Browsing by Author "Wong, Junhua"

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    Efficient IoT Big Data Streaming With Deep-Learning-Enabled Dynamics
    (IEEE, 2022-11-11) Wong, Junhua; Piuri, Vincenzo; Scotti, Fabio; Zhang, Qingxue; Electrical and Computer Engineering, School of Engineering and Technology
    Internet of Medical Things (IoMT) is igniting many emerging smart health applications, by continuously streaming the big data for data-driven innovations. One critical obstacle in IoMT big data is the power hungriness of long-term data transmission. Targeting this challenge, we propose a novel framework called, IoMT big-data Bayesian-backward deep-encoder learning (IBBD), which mines deep autoencoder (AE) configurations for data sparsification and determines optimal tradeoffs between information loss and power overhead. More specifically, the IBBD framework leverages an additional external Bayesian-backward loop that recommends AE configurations, on top of a traditional deep learning loop that executes and evaluate the AE quality. The IBBD recommendation is based on confidence to further minimize the regularized metrics that quantify the quality of AE configurations, and it further leverages regularization techniques to allow adjusting error–power tradeoffs in the mining process. We have conducted thorough experiments on a cardiac data streaming application and demonstrated the superiority of IBBD over the common practices such as discrete wavelet transform, and we have further generalized IBBD through validating the optimal AE configurations determined on one user to other users. This study is expected to greatly advance IoMT big data streaming practices toward precision medicine.
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    Wearable Big Data Pertinence Learning with Deep Spatiotemporal co-Mining
    (IEEE, 2022-05) Wong, Junhua; Zhang, Qingxue; Electrical and Computer Engineering, School of Engineering and Technology
    Wearable Computers are greatly advancing big data practices, by levering their capabilities of ubiquitous big data capturing and streaming. However, one critical challenge is the amount of data to be transmitted, which consumes too much energy of the battery-constrained wearable devices. Targeting this obstacle, we propose a novel big data pertinence learning approach, which can learn and extract pertinent patterns in wearable big data for redundancy reduction. More specifically, a hybrid deep learning approach based on both Convolutional Autoencoder and Long Short-term Memory is proposed, which can mine both spatial and temporal patterns in the data for key pattern extraction. The achieved spatiotemporal co-mining ability when evaluated on a real- world motion dynamics big data application, demonstrates the attractive potential of pertinence extraction and redundancy minimization. This study is expected to greatly advance wearable big data practices.