIoT) notion is applied to build social networks among DTs and, in parallel, name-based primitives, according to the information Centric Networking (ICN) paradigm, are considered to support inter-twin interactions. Two distributed name-based service discovery mechanisms are envisioned: a social-driven scheme, leveraging friendship and similarities among DTs' names, and a network-driven scheme, leveraging the ICN forwarding fabric only. A performance evaluation shows the benefits of the conceived solution in terms of reduced discovery latency compared to legacy centralized approaches.
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