48 research outputs found

    The Assessment of Learning Performance Using Dynamic Time Warping Algorithm for the Virtual Reality of Full-Body Motion Sensing Control

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    The issue of learning performance assessed by objective mathematic equation for the virtual reality of motion training is worth exploring, especially on the device of full-body control. We build a virtual reality system with full-body motion sensing control that offers an objective assessment method for the training of body movement. This proposed virtual reality system uses an intuitive interaction method through the motion of the trainer visualized in real time, and provides users with viewing the motion from the third-person viewpoint to follow the teacher as close as possible. The matching assessment method is based on dynamic time warping algorithm for two time series. In order to understand the effectiveness of objective assessment in interacting virtual environment with full-body control, we make a comparison with the subjective assessment by six viewers. Experimental results show that the dynamic time warping algorithm is promising and the same as the subjective assessment

    Multimedia Scheduling in Bandwidth Limited Networks

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    The Effects of Presence on User Experience Based on Regulatory Focus Theory

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    The goal of this study is to find the relationship between three types of presences and different facets of user experience, including perceived attractiveness, perceived ergonomic quality, and perceived hedonic quality. In addition, we would like to understand the effect of three types of presences for the different types of tasks based on the regulatory focus theory on user experience

    Tracking multitarget in cluttered environment

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    [[abstract]]A method for multitarget tracking and initiating tracking in a cluttered environment is proposed. The algorithm uses a sliding window of length uT (T is the sampling time) to keep the measurement sequence at time k. Instead of solving a large problem, the entire set of targets and measurements is divided into several clusters so that a number of smaller problems are solved independently. When a set of measurements is received, a new set of data-association hypotheses is formed for all the measurements lying in the validation gates within each cluster from time K-u+1 to K. The probability of each track history is computed, and, choosing the largest of these histories, the target measurement is updated with an adaptive state estimator. A covariance-matching technique is used to improve the accuracy of the adaptive state estimator. In several examples, the algorithm successfully tracks targets over a wide range of conditions[[booktype]]紙本[[booktype]]電子

    Manoeuvring multitarget tracking method in cluttered environment

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    [[abstract]]A method for tracking a manoeuvring multitarget in a cluttered environment is presented. The clutter or false alarms are assumed to occur uniformly and to be independently distributed. The algorithm is performed by taking a sliding window of length uT (T is the sampling time) at time K. Instead of solving a large problem, the entire set of targets and measurements is divided into clusters so that a number of smaller problems are solved independently. When a set of measurements is received, we form a new data-association hypothesis for the set of measurements lying in the validation gales; with each cluster from time K — u + 1 to K the probability of each track history is computed, and ihen by choosing the largest of these histories we perform the target measurement updated with the adaptive state esiimator. Meanwhile, the covariance-matching technique is adopted so that the accuracy of the adaptive state estimator will be improved. Simulation has shown the effectiveness of the tracking algorithm.[[booktype]]紙

    A method of compensation to adaptive state estimator

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    [[notice]]補正完畢[[conferencetype]]國內[[conferencedate]]19871219~1987121

    Manoeuvring target tracking algorithm for a radar system

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    [[abstract]]By incorporating the semi-Markov process into a bayesian estimation scheme, an adaptive state estimator is developed. This estimator can prevent the loss of the target tracking when a target makes a sudden radical change in its flight trajectory. A method of compensating for the uncertainty of the tracking performance is also presented. The covariance-matching technique is adopted such that the accuracy of the adaptive state estimator is improved. Several examples are given to illustrate the superior tracking performance, and this adaptive algorithm can easily be implemented on the digital computer with a little modification for different speeds.[[booktype]]紙

    An adaptive state estimator for radar tracking systems

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    [[notice]]補正完畢[[journaltype]]國

    A maneuvering tracking method in crossing targets

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    [[note]]補正完畢[[conferencetype]]國內[[conferencedate]]19880802~1988080
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