bluuebunny/crossref_metadata_2025_split huggingface dataset:
Image registration method using adaptive nonlinear filter
Authors: Morishita Koichi , Yokoyama Tetsuo 
Published: July 2007

AbstractThe subtraction technique is applied widely in various fields. It performs the subtraction between images with different imaging conditions. In medicine, angiography called DSA is well known. However, it has a problem in the alignment of images to cope with the motion of the patient. The authors have developed a highly accurate alignment registration method to cope with such changes of position. This method is based on template matching and uses the adaptive filter in the preprocessing. However, the problem with template matching is that the accuracy of the positioning as well as the image quality is degraded in the region (flat region) where the gray‐level variation of the image is small. This paper proposes a system in which the boundary such as that of the vessel is emphasized in the pre‐processing, and the highly accurate registration is realized in the entire region of the image. To determine the filter which is effective in the preprocessing, a quantitative evaluation was made for the traditional differential filter and the frequency‐domain filter. The adaptive filter based on local statistics is applied, and it is shown that, compared with the traditional filter, the error caused by misregistration can be reduced to 1/2 ‐ 1/7. Then it is shown experimentally that the optimum window size (W) and the optimum emphasis factor (α) are W = 3 ‐ 7 and α ≥ 10 (5122 pixel size), respectively, and a high‐speed processing comparable to the speed of the differential filter can be realized.
Test effort‐dependent software reliability growth models and comparisons of goodness‐of‐fit
Authors: Yamada Shigeru 
Published: July 2007

AbstractTesting in software development is very important since it is the final refining stage before realizing the software to the user and requires a large amount of development resources. In this paper, the time‐dependent behavior of the test effort, such as the manpower spent in the testing process, is represented by exponential and Rayleigh functions, and the software reliability growth model is discussed, which includes the test‐effort functions. The nonhomogeneous Poisson process is assumed as the stochastic principle describing the software error‐detection phenomenon in the testing procedure. Especially, the variance analysis is discussed to determine the significance of the estimation of the test‐effort function by the least‐square method, as well as the prediction of the test effort invested at an arbitrary testing time. It is shown that the software reliability measure derived from the proposed model is a function of the invested test effort. Not only is the point estimation presented, but also the interval estimation. Examples are shown where the proposed model is applied to the actually observed test data, and the goodness‐of‐fit is compared with the existing software reliability growth models. As a result, the goodness‐of‐fit of the proposed model is verified, where the effect of the invested test effort is considered.
Multivalued input, two‐valued output logical element as a two‐valued universal logic module
Authors: Haga Takahiro , Fukumura Teruo 
Published: July 2007

AbstractThis paper proposes to use a multiple‐valued (p‐valued) input, 2‐valued output logical element ((p, 2)‐logical element) for the construction of a 2‐valued ULM (Universal Logic Module). The (p, 2)‐logical element can conveniently be programmed to work as an expected 2‐valued logical function by appropriately selecting two input values among p values. The (p, 2)‐logical element as a ULM also has some preferable points such that the interconnections between ULM's can be decreased in VLSI, compared with the ULM in usual 2‐valued logic.
An extended syllogistic system with conjunctive and complementary terms, and its completeness proof
Authors: Nishihara Noritaka , Morita Kenichi 
Published: July 2007

AbstractAristotle's (Categorical) Syllogism is a logic system in which a new proposition is derived from several propositions stating the relation among “terms” (corresponding to common nouns). It can directly treat a term itself, and has a unique character different from predicate logic where its semantics is constructed based on “individuals”. Therefore, syllogism has several advantages as a logic system for processing a general knowledge such as one expressed by a natural language. However, the expressive ability of classical syllogism is poor. In particular, it can treat only atomic terms as primitive objects. Previously, we proposed an extended syllogistic system with the term operator of conjunction. This paper is a further extension of that system. We introduce a syllogistic system which can treat compound terms produced by using the term operators of conjunction, disjunction and complementation. A set of axioms and an arithmetic interpretation for this system are given, and its completeness is proved.
A fast digital search algorithm using a double‐array structure
Authors: Aoe Jun‐Ichi 
Published: July 2007

