Dynamic systems theory provides a framework for understanding and modeling systemic phenomena. Yet, dynamic systems concepts are difficult for novices to master. The difficulties of learning dynamic systems concepts include the abstract nature of systems principles, the counterintuitive system behaviors, and the challenge of observing these behaviors directly. Hence, effective systems education is confined to specialized higher-level courses. To address these difficulties and make introductory systems education more accessible, this work-in-progress paper presents a tangible learning kit inspired by the MIT Differential Analyzer, a mechanical analog computer developed by Vannevar Bush in the 1930s. The learning kit employs mechanical analogies to enable the real-time, hands-on modeling and simulation of dynamic systems. Learners can physically assemble computing modules to represent systems and directly observe emergent behaviors. By materializing abstract concepts, the kit makes system concepts tangible for novices, aiming to foster learning engagement and construct a foundational understanding of systems.

The right to fully participate into the community and enjoy the life is the same for people with or without a visual impairment. However, many existing research for assistive products or technology for visually impaired people are piecemeal, because there is no holistic and systemic research focus on the visually impaired people--s travelling experience. We propose the SECPT Framework which based on the visually impaired people--s travelling experience, can be a starting point of the design process for designers to encounter visually impaired people, and enable them to quick understanding the target group and determine their research area.

Motion graphs are regarded as a promising technique for interactive applications. However, the graphs are generated based on the distance metric of whole body, which produce a limit set of possible transitions. In this paper, we present an automatic method to construct a new data structure that specifies transitions and correlations between partial-body motions, called Synchronized Partial-body Motion Graphs (SPbMGs). We exploit the similarity between lower-body motions to create synchronization conditions with upper-body motions. Under these conditions, we generate all possible transitions between partial-body motions. The proposed graph representation not only maximizes the reusability of motion data, but also increases the connectivity of motion graphs while retaining the quality of motion.

Most of the digital musical instruments developed so far were specifically designed for music performances and the interfaces are disruptive. They are not generally available and some of them were made with expensive technologies, making them inaccessible to general users. This paper presents an attempt to make use of Wii Controller(or Wiimote), a low cost and readily available game controller developed by Nintendo to develop an interactive music performance system on PC platform. It employs analytical techniques to study the motion data captured, recognizes and maps the detected gestures meaningful and interesting to music performance.

Prior research attempts to formalize the structure of object-oriented design patterns for a more precise specification of design patterns. It also allows automation support to be developed for user-defined design patterns in the future CASE tools. Targeting to a particular type of automation (e.g. verification of pattern instances), previous specification approaches over-specify pattern structures to a certain extend. Over-specification makes pattern specification ambiguous and disallows the specification language to be used for specifying compound patterns. In this paper, we present the structural properties of design patterns which reveal the true abstract nature of pattern structures. To support these properties so as to solve the over-specification problem, we propose an extension to UML 1.5 (basically UML 1.4 with Action semantics). The specialization and refining mechanism of UML provides also a smooth support for the instantiation, refinement and integration of pattern structures specified in UML. Our work makes no significant extension to the UML 1.5 meta-model but more in a UML Profile approach to ease the migration of our work to UML 2.0, which has not yet officially released by OMG during this work.

Prior research suggested modeling languages for precise specification of design pattern structures and behaviors. However, seldom has put effort on their integrations as well as their specifications. To provide a first class CASE support to the recognition, verification and application of design patterns as well as their compounds, a precise specification to their leitmotifs is critical. In this paper, we present the essentials of pattern integration and propose an extended version, we name it exLePUS, to a pattern specification language (LePUS) in order to support the specification of these essentials and thus compound patterns. A case study has illustrated how it is used a well-known compound pattern.

Although the "neural-gas" network proposed by Martinetz et al. in 1993 has been proven for its optimality in vector quantizer design and has been demonstrated to have good performance in time-series prediction, its high computational complexity (NlogN) makes it a slow sequential algorithm. We suggest two ideas to speedup its sequential realization: (1) using a truncated exponential function as its neighborhood function and (2) applying a new extension of the partial distance elimination method (PDE). This fast realization is compared with the original version of the neural-gas network for codebook design in image vector quantization. The comparison indicates that a speedup of five times is possible, while the quality of the resulting codebook is almost the same as that of the straightforward realization.

In this paper, we study a qualitative property of a class of competitive learning (CL) models, which is called the multiplicatively biased competitive learning (MBCL) model, namely that it avoids neuron underutilization with probability one as time goes to infinity. In the MBCL, the competition among neurons is biased by a multiplicative term, while only one weight vector is updated per learning step. This is of practical interest since its instances have computational complexities among the lowest in existing CL models. In addition, in applications like classification, vector quantizer design and probability density function estimation, a necessary condition for optimal performance is to avoid neuron underutilization. Hence, it is possible to define instances of MBCL to achieve optimal performance in these applications.

We propose the distortion sensitive competitive learning (DSCL) algorithm for codebook design in image vector quantization. The algorithm is based on the equidistortion principle for an asymptotically optimal vector quantizer after Gersho (1979) and from Ueda and Nakano (1994). The DSCL is simple and efficient in that a single weight vector update is performed per training vector, and the processing speed of the DSCL in a sequential or multiprocessor environment can further be improved by applying a modified partial distance elimination (MPDE) method. Simulations indicate that the DSCL outperforms some previously proposed neural algorithms, including the "neural-gas" from Martinetz et al. (1993) and the DEFCL from Butler and Jiang (1996). In combining with the MPDE, the DSCL is faster than the "neural-gas" up to a factor of 45 times on a sequential machine, and yet arrives at better codebooks with the same number of iterations.

Self-organizing model has been successfully applied to solving some combinatorial optimization problems, including the travelling salesman problem, the routing problem and the cell-placement problem, but there has not much work reported on its application to solving the graph partitioning problem. We propose a novel mapping which has not been proposed before, with some changes to the original Kohonen's (1982) algorithm so as to enable it to solve a partitioning problem-the bipartite subgraph problem. This new approach is compared with to the maximum neural network for solving the same problem, showing that the performance of our new approach is superior to that of the maximum neural network.

In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, organized in a rectangular grid. Experimental results indicate high accuracy in voting is attainable despite its small memory requirement.

Proposes a new approach which requires neither neuron addition nor deletion, and at the same time, N neurons are sufficient to solve an N-city travelling salesman problem. the authors begin with a description of their model, and then results for applying the model to solve the 30-city problem from Hopfield are presented. Results of practical testing show that the present approach always converges. It has the highest chance to achieve the optimal solution, and gives the best most probable solution, as compared to other self-organizing algorithms.>