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Techniques for partial discharge (PD) processing have been increasing in recent researches, since on-site PD measurement can provide valuable information about electrical equipments and their insulation. A lot of approaches for PD filtering have been presented in the literature, mainly based on classical techniques of signal processing. However, few approaches evaluate or present effective methods...
We propose a novel signal model, based on sparse representations, that captures cross-scale features for visual signals. We show that cross-scale predictive model enables faster solutions to sparse approximation problems. This is achieved by first solving the sparse approximation problem for the downsampled signal and using the support of the solution to constrain the support at the original resolution...
In this paper, we shall critically appraise sparse representation based denoising applications. An essential task for this framework is dictionary learning. Our novel proposition involves learning such a dictionary not only by analyzing the distribution of training data in the metric space but also exploiting local nature of the visual scene. Subsequently, the learning scheme is further developed...
In this paper, we propose to use noise reduction technology on both speech signal and visual signal by using exemplar-based sparse representation features for audio-visual speech recognition. First, we introduce sparse representation classification technology and describe how to utilize the sparse imputation to reduce noise not only for audio signal but also for visual signal. We utilize a normalization...
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