AbstractThis paper presents an efficient digital search algorithm by introducing a new internal array structure called a double‐array that combines the fast access of a matrix form with the compactness of a list form. Each arc of a digital search tree called a DS‐tree can be computed from the double‐array inO(1) time; that is, the worst‐case time complexity for retrieving a key becomesO(k)for the lengthkof that key. The double‐array is modified to make the size compact while maintaining fast access and algorithms for retrieval, insertion and deletion are presented. Suppose that the size of the double‐array isn+cm, wherenis the number of nodes of the DS‐tree,mis the number of input symbols, andcis a constant depending on each double‐array. Then it is proved theoretically that the worst‐case times of deletion and insertion are proportional tocmandcm2, respectively, independent ofn. From the experimental results of building the double‐array incrementally for various sets of keys, it is shown that the constantchas an extremely small value, ranging from 0.17 to 1.13.
A virtual optical disk method to realize rewritability and revision control on a write‐once optical disk
Authors: Member Takashi Yokozeki Associate , Namiki Mitarou , Nakagawa Masaki , Members Nobumasa Takahashi 
Published: July 2007

AbstractThis paper proposes a virtual optical disk method which uses the write‐once optical disk as a rewritable secondary memory on existing operating systems. The method also provides revision history of all files on an optical disk. The virtual disk method realizes a virtually rewritable disk (virtual disk), using the write‐once optical disk. The rewriting is simulated using the disk sector as the unit. It completes re‐writability and revision control without additional rewritable memory devices such as magnetic disks. The method is actually built into an OS for a personal computer, and the access speed of a virtual disk is made almost equal to that of a floppy disk by adding an optical disk cache memory. The virtual disk method has a merit in that it can cope with write‐errors in a write‐once optical disk, which may occur consecutively at writing process. Thus, it is seen that the virtual optical disk method is a useful means of this medium management.
Note on the efficiency of methods to generate quasi‐random sequences
Authors: Yamamoto Hiroaki 
Published: July 2007

AbstractThere have been several researches on how to generate quasi‐random sequences from semirandom sources (SRS). Recently, SRSs have been studied and generalized such that they output an element over an arbitrary finite set. However, the existing generator outputs one element from ω ((logn)2) inputs and uses ω ((logn)2) SRSs. This paper presents more efficient methods to generate quasi‐random sequences. This method uses ω (logn) inputs to make one output.
Learning of linear associative mapping by latticed network circuits
Authors: Utsugi Akio , Ishikawa Masatoshi 
Published: July 2007

AbstractA latticed network circuit is proposed for realizing learning of linear associative mapping as a method to realize analog implementation of neural network algorithms. Latticed network circuits considered herein have a structure in which one end of the current source arranged in a line is connected with a neighboring current source via register lines and the other end is grounded. It is shown that a latticed network circuit with nonuniform register distribution gives a solution to the Dirichlet‐type boundary value problem of a Poisson equation discretized by unequal spacing as its node voltage. From this and the aquation corresponding to Green's Theorem, an equation is derived to obtain a linear sum for input current distribution as the difference of boundary current. Based on this equation, a latticed network for computing linear mapping is constructed. Next, a procedure is obtained for learning an optimal linear associative mapping by altering the delta rule. Finally, comparison is made with the existing circuit for computing linear associative mapping.
Gray‐level image recognition based on multiple cell‐features
Authors: Sano Mutsuo , Meguro Shinichi , Ishii Akira 
Published: July 2007

AbstractThis paper proposes a gray‐level image recognition algorithm based on multiple cell‐features for general‐purpose model matching using the generalized Hough transform, which removes the instability of conventional edge‐segment‐based feature description and simplifies its feature matching.Image features are derived in terms of four basic representations: density; edge direction; edge density; and global edge configuration. These are integrated into a multidimensional feature vector for each “cell,” which is a sampled subregion of the image. This method provides powerful and stable description since it describes contour and area properties as well as local and global features. It also allows the effective use of generalized Hough transform matching.Multiple cell features are extracted systematically by combining three fundamental operations. These consist of two feature extraction operations, extended convolution and radial traverse probing, and a data compression operation which converts pixel features to cell features by calculating a histogram of extracted features for each cell. Feature dimensionality is decreased efficiently by selecting feature kinds and salient cell positions based on the F‐ratio calculated from training samples. Using the gray‐level images of prepaid telephone cards, a recognition experiment with 20 different card images and a positioning experiment with overlapping cards has been carried out.
Decentralized priority encoder and its applications
Authors: Maeda Toshiyuki , Sato Yoichiro , Yokohira Tokumi , Okamoto Takuji 
Published: July 2007

