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International Journal of Embedded and Real-Time Communication Systems
Journal Prestige (SJR): 0.176

Citation Impact (citeScore): 1
Number of Followers: 6

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ISSN (Print) 1947-3176 - ISSN (Online) 1947-3184
Published by IGI Global

[146 journals]

Concept and Models of Information Application for Actions in Systems
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  Abstract: The article elaborates the concept and models of information application for actions in system. It is shown that there is a gap between the need to solve practical problems of information application for actions in systems based on mathematical models, which can predict results of information application analytically — from the one hand, and absence of needed mathematical models and modelling methods — from another hand. Modelling method suggested filling this gap uses schemes of information application for actions in systems. Method illustrated by creation of graph-theoretic and, based on them, numerical probabilistic models of information application for actions in systems. Graph-theoretic models depicts possible sequences of probable actions, information use, resulting events and states of functional probabilistic models and based on them – measures of changing functioning quality in changing conditions. Such measures allow choosing information actions in real time. Examples are shown. The article is an extended version of earlier publication of the conference paper.
  Keywords: Networking and Telecommunications; Media & Communications; Telecommunications
  Citation: International Journal of Embedded and Real-Time Communication Systems (IJERTCS), Volume: 14, Issue: 1 (2023) Pages: 0-0
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJERTCS.317084
  Issue No: Vol. 14, No. 1 (2023)
One, Five, and Ten-Shot-Based Meta-Learning for Computationally Efficient
Head Pose Estimation
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  Authors: Joshi; Manoj, Pant, Dibakar Raj, Heikkonen, Jukka, Kanth, Rajeev
  Pages: 1 - 24
  Abstract: Many real-world applications rely on head pose estimation. The performance of head pose estimation has significantly improved with techniques like convolutional neural networks (CNN). However, CNN requires a large amount of data for training. This article presents a new framework for head pose estimation using computationally efficient first-order model-agnostic meta-learning (FO-MAML)-based method and compares the performance with existing MAML-based approaches. Experiments using one-shot, five-shot, and ten-shot settings are done using MAML and FO-MAML. A mean average error (MAEavg) of 7.72, 6.30, and 5.32 has been achieved in predicting head pose using MAML for one-, five-, and ten-shot settings, respectively. Similarly, MAEavg of 8.33, 6.84, and 6.23 has been achieved in predicting head pose using FO-MAML for one-, five-, and ten-shot settings, respectively. The computational complexity of an outer-loop update in MAML is found to be O(n2) whereas for FO-MAML it is O(n).
  Keywords: Networking and Telecommunications; Media & Communications; Telecommunications
  Citation: International Journal of Embedded and Real-Time Communication Systems (IJERTCS), Volume: 14, Issue: 1 (2023) Pages: 1-24
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJERTCS.316877
  Issue No: Vol. 14, No. 1 (2023)

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