In IoT-based air quality monitoring system, a set of IoT devices are deployed for sensing of the air quality data at different junction of a smart city. These deployed IoT devices periodically forward the sensed data ...
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ISBN:
(纸本)9789811925412;9789811925405
In IoT-based air quality monitoring system, a set of IoT devices are deployed for sensing of the air quality data at different junction of a smart city. These deployed IoT devices periodically forward the sensed data to the base station for further processing and analytics. Missing of Air Quality Index (AQI) data is very challenging issues in real-time monitoring of AQI in a smart city due to failure of IoT devices, data corruption in the wireless transmission, malfunction of sensors etc. Missing datarecovery is a very fundamental issue with real-time IoT-based AQI monitoring system. To solve the missing data recovery problem, this paper has used tensor complete based datarecovery models such as Bayesian Gaussian Canonical Polyadic (BGCP) decomposition, Bayesian Augmented Tensor Factorization (BAIT) and High accuracy Low Rank Tensor Completion (HaLRTC) to recovery the AQI missing data. Performance analysis of the tensor complete based datarecovery models is evaluated using real time AQI dataset in terms of Root Mean Square Error and Mean Absolute Percentage Error.
Colourisation is a process of adding colours to greyscale image. It is noticed that the colourisation process for corrupted image usually needs image to be completed and then colourised. In this study the authors pres...
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Colourisation is a process of adding colours to greyscale image. It is noticed that the colourisation process for corrupted image usually needs image to be completed and then colourised. In this study the authors present a novel scribble-based colourisation algorithm of corrupted greyscale image. The authors construct a block-based bilateral filter (BBF) framework for image completion and colourisation. Distance transform and an adaptive weight selection scheme are introduced to the BBF framework in order to achieve improvements of accuracy and speed. The rationale behind the authors' algorithm is that image completion and colourisation can be both regarded as missing data recovery problem. Unlike existing sequential image completion method and colourisation method, the authors' algorithm can accomplish image completion and colourisation work under a proposed novel unified framework. Experiments results show the benefits of the authors' method.
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