206606389
e-Learning Martial Arts
Traditionally, when people want to learn martial arts, they have to go to training clubs and learn under the coach together with the other students. To reduce the tuition fees, there are usually a lot of students under a single coach and hence, it is difficult for the students to get enough suggestions in the class. It would be far easier if the students could practice themselves at home and ask for suggestions from a virtual coach in the computer. Occasionally, in case they find difficulties going to the next step, they may then approach the real coach for suggestions and training. In this paper, we propose such a training system based on the motion capture system. The system automatically analyzes the motions of the player and gives suggestions. The students can also view the martial art techniques stored in the system or their own techniques captured by the motion capture system from various points of view in order to gain objective ideas of the techniques.
6040271
Web-Based Handwriting Education with Animated Virtual Teacher
Traditionally, in teaching handwriting, the teacher writes a character on the blackboard, and then students try to follow the teacher in order to write the same character. The teacher can check whether a student’s handwriting is spatially correct by looking at its shape but he/she is not able to verify if the student is writing in the correct stroke order with the correct number of strokes. As a result, we design a web-based handwriting education system. With our proposed system, students can stay at home to do the exercises for learning the handwriting of characters. A student’s handwriting can be captured in digital format with the information in both temporal and spatial domain and the results can be sent to the teacher for evaluation. In addition, we propose to animate a virtual teacher on the student’s terminal for demonstrating the handwriting of the characters to give students a sense of presence.
18854339
Brush Writing Style Classification from Individual Chinese Characters
Chinese calligraphic artwork usually contains a few or even a single character. In order to perform automatic brush writing style classification on these kinds of Chinese calligraphic images, existing approaches need to be modified because they assume that many characters of the same style exist in the input image. A novel approach is proposed to address the brush writing style classification problem for single-character Chinese calligraphic images by combining the texture analysis and the structural information through a set of parameterised ellipses
33568725
A novel M-ary chaotic spread spectrum communication scheme for DSRC system in ITS
In this paper, a novel non-coherent M-ary chaotic spread spectrum communication scheme is proposed for dedicated short range communications (DSRC) in intelligent transportation systems (ITS). The proposed scheme encodes the M-ary information symbols on to the bifurcation parameter of a chaotic system. This new chaotic communication scheme exploits the ergodic properties of chaotic signals for demodulating the received signal. The proposed scheme is basically non-coherent which eliminates the complex synchronization procedure. Furthermore, the proposed scheme utilizes a low complexity multipath mitigation technique to combat fading. Performance analysis shows the superior communication performance of the proposed chaotic spread spectrum scheme over conventional spread spectrum schemes in rapid multipath fading environments. Thus, the developed scheme is attractive in terms of communication performance, multipath mitigation, and system complexity.
38019992
A comparative study of different chaos based spread spectrum communication systems
Using different properties of chaotic systems, different chaos based communication schemes have been proposed in the literature. However, their performances are seldom compared. In this paper, we implement several major chaos based communication methods including chaotic spreading codes, chaotic carrier, chaos shift keying, chaotic masking and chaotic parameter modulation in a common digital communication background. Their performance in terms of bit error rate and robustness are then evaluated.
683638
Descriptional Complexity of Machines with Limited Resources
Over the last 30 years or so many results have appeared on the descriptional complexity of machines with limited resources. Since these results have appeared in a variety of different contexts, our goal here is to provide a survey of these results. Partic- ular emphasis is put on limiting resources (e.g., nondeterminism, ambiguity, lookahead, etc.) for various types of finite state machines, pushdown automata, parsers and cellu- lar automata and on the effect it has on their descriptional complexity. We also address the question of how descriptional complexity might help in the future to solve practical issues, such as software reliability.
39013687
On factorization forests of finite height
Simon (Theoret. Comput. Sci. 72 (1990) 65-94) has proved that every morphism from a free semigroup to a finite semigroup S admits a Ramseyan factorization forest of height at most 9|s|. In this paper, we prove the same result of Simon with an improved bound of 7|S|. We provide a simple algorithm for constructing a factorization forest. In addition, we show that the algorithm cannot be improved significantly. We give examples of semigroup morphism such that any Ramseyan factorization forest for the morphism would require a height not less than |S|.
38923052
Reconstruction of piecewise chaotic dynamics using a multiple model approach
In this paper we propose using a multiple model (MM) predictor to reconstruct piecewise chaotic dynamic. The motivation relies on the observation that conventional single model is usually incapable of reconstructing the piecewise dynamic properly because a piecewise map is non-differentiable. In our approach, multiple radial basis function (RBF) neural nets are used to model the dynamic in different partition intervals. Switching between different intervals could be estimated by a nonlinear gate model. In particular, an Expectation-Maximization (EM) algorithm is employed to train the MM-RBF. Compared to the conventional approach, the proposed MM is shown to greatly improve the reconstruction performance for piecewise chaotic dynamic. We further apply it to combat channel distortions in an analog chaotic spread spectrum (SS) system. It is found that the proposed method has satisfactory equalization performance even when channel effect is strong.