This paper deals with statistical mechanical characteristics of fuzzy clustering regularized with fuzzy entropy. We obtain the Fermi-Dirac distribution function as a membership function by regularizing the fuzzy c-means with fuzzy entropy. Then we formulate it as a direct annealing clustering, and examine the meanings of Fermi-Dirac function and fuzzy entropy from a statistical mechanical point of view, and show that this fuzzy clustering method is none other than the Fermi-Dirac statistics.

Two ways of introducing alternation for context-free grammars and pushdown automata are compared. One is the usual way which combines "states" with alternation [1], [4], [7], and the other is the way used in [6] to define the alternating context-free grammar, i.e., alternation is governed by the variables of the grammar. In this paper the latter way is taken over to define a new type of alternating pushdown automaton by combining the "pushdown symbols" of the pushdown automaton with alternation. We have derived a characterization of the original alternating context-free grammars in terms of such a new type of alternating pushdown automaton without states. It is also shown that, if (non-alternating) states are introduced as an additional feature for this type of pushdown automaton, then the resulting alternating pushdown automaton has exactly the same expressive power as the original alternating pushdown automaton.

This paper concerns the Geffert normal forms for phrase structure grammars. We first generalize them to have a new formulation of minimal linear grammars with cancellation productions, called "cancel minimal linear grammars". Then the generative powers of some classes of those grammars are investigated. It is shown that the class of languages generated by grammars with a unique {AB}-cancellation production properly includes the class of linear languages, while it is included in the class of context-free languages. Furthermore, the corresponding class of languages generated by grammars with a unique {AA}-cancellation production is shown to be a proper subclass of linear languages.

In arithmetic circuits for digital signal processing, radixes other than two are often used to make circuits faster. In such cases, radix converters are necessary. However, in general, radix converters tend to be complex. This paper considers design methods for p-nary to binary converters. First, it considers Look-Up Table (LUT) cascade realizations. Then, it introduces a new design technique called arithmetic decomposition by using LUTs and adders. Finally, it compares the amount of hardware and performance of radix converters implemented by FPGAs. 12-digit ternary to binary converters on Cyclone II FPGAs designed by the proposed method are faster than ones by conventional methods.

We consider an initialization problem in single-hop radio networks. The initialization is the task of assigning distinct ID numbers to nodes in a network. We have greatly improved the previous results [10] for the initialization in an n-node network. We propose randomized initialization algorithms in two cases. The first case is that n is known to all the nodes and the second case is that n is unknown to all the nodes. The algorithm for the first case completes in en+ln n+O (1) expected time slots, and the algorithm for the second case completes in en+O() expected slots. The main idea of the algorithm for the case that n is unknown is presumption of the number of nodes. In the algorithm, each node presumes the number of nodes efficiently and is assigned ID by using the algorithm for the case that n is known with the presumption value.

An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.

This paper proposes a Mel-Wiener filter to enhance Mel-LPC spectra in the presence of additive noise. The transfer function of the proposed filter is defined by using a first-order all-pass filter instead of unit delay. The filter coefficients are estimated based on minimization of the sum of the square error on the linear frequency scale without applying the bilinear transformation and efficiently implemented in the autocorrelation domain. The proposed filter does not require any time-frequency conversion, which saves a large amount of computational load. The performance of the proposed system is comparable to that of ETSI AFE. The optimum filter order is found to be 3, and thus filtering is computationally inexpensive. The computational cost of the proposed system except VAD is 53% of ETSI AFE.

The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.

We previously proposed a rotation-spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.

Nuclear power plants (NPPs) must be maintained periodically. Their maintenance efficiency must be improved and human error must be reduced simultaneously to improve NPPs' competitive capability in electricity markets. Although Augmented Reality (AR) offers great possibilities to support NPP maintenance work, some difficulties exist for application of AR to actual work support because current AR systems cannot be implemented in NPP environments without technical improvement. There are several kinds of problems such as recognition distance, tracking accuracy, and a complex working environment when applying AR to NPP field work support. Considerable extension of tracking distance and improvement of accuracy are particularly desired because NPPs are large-scale indoor environments. This study designed a linecode marker, a new type of paper-based marker, along with recognition and tracking algorithms for it to resolve these problems. In contrast to conventional paper-based markers, such as square markers and circle markers, the linecode marker is not merely easier to set up in complex industrial environments: it also enables the use of AR in industrial plants because of its considerable tracking-performance improvement. To evaluate tracking accuracy, the trackable distance, and the tracking speed of the proposed tracking method, an evaluation experiment was conducted in a large room. The experiment results show that the tracking distance is extended extremely over that of the traditional marker-based tracking method: tracking accuracy improved to 20 cm over a 10 m distance. The running speed of the tracking can be as fast as 15 frames per second using a laptop PC.

