This paper proposes a neural network model which receives visual inputs from a robot moving freely in a room, and extracts its position and direction information separately. The model has three-dimensional structure i...
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Web applications exhibit dynamic behaviour through such features as animation, rapidly changing presentations, and interactive forms. The growing complexity of web applications requires a rigorous modelling approach c...
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Every activity has some Risk involved in it. Analyzing the Risk involved in a transaction is important to decide whether to proceed with the transaction or not. Similarly in Peer-to-Peer communication analyzing the Ri...
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Risk is associated with almost every activity that is undertaken on a daily life. Risk is associated with Trust, Security and Privacy. Risk is associated with transactions, businesses, informationsystems, environment...
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Takemaru-kun system is a practical speech-oriented guidance system developed to examine spoken interface through longterm operation in a public place that collected natural humanmachine interaction data. In 2004 the f...
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Takemaru-kun system is a practical speech-oriented guidance system developed to examine spoken interface through longterm operation in a public place that collected natural humanmachine interaction data. In 2004 the following advances improving reliability of the system were introduced, which conduced acquiring positive increase of access from users: (1) Rejection of unintended speech based on Gaussian Mixture Models (GMMs);(2) Removal of short, unnecessary inputs of impulsive noise;(3) Child or adult user discrimination;(4) Web-based monitoring mechanisms. This paper summarizes the Takemaru-kun system and analysis of 177,789 data collected by two-years actual operation. Experiments with the collected data proved that a combination of GMM-based verification and short input removal can excise 85% of the invalid inputs, including laughter, incomprehensible utterances, and even some background utterances.
Workflow technology has recently been employed as a framework for implementing large-scale business-to-business (B2B) informationsystems over the Internet. This typically requires collaborative enactment of complex w...
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The proposed scaling algorithm outperforms other standard and widely used scaling techniques. The algorithm uses a mask of maximum four pixels and calculates the final luminosity of each pixel combining two factors;th...
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We show that a Horn SAT and logic programming approach to obtain polynomial time algorithms for problem solving can be fruitfully applied to finding plans for various kinds of goals in a non-deterministic domain. We p...
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Finding an object or a face in an input image is a search problem in the spatial domain. Neural networks have shown good results in detecting a certain face/object in a given image. In this paper, faster neural networ...
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Finding an object or a face in an input image is a search problem in the spatial domain. Neural networks have shown good results in detecting a certain face/object in a given image. In this paper, faster neural networks for face/object detection are presented. Such networks are designed based on cross correlation in the frequency domain between the input image and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the search process. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small-size sub-images, and then each of them is tested separately using a single faster neural network. Furthermore, the fastest face/object detection is achieved using parallel processing techniques to test the resulting sub-images simultaneously using the same number of faster neural networks. In contrast to using faster neural networks only, the speed-up ratio is increased with the size of the input image when using faster neural networks and image decomposition. Moreover, the problem of local subimage normalization in the frequency domain is solved. The effect of image normalization on the speed-up ratio for face/object detection is discussed. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. The overall speed-up ratio of the detection process is increased as the normalization of weights is carried out off line.
This paper proposes a detection procedure for the interval change of two chest X-ray images with different rotation angles of the human body around the axis parallel to the projection plane (such as anterior-posterior...
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