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  • Open Access


    A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

    Ahmad F. Subahi*

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 13-39, 2021, DOI:10.32604/csse.2021.014376

    Abstract This work presents the design of an Internet of Things (IoT) edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places. A wireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design. A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design. A Neo4j graph database is used as a target implementation generated from the proposed transformational system… More >

  • Open Access


    MoTransFrame: Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

    Panyu Liu1, Huilin Ren2, Xiaojun Shi3, Yangyang Li4, *, Zhiping Cai1, Fang Liu5, Huacheng Zeng6

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2321-2334, 2020, DOI:10.32604/cmc.2020.010522

    Abstract Deep learning technology has been widely used in computer vision, speech recognition, natural language processing, and other related fields. The deep learning algorithm has high precision and high reliability. However, the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power. In this paper, we propose MoTransFrame, a general model processing framework for deep learning models. Instead of designing a model compression algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and More >

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