35321520
Reconstructing the Correct Writing Sequence from a Set of Chinese Character Strokes
A Chinese character is composed of several strokes ordered in a particular sequence. The stroke sequence contains useful online information for handwriting recognition and handwriting education. Although there exist some general heuristic stroke sequence rules, sometimes these rules can be inconsistent making it difficult to apply them to determine the standard writing sequence given a set of strokes. In this paper, we proposed a method to estimate the standard writing sequence given the strokes of a Chinese character. The strokes are modeled as discrete states with the state transition costs determined by the result of the classification into forward/backward order of each stroke pair using the positional features. Candidate sequences are found by shortest path algorithm and the final decision of the stroke sequence is made according to the total handwriting energy in case there is more than one candidate sequence. Experiments show that our results provide better performance than existing approaches.
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
206606372
A Web-Based Chinese Handwriting Education System with Automatic Feedback and Analysis
We propose a web-based Chinese handwriting system that allows students to learn Chinese handwriting at anytime and anywhere. The system combines technologies such as electronic ink, database, Flash animations, and pattern recognition technique to provide students with an intelligent and effective learning environment. In particular, our system contains an Automatic Feedback and Analysis (AFA) tool that can check multiple kinds of handwriting mistakes and provide useful feedback to the user. Survey results show that most users agreed that our proposed system can help them to learn Chinese handwriting more efficiently as well as reducing teachers’ workload. The user evaluation results suggest that it takes less time for users to learn Chinese handwriting with our system than with traditional teaching method.
37235208
Model-Based Analysis of Chinese Calligraphy Images
8282971
Protein Subcellular Localization Based on Psi-blast and Machine Learning
Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.
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.
1007735
Structurally Unambiguous Finite Automata
We define a structurally unambiguous finite automaton (SUFA) to be a nondeterministic finite automaton (NFA) with one starting state q0 such that for all input strings w and for any state q, there is at most one path from q0 to q that consumes w. The definition of SUFA differs from the usual definition of an unambiguous finite automaton (UFA) in that the new definition is defined in terms of the transition logic of the finite automaton, and is independent of the choice of final states. We show that SUFA can be exponentially more succinct in the number of states than UFA and MDFA (deterministic finite automata with multiple initial states). Some interesting examples of SUFA are given. We argue that SUFA is a meaningful concept, and can have practical importance as it can implemented efficiently on synchronous models of parallel computation.
32788724
Tight Lower Bounds on the Size of Sweeping Automata
A sweeping automaton is a two-way deterministic finite automaton which makes turns only at the endmarkers. We say that a sweeping automaton is degenerate if the automaton has no left-moving transitions. We show that for each positive integer n, there is a nondeterministic finite automaton An over a two-letter alphabet such that An has n states, whereas the smallest equivalent nondegenerate sweeping automaton has 2n states.
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.
11779035
Chaotic binary sequences for efficient wireless multipath channel estimation
This paper presents a better binary sequence design for efficient wireless multipath channel estimation. Chaotic binary sequences with better properties than conventional maximal length sequences are obtained through an optimization algorithm based on DNA computation. The proposed method is attractive due to its ability to generate binary sequences for specific design criteria. The generated chaotic binary sequences have superior performance compared to maximal length sequences and have complexity similar to the levels of shift register implementation. The effectiveness of the proposed method was evaluated for sequence design for frequency domain channel estimation. Simulation results clearly show the improved performance of the sequence obtained using the proposed scheme over the maximal length sequence. Further, the proposed scheme is not only limited to channel estimation sequence design but can be applied to various communication system design problems.
16283263
Robust classification of blurred imagery
In this paper, we present two novel approaches for the classification of blurry images. It is assumed that the blur is linear and space invariant, but that the exact blurring function is unknown. The proposed fusion-based approaches attempt to perform the simultaneous tasks of blind image restoration and classification. We call such a problem blind image fusion. The techniques are implemented using the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image restoration and the Markov random field (RIRF)-based fusion method for classification by Schistad-Solberg et al. Simulation results on synthetic and real photographic data demonstrate the potential of the approaches. The algorithms are compared with one another and to situations in which blind blur removal is not attempted.