AbstractThis paper contains the realization of the decentralized priority encoder (DPE) and presents two application examples. DPE is constructed by placing a module for each device issuing a request and by connecting the outputs from the modules using wired‐OR. Each module has the function to modify the priority dynamically. The request signal from the corresponding device is connected to the input of the module.When the priority of the module M issuing the request is higher than those of any other modules issuing requests, M produces the priority of its request as a binary code on the bus. A data selector can be constructed using DPE, since the data with the highest priority can be selected from a set of data with priorities. This data selector is suited especially to the data selector for image data in the enhanced multiwindow system. Using DPE as the priority comparator, a decentralized independent‐request arbiter with priority is obtained.
Parallel processing scheme of a basic block in a fortran program on oscar
Authors: Honda Hiroki , Kasahara Hironori , Kasahara Hironori , Narita Seinosuke 
Published: July 2007

AbstractWith the development of the supercomputer with multiprocessors, the parallel processing of a Fortran program on the multiprocessor system is considered interesting.This paper proposes a parallel processing scheme for the Fortran program where the assignment unit (task) to the processor is the processing of an arithmetic substitution statement. The implementation and performance evaluation of the proposed scheme on the actual system are reported.In the proposed scheme, the arithmetic substitution statement in the basic block is defined as the task, and the precedence constraints among the tasks due to data dependencies are determined. Based on the derived constraints, the allocation of the tasks to the processors as well as the execution order are determined at the compiling stage, using the multiprocessor scheduling algorithm. Then the codes for the processors are generated and the parallel processing is executed.The proposed scheme was implemented on an actual system and the performance was evaluated. The effect of the parallel processing is manifested. In the conventional loop parallel processing, the effect of the parallel processing cannot be expected for the basic block in the loop or for the basic block of the scalar operation unit outside the loop. On the other hand, it was verified that the parallel processing of those blocks can be realized by applying the proposed method.
Learning of neural networks using their adjoint systems
Authors: Matsuoka Kiyotoshi 
Published: July 2007

AbstractThe backpropagation method is well known in the neural network and was proposed originally as a learning algorithm for the layered network composed of static nonlinear units. On the other hand, for problems such as those in control and signal processing where the time series is considered, the learning must be discussed in the network composed of dynamic units and containing feedback loops.Pearlmutter discussed such a case and showed that a learning algorithm can be derived by introducing the adjoint system for the system obtained by linearizing the network. This is a natural extension of the ordinary backpropagation method.This paper extends the method further and shows that a powerful learning algorithm is obtained for a very general neural network and evaluation function. By employing the proposed method, it is possible to realize the network with connection coefficients with a spatial structure, to determine the initial state of the network and to execute the learning for the input, not only for the connection coefficients.
A Method of Information Integration by Multilayer Neural Networks and Its Theoretical Study
Authors: Yonekura Tatsuhiro , Yokoi Shigeki , Toriwaki Jun‐Ichiro 
Published: July 2007

AbstractThis paper proposes a method for concentrating multivariate data (dimensionality reduction) using a neural network model. Specifically, a pulse‐input pattern‐output network (PPN), i.e., a multilayer network in which the input is presented as a pulsed input (a single true input with the others being held to zero Here, by pulsed input, we mean that only one component of the input vector is presented with a value of 1 and has no connection with pulsed signals in the time domain.), is employed and the N‐dimensional training pattern is presented at the outputs. Using backpropagation learning in a PPN, it is demonstrated mathematically that subject to a certain set of conditions, the dimension of the sample space can be reduced arbitrarily while preserving optimality. That capability is compared with principal component analysis (K‐L expansion), and it is demonstrated that the nonlinear dimensionality reduction capabilities of the model by means of a component analysis experiment employ the iris data—a data set which is well known in the field of multivariate analysis.
Functional model‐based assistance for modifying software
Authors: Yamada Hiroyuki , Tezuka Yoshikazu 
Published: July 2007

