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One-bit transform (1BT), followed by binary motion estimation, is an effective alternative for accelerating traditional 8-bit motion estimation (ME) in video coding. The underlining assumption in the design of 1BT methods is that natural videos contain noise. For screen content videos, however, the special characteristics (e.g. screen content is typically noise-free) can be exploited to further improve...
In sensor networks, due to power outage at a sensor node, hardware dysfunction, or bad environmental conditions, not all sensor samples can be successfully gathered at the sink. Additionally, in the data stream scenario, some nodes may continually miss samples for a period of time. In this paper, a sparsity-based online data recovery approach is proposed. We construct an overcomplete dictionary composed...
The aim of this paper is to introduce the design and evaluation of an ontology-based affective tutoring system on digital arts. The major clues for emotion recognition are the text pieces inputted by the learners. The semantic inference of the emotions is done by use of an ontology called OMCSNet. The system also incorporates an agent that provides feedback based on the inferred emotions. The SUS...
Audio is a useful modality complement to video for healthcare monitoring. In this paper, we investigate the use of hierarchical hidden Markov models (HHMMs) for healthcare audio event classification. We show that HHMM can handle audio events with recursive patterns to improve the classification performance. We also propose a model fusion method to cover large variations often existing in healthcare...
We formalize data scaling classification (DSC) as a technique to trade the accuracy of classification with the network transmission load in stream analysis frameworks. We apply the proposed data scaling approaches to ECG classification in remote health monitoring systems. Experimental results show satisfactory resource savings for small amounts of utility degradation (e.g., 33% of bandwidth saving...
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