Home / Journals / JNM / Vol.2, No.1, 2020
  • Open Access

    ARTICLE

    Mixed Noise Removal by Residual Learning of Deep CNN

    Kang Yang1, Jielin Jiang1,2,*, Zhaoqing Pan1,2
    Journal of New Media, Vol.2, No.1, pp. 1-10, 2020, DOI:10.32604/jnm.2020.09356
    Abstract Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose a method based on residual learning… More >

  • Open Access

    ARTICLE

    A LoRaWAN Access Technology Based on Channel Adaptive Adjustment

    Li Ma, Meng Zhao*, Dongchao Ma, Yingxun Fu
    Journal of New Media, Vol.2, No.1, pp. 11-20, 2020, DOI:10.32604/jnm.2020.09715
    Abstract Low-power wide area network (LPWAN) has developed rapidly in recent years and is widely used in various Internet of Things (IoT) services. In order to reduce cost and power consumption, wide coverage, LPWAN tends to use simple channel access control protocols, such as the Aloha protocol. This protocol is simple with poor extension capability. In high-density environment, Aloha protocol will lead to low channel utilization, prolonged access and high conflict probability. Therefore, in order to solve the above problems, we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol, that is, a dynamic listening backoff mechanism.… More >

  • Open Access

    ARTICLE

    Knowledge Graph Representation Reasoning for Recommendation System

    Tao Li, Hao Li*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu
    Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767
    Abstract In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS model. KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm, then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning, then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning. Finally, through the full connection layer and sigmoid function to get the prediction ratings, and the items are sorted… More >

  • Open Access

    ARTICLE

    Authorized Attribute-Based Encryption Multi-Keywords Search with Policy Updating

    Muqadar Ali, Chungen Xu*, Abid Hussain
    Journal of New Media, Vol.2, No.1, pp. 31-43, 2020, DOI:10.32604/jnm.2020.09946
    Abstract Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage. Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes. However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation. In this paper, a ciphertext policy attribute-based encryption (CP-ABE) scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority. The data owner encrypts… More >

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