AbstractWhen a part of software is modified, the side effect due to the modification is propagated to other parts, necessitating the reexamination of the whole software. As a result of this situation, it is not easy to reuse the software by modification.This paper assumes the situation where the side effect due to modification (interaction) is produced by the functional relations among the modules (objects) composing the software and discusses the whole framework (representation of interaction information and the assist mechanism) for the modification‐assist by specifying the interactions due to those relations.The proposed form for representation (functional model) explicitly represents the functional relations among the objects, using the node to represent the object and the link to represent the functional relation. At each node, the concept related to the interaction can be described directly to realize the modification‐assist facility. By this scheme, the information can be described at a higher concept level, which simplifies the human description of the functional model.In this approach, the object of description is restricted to the functional relations among the objects. Since the object of description is clearly specified, it is easy to construct a knowledge‐base for the software. A system was constructed which can specify the interaction based on the functional model at the time of the software modification. Through a modification example of ferry simulation software, the usefulness of the proposed system is demonstrated.
A restoration method of medical x‐ray images based on an extended regression analysis method
Authors: Ohta Mitsuo , Ikuta Akira , Mitani Yasuo , Kodera Yoshie , Ogawa Masaaki , Fujita Minoru , Wada Takuro 
Published: July 2007

AbstractIn this paper, a restoration method for the X‐ray images with optical blurs and quantum mottles is proposed by considering each physical mechanism of the medical X‐ray image formation. More specifically, the optical blurs are first deterministically cleared away by using the transfer characteristic of the laser scanner, the characteristic curve of the film, the logarithmic transformation of the optical density and a pseudo‐inverse filter based on the point spread function. Next, as a restoration method for remaining quantum mottles, a new extended regression model that may have a little information lack and a simple experimental regression model are derived on the basis of a Bayes' theorem. Thus the quantum mottles were statistically removed. Finally, the restoration method of X‐ray images proposed in this paper is applied to those of the actual dental images, and dentists visually evaluate and confirm the effectiveness of these processed images.
A formal description of monitors by CCS
Authors: Yuen Shoji , Sakabe Toshiki , Inagaki Yasuyoshi 
Published: July 2007

AbstractThis paper formalizes the operation of a monitor—one of the fundamental models for concurrent processing—in a framework which is an extension of CCS (the calculus of communication systems) proposed by Milner. The purpose of this paper is to define the mutual exclusion and synchronization control of a monitor based on formal computation mechanisms and to provide a basis for the verification of the properties of a monitor as a concurrent program.For this purpose, a programming language which directly represents the execution mechanism of the monitor and the syntactical translation from that language to a CCS expression are defined. Through this translation, the mutual exclusion of the monitor procedure as well as synchronization control by means of the conditional variable—the essential features of the monitor—are formalized within the CCS framework. As a result, by adopting an observable equivalence relation between the semantics of CCS operational expressions of the semantics of operation of the model, we show that it is possible to demonstrate the abstraction mechanism of the model.
A neural network model of competitive learning which converges to the global equilibrium
Authors: Masuda Tadashi 
Published: July 2007

AbstractWith regard to competitive learning a neural network model consisting of two layers of neurons was constructed and a new energy (loss) function to ensure that the network will converge uniquely to the desired state is proposed. It was proved that when the number of cells in the hidden layer is equal to the number of the input patterns, this model does not have any local minima of the energy function other than the one that yields the global minimum and, consequently, converges to the global minimum from any initial values of the synaptic connections.In this equilibrium the network is organized to the state where only a single hidden cell responds to its corresponding input pattern. This model is compared with the other models of competitive learning proposed up to the present. Then a model of the associative memory based on this competitive learning model is proposed. In this model of the associative memory only output patterns that are presented during the learning are recalled even in response to an input that has never been presented during the learning. This model has an extra advantage in that the input patterns are requested to be neither orthogonal nor independent of each other.
A diagnostic and observation expert system for large TCP/IP‐based internetworks
Authors: Sugawara Toshiharu 
Published: July 2007

AbstractWith the expanding use of the computer networks based on LAN, there is a rapid increase of fault generation. A tremendous amount of time and effort is required in the dissolution of those faults since the origins of those faults are physically distant and the data are difficult to obtain due to the distributed management. In addition, the prevention, as well as the early detection and remedy of faults, are required since a failure may affect a broad range.The authors have constructed a diagnosis/observation expert system LODES for LAN, by constructing a knowledgebase of the experiences in dissolving the failure. LODES discovers the violation of protocol by referring to the TCP/IP‐related protocols, automatic detection of fault (or its indication) by observing packets, and points out the abnormality/missetting of network‐related information of the host.In the diagnosis, it has the feature that the distributed cooperative problem‐solving is executed by communicating among LODES (large internetwork observation and diagnosis expert system) attached to each of the network segments. LODES is actually operated, and it is shown that the phenomena and failures which have not been detected can now be detected.