1639053
A Constraint-Based Interactive Train Rescheduling Tool
In this paper, we report the design and implementation of a constraint-based interactive train rescheduling tool, a project in collaboration with the International Institute for Software Technology, United Nations University (UNU/IIST), Macau. We formulate train rescheduling as constraint satisfaction and describe a constraint propagation approach for tackling the problem. Algorithms for timetable verification and train rescheduling are designed under a coherent framework. Formal correctness properties of the rescheduling algorithm are established. We define two optimality criteria for rescheduling that correspond to minimizing the number of station visits affected and passenger delay respectively. Two heuristics are then proposed to speed up and direct the search towards optimal solutions. The feasibility of our proposed algorithms and heuristics are confirmed with experimentation using real-life data.
42246755
A Constraint-Based Interactive Train Rescheduling Tool
In this paper, we formulate train rescheduling as constraint satisfaction problem and describe a constraint propagation approach to tackle it. Algorithms for timetable verifications and train rescheduling are designed under a coherent framework. We define two optimality criteria that correspond to minimizing passenger delay and the number of station visit modifications respectively for rescheduling. Two heuristics are then proposed to speed up and direct the search towards the optimal solutions. The feasibility of our proposed algorithms and heuristics are confirmed with experimentation using real-life data.
6994949
Ergodic chaotic parameter modulation with application to digital image watermarking
This paper presents a novel technique for image watermarking based on chaos theory. Chaotic parameter modulation (CPM) is employed to modulate the copyright information into the bifurcating parameter of a chaotic system. The system output is a wideband signal and is used as a watermark to be inserted into the host image. In the detection, a novel method based on the ergodic property of chaotic signal is developed to demodulate the embedded copyright information. Compared to previous works on blind watermarking, the proposed technique can effectively remove the interference from the host image and, thus, improve the detection performance dramatically. Simulation results show that the ergodic CPM approach is effective for image watermarking in terms of noise performance, robustness against attacks, and payload. In addition, its implementation is very simple and the computation speed is fast. Compared to holographic transform domain method and the conventional spread spectrum watermarking scheme, the proposed technique is shown to be superior.
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.
17170661
On finite automata with limited nondeterminism
Abstract. We develop a new algorithm for determining if a given nondeterministic finite automaton is limited in nondeterminism. From this, we show that the number of nondeterministic moves of a finite automaton, if limited, is bounded by 
$2^{n} - 2$ where 
$n$ is the number of states. If the finite automaton is over a one-letter alphabet, using Gohon's result the number of nondeterministic moves, if limited, is less than 
$n^{2}$. In both cases, we present families of finite automata demonstrating that the upper bounds obtained are almost tight. We also show that the limitedness problem of the number of nondeterministic moves of finite automata is PSPACE-hard. Since the problem is already known to be in PSPACE, it is therefore PSPACE-complete.

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.
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.