More Than Scheduling: Novel and Efficient Coordination Algorithms for Multiple readers in RFID Systems

Li, Xuan; Chen, Xinning; Yang, Qiuying; Zhang, Shigeng; Guo, Song; Luo, Jie; Li, Kenli

doi:10.1109/tmc.2022.3167843

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Siamese network is a special type of neural network used to determine whether two input data are similar or the same [21, 22]. The MOCO‐MIM model utilizes a Siamese network with two branches: online one encodes the first view and predicts the second based on relative positions, while the target one generates the new target by encoding the second view.…”

Section: Methodsmentioning

confidence: 99%

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Wang,

Qiao,

Guo

et al. 2024

Electronics Letters

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Retinopathy of prematurity (ROP) mainly occurs in premature infants with low birth weight, and it is the leading cause of childhood blindness. Early and accurate ROP diagnosis is imperative for appropriate treatment. However, less research concentrates on early‐stage ROP diagnosis based on limited‐labelled images in an imbalanced dataset. To address the dilemma, this study proposed a novel self‐supervised network, MOCO‐MIM, for early ROP grading. The proposed classification network was evaluated on a total of 553 labelled fundus images from 89 preterm infants. The trained network achieved a test accuracy of 98.29% and an AUC score of 97.6% for three stages of grading. The adopted method is verified that the proposed method can be detected early stages of ROP more efficiently and grade the severity more accurately based on limited‐labelled fundus images, which is superior to the existing state‐of‐the‐art methods.

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Section: Methodsmentioning

confidence: 99%

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Wang,

Qiao,

Guo

et al. 2024

Electronics Letters

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“…Several meta-heuristic techniques [23][24][25][26] have been applied in diverse domains such as industry, academic, research, and business to solve the NP-complete problem, 2728 combinatorial optimization, 29 pattern recognition, 30 anti-collision problems, 31 and robot motion. 32 These algorithms provide the approximate solution of the problem and have the capability to provide the compromise solution of the conflicting parameter within the limited period in the cloud environment.…”

Section: Motivation To Choose Meta-heuristic Techniquesmentioning

confidence: 99%

“…where ESM ij indicates the ith monkey of the population with jth dimension, SN is food source position, and SUM represents the difference between current and randomly generated position as shown in Equation (31).…”

Section: Enhanced Smo Algorithmmentioning

confidence: 99%

Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

Kumar

Dubey

Singh

et al. 2022

Concurrency and Computation

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The cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on-demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self-adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission-fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

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People Counting with Carry-on RFID Tags

Wang,

Liu,

et al. 2023

2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)

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More Than Scheduling: Novel and Efficient Coordination Algorithms for Multiple readers in RFID Systems

Cited by 7 publications

References 34 publications

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

People Counting with Carry-on RFID Tags

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