Sanparith Marukatat

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This paper investigates two major issues in using a tri-axial accelerometer-embedded mobile phone for continuous activity monitoring, i.e. the difference in orientations and locations of the device. Two experiments with a total of ten test subjects performed six daily activities were conducted in this study: one with a device fixed on the waist in sixteen(More)
We investigate a new approach for online handwritten shape recognition. Interesting features of this approach include learning without manual tuning, learning from very few training samples, incremental learning of characters, and adaptation to the user-specific needs. The proposed system can deal with two-dimensional graphical shapes such as Latin and(More)
In this paper we study the use of confidence measures for an on-line handwriting recognizer. We investigate various confidence measures and their integration in an isolated word recognition system as well as in a sentence recognition system. In isolated word recognition tasks, the rejection mechanism is designed in order to reject the outputs of the(More)
This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device(More)
In this paper, we proposed a novel technique for face recognition using Two-Dimensional Random Subspace Analysis (2DRSA), based on the Two-Dimensional Principal Component Analysis (2DPCA) technique and Random Subspace Method (RSM). In conventional 2DPCA, the image covariance matrix is directly calculated via 2D images in matrix form, by concept of the(More)
In this paper, we proposed a new two-dimensional linear discriminant analysis (2DLDA) method. Based on two-dimensional principle component analysis (2DPCA), face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the small sample size (SSS)(More)
This paper proposes an efficient acoustic model adaptation method based on the use of simulated-data in maximum likelihood linear regression (MLLR) adaptation for robust speech recognition. Online MLLR adaptation is an unsupervised process which requires an input speech with phone labels transcribed automatically. Instead of using only the input signal in(More)
This paper focuses on designing a handwriting recognition system dealing with on-line signal, i.e. temporel handwriting signal captured through an electronic pen or a digitalized tablet. We present here some new results concerning a hybrid on-line handwriting recognition system based on Hidden Markov Models (HMMs) and Neural Networks (NNs), which has(More)