8 research outputs found

    Impact of 5g telematics

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    New technologies such as wearable wireless medical devices are transforming the way healthcare is delivered. As these devices become more powerful and numerous, the daunting challenge is whether the existing communications infrastructure can meet the requirements of the changing landscape. 5G technology offers huge potential for future personalised healthcare delivery.This article appears in the Special issue on Healthcare Technology,and is part of the editorial section - Information Technology

    Impact of 5g telematics

    No full text
    New technologies such as wearable wireless medical devices are transforming the way healthcare is delivered. As these devices become more powerful and numerous, the daunting challenge is whether the existing communications infrastructure can meet the requirements of the changing landscape. 5G technology offers huge potential for future personalised healthcare delivery.This article appears in the Special issue on Healthcare Technology,and is part of the editorial section - Information Technology

    LMS-based low-complexity game workload prediction for DVFS

    No full text
    While dynamic voltage and frequency scaling (DVFS) based power management has been widely studied for video processing, there is very little work on game power management. Recent work on proportional-integral-derivative (PID) controllers fro predicting game workload used hand-turned PID controller gains on relatively short game plays. This left open questions on the robustness of the PID controller and how sensitive the prediction quality is on the choice of the gain values, especially for long game plays involving different scenarios and scene changes. In this paper we propose a Least Mean Squares (LMS) Linear Predictor, which is a regression model commonly used for system parameter identification. Our results show that game workload variation can be estimated using a linear-in-parameters (LIP) model. This observation dramatically reduces the complexity of parameter estimation as the LMS Linear Predictor learns the relevant parameters of the model iteratively as the game progresses. The only parameter to be tuned by the system designer is the learning rate, which is relatively straightforward. Our experimental results using the LMS Linear Predictor show comparable power savings and game quality with those obtained from a highly-tuned PID controller
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