In this paper, we introduce a syntactically embedded (s-embedded) language, and consider its principal congruence. The following three results are proved, where P_L is the principal congruence of a language L, and W(L) is the residual of L. (1) For a language K, s-embedded in M, K is equal to a P_M class. (2) For a language K, s-embedded in an infix language M, K is equal to a P_W(M) class. (3) For a nonempty s-embedded language L, if L is double-unitary, then L is equal to a P_W(M) class. From the above results, we can obtain those for principal congruence of some codes. For example, Lⁿ is equal to a P_Lⁿ⁺¹ class for an inter code L of index n.

With advances in ubiquitous environments, user demand for easy data-lookup is growing rapidly. Not only users but intelligent ubiquitous applications also require data-lookup services for a ubiquitous computing framework. This paper proposes a backward-compatible, searchable virtual file system (S-VFS) for easy data-lookup. We add search functionality to the VFS, the de facto standard abstraction layer over the file system. Users can find a file by its attributes without remembering the full path. S-VFS maintains the attributes and the indexing structures in a normal file per partition. It processes queries and returns the results in a form of a virtual directory. S-VFS is the modified VFS, but uses legacy file systems without any modification. Since S-VFS supports full backward compatibility, users can even browse hierarchically with the legacy path name. We implement S-VFS in Linux kernel 2.6.7-21. Experiments with randomly generated queries demonstrate outstanding lookup performance with a small overhead for indexing.

In this paper, we present a novel method to incorporate metadata into data mining. The method has many advantages. It can be completed automatically and is independent of a specific database. Firstly, we convert metadata into ontology. Then input a rule set to a reasoner, which supports rule-based inference over the ontology model. The outputs of the reasoner describe the prior knowledge in metadata. Finally, incorporate the prior knowledge into data mining.

In this paper, we propose an efficient resource discovery scheme for large-scale ubiquitous computing environments, which supports scalable semantic searches and load balancing among resource discovery resolvers. Here, the resources are described based on the concepts defined in the ontological hierarchy. To semantically search the resources in a scalable manner, we propose a semantic vector space and semantic resource discovery network in which the resources are organized based on their respective semantic distances. Most importantly, landmarks are introduced for the first time to reduce the dimensionality of the vector space. Computer simulation with CAN verifies the effectiveness of the proposed scheme.

Traffic markers differentiate among packets from senders based on their service profile in the differentiated service networks. Researchers have previously revealed that the existing marking mechanism causes the unfairness in aggregates. This study presents a new marking algorithm. Simulation results demonstrate that the fairness of the proposed scheme exceeds that of SRTCM, TRTCM, TSWTCM and ITSWTCM for medium to high network provision levels.

In this letter, we propose a enhanced framework for a Personalized User Interface (PUI). This framework allows users to access and customize virtual objects in virtual environments in the sense of sharing user centric context with virtual objects. The proposed framework is enhanced by integrating a unified context-aware application for virtual environments (vr-UCAM 1.5) into virtual objects in the PUI framework. It allows a virtual object to receive context from both real and virtual environments, to decide responses based on context and if-then rules, and to communicate with other objects individually. To demonstrate the effectiveness of the proposed framework, we applied it to a virtual heritage system. Experimental results show that we enhance the accessibility and the customizability of virtual objects through the PUI. The proposed framework is expected to play an important role in VR applications such as education, entertainment, and storytelling.

The present paper describes a quality enhancement of speech corrupted by additive background noise in a single channel system. The proposed approach is based on the introduction of perceptual criteria using a frequency-weighting filter in a subtractive-type enhancement process. This newly developed algorithm allows for an automatic adaptation in the time and frequency of the enhancement system and finds a suitable noise estimate according to the frequency of the corrupted speech. Experimental results show that the proposed approach can efficiently remove additive noise related to various types of noise corruption.

In recent years, network analysis has revealed that some real networks have the properties of small-world and/or scale-free networks. In this study, a simple Genetic Algorithm (GA) is regarded as a network where each node and each edge respectively represent a population and the possibility of the transition between two nodes. The characteristic path length (CPL), which is one of the most popular criteria in small-world networks, is derived analytically and shows how much the crossover operation affects the path length between two populations. As a result, the crossover operation is not so useful for shortening the CPL.