Given a text T of length n and a pattern P of length m, the string matching problem is a task to find all occurrences of P in T. In this study, we propose an algorithm that solves this problem in O((n + m)q) time considering the distance between two adjacent occurrences of the same q-gram contained in P. We also propose a theoretical improvement of it which runs in O(n + m) time, though it is not necessarily faster in practice. We compare the execution times of our and existing algorithms on various kinds of real and artificial datasets such as an English text, a genome sequence and a Fibonacci string. The experimental results show that our algorithm is as fast as the state-of-the-art algorithms in many cases, particularly when a pattern frequently appears in a text.

A self-reconfigurable modular robotic system has potential to carry out a wide variety of tasks in various fields and conditions. For realizing such versatileness and robustness, the compositional modules have to be designed so that the combined structure is equipped with high degree of freedom of the self-reconfiguration. We propose a new design of a modular robot using helically magnetized axes. The axes are arranged at all of the edges of the cubic-shaped module and contribute to both connection and transformation among the modules. The mechanism enables the modules to construct a 3-D orthogonal lattice structure, execute slide movement to neighboring vacant grid positions, and change the structural configuration by themselves. This paper firstly describes the conceptual design and fabrication method of the helically magnetized axis. Then, total design and specification of the module are explained. Finally, experimental results to evaluate the performance of connection and transformation are presented.

Various artificial molecular devices, including some made of DNA or RNA , have been developed to date. The next step in this area of research is to develop an integrated system from such molecular devices. A molecular robot consists of sensors, computers, and actuators , all made of molecular devices, and reacts autonomously to its environment by observing the environment, making decisions with its computers, and performing actions upon the environment. Molecular computers should thus be the intelligent controllers of such molecular robots. Such controllers can naturally be regarded as hybrid systems because the environment, the robot, and the controller are all state transition systems having discrete and continuous states and transitions. For modeling and designing hybrid systems, formal frameworks, such as hybrid automata , are commonly used. In this perspective paper, we examine how molecular controllers can be modeled as hybrid automata and how they can be realized in a molecular robot. We first summarize the requirements for such molecular controllers and examine existing frameworks of DNA computing with respect to these requirements. We then show the possibility of combining existing frameworks of DNA computing to implement a sample hybrid controller for a molecular robot.

In this paper we outline a multimodal interaction description language XISL (Extensible Interaction-Sheet Language) that is developed to describe multimodal interactions (MMI), and to separate the description of interactions from XML contents. XISL makes an XML document independent of interactions that may differ between each terminal, and so enables such seamless services as web-browsing to be constructed easily. Since XISL also provides various combinatorial usage of modalities, a developer can describe a MMI scenario easily. We implemented an interpreter of XISL on prototype systems for multimedia lectures with different types of MMI and proved the viability of XISL by experiments using the systems.

This paper presents an efficient algorithm for learning in the limit a special type of regular languages, called strictly locally testable languages from positive data, and its application to identifying the protein /spl alpha/-chain region in amino acid sequences. First, we present a linear time algorithm that, given a strictly locally testable language, learns its deterministic finite state automaton in the limit from only positive data. This provides one with a practical and efficient method for learning a specific concept domain of sequence analysis. We then describe several experimental results using the learning algorithm developed above. Following a theoretical observation which strongly suggests that a certain type of amino acid sequences can be expressed by a locally testable language, we apply the learning algorithm to identifying the protein /spl alpha/-chain region in amino acid sequences for hemoglobin. Experimental scores show an overall success rate of 95% correct identification for positive data, and 96% for negative data.

Speech speed is measured and displayed with our specific algorithm TEMAX (Temporal Evaluation and Measurement Algorithm by KS). The TEMAX-gram, a sonagraphic output of speech envelope, the DFT using a 1-second window is convenient to set off isosyllabic characteristics. For Japanese traces 2 dark bars, called rhythmic formants: RF1 and RF2: the first one, around 8 Hz, and the second one, at halfway. RF1 corresponds to speech rate, RF2 represents the bimoraic rhythmic foot. As far as English, its isochronic characteristics are observable with a 2-seconds window as RF1. Furthermore, using a 1-second window the periodicity of syllables between stress is displayed as RF2.