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Face recognition based on sparse representation is investigated in this paper. Dimensionality reduction is the process of projecting original image data into a low dimensional space, and usually be conducted before dictionary learning. However dimensionality reduction may lose information that is important to the face recognition task. Although the accuracy rate of face recognition varies with different...
This paper presents a novel WLAN-based indoor localization algorithm (i.e., HED) to combat the environmental dynamics by tolerating the sequence disorders caused by AP (access point) changes, while harvesting from the bursting number of available wireless resources. Via extensive real-world experiments lasting for over 6 months, we show the superiority of our HED algorithm in terms of accuracy and...
Indoor localization remains a hot topic and receives tremendous research efforts during the last few decades. While most previous efforts focus on the designing issue, little effort has been paid to the impact of different environmental parameters on the system performance. To this end, we present an extensive empirical study with real-world experiments to provide sufficient data for analysis. By...
Hidden Markov model (HMM) is successfully used in speech recognition. However, there is an unavoidable flaw in assuming strong independence for sequences labeling in HMM. While conditional random fields (CRFs) can relax this assumption for HMM, and can also solve the label bias problem efficiently. In this paper, we investigate CRFs for Chinese syllable recognition in continuous speech due to its...
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