In this paper, Wavelength Division Multiple access (WDM) ring is proposed for interconnection in workstation clusters or parallel machines. This network consists of ring connected routers each of which selectively passes signals addressed in some particular wavelengths. Other wavelengths are once converted to electric signals, and re-transmitted being addressed in different wavelengths. Wavelengths are assigned to divisors of the number of nodes in the system. Using the regular WDM ring with imaginary nodes, the diameter and average distance are reduced even if the number of nodes has few divisors. It provides better diameter and average distance than that of the uni-directional torus. Although the diameter and average distance is worse than that of ShuffleNet, the physical structure of the WDM ring is simple and the available number of nodes is flexible.

A computational model for security verification of cryptographic protocols is proposed. Until most recently, security verification of cryptographic protocols was left to the protocol designers' experience and heuristics. Though some formal verification methods have been proposed for this purpose, they are still insufficient for the verification of practical real-time cryptographic protocols. In this paper we propose a new formalism based on a term rewriting system approach that we have developed. In this model, what and when the saboteur can obtain is expressed by a first-order term of a special form, and time-related concepts such as the passage of time and the causality relation are specified by conditional term rewriting systems. By using our model, a cryptographic protocol which was shown to be secure by the BAN-logic is shown to be insecure.

In many applications, information retrieval is a very important research field. In several key strategies, the binary trie is famous as a fast access method able to retrieve keys in order. Especially, a Patricia trie gives the shallowest trie by eliminating all nodes which have only one arc, and it requires the smallest storage among the other trie structures. If trie structures are implemented, however, the greater the number of the registered keys, the larger storage is required. In order to solve this problem, Jonge et al. proposed a method to change the normal binary trie into a compact bit stream. This paper proposes the improved trie representation for the Patricia trie, as well as the methods for searching and inserting the key on it. The theoretical and experimental results, using 50,000 Japanese nouns and 50,000 English words, show that this method generates 25-39 percent shorter bit streams than the traditional method. This method, thus, enables us to provide more compact storage and faster access than the traditional method.

This paper proposes a new methodology for diagnosing transistor leakage faults with information on I_DDQ and logic values at primary output lines. A hierarchical approach is proposed to identify the faults that do not exist in the circuit through comparing their I_DDQ and logic behaviors predicted by simulation with observed responses. Several techniques for handling intermediate faulty voltages in fault simulation are also proposed. Further, an approach is proposed to generate diagnostic vectors based on I_DDQ information. In addition, a method for identifying I_DDQ equivalent faults is proposed to reduce the time needed for diagnostic vector generation and to improve diagnostic resolution. Experimental results show that the proposed methodology often confines diagnosed faults to only a few gates.

Segmenting the images obtained from magnetic resonance imaging (MRI) is an important process for visualization of the human soft tissues. For the application of MR, we often have to introduce a reasonable segmentation technique. Neural networks may provide us with superior solutions for the pattern classification of medical images than the conventional methods. For image segmentation with the aid of neural networks of a reasonable size, it is important to select the most effective combination of secondary indices to be used for the classification. In this paper, we introduce a vector quantized class entropy (VQCCE) criterion to evaluate which indices are effective for pattern classification, without testing on the actual classifiers. We have exploited a newly developed neural tree classifier for accomplishing the segmentation task. This network effectively partitions the feature space into subregions and each final subregion is assigned a class label according to the data routed to it. As the tree grows on, the number of training data for each node decreases, which results in less weight update epochs and decreases the time consumption. The partitioning of the feature space at each node is done by a simple neural network; the appropriateness of which is measured by newly proposed estimation criterion, i. e. the measure for assessment of neuron (MAN). It facilitates the obtaining of a neuron with maximum correlation between a unit's value and the residual error at a given output. The application of this criterion guarantees adopting the best-fit neuron to split the feature space. The proposed neural classifier has achieved 95% correct classification rate on average for the white/gray matter segmentation problem. The performance of the proposed method is compared to that of a multilayered perceptron (MLP), the latter being widely exploited network in the field of image processing and pattern recognition. The experiments show the superiority of the introduced method in terms of less iterations and weight up dates necessary to train the neural network, i. e. lower computational complexity; as well as higher correct classification rate.

Handprinted Chinese character recognition (HCCR) can be classified into two major approaches: statistical and structural. While neither of these two approaches can lead to a total and practical solution for HCCR, integrating them to take advantages of both seems to be a promising and obviously feasible approach. But, how to integrate them would be a big issue. In this paper, we propose an integrated HCCR system. The system starts from a statistical phase. This phase uses line-density-distribution-based features extracted after nonlinear normalization to guarantee that different writing variations of the same character have similar feature vectors. It removes accurately and efficiently the impossible candidates and results in a final candidate set. Then follows the structural phase, which inherits the line segments used in the statistical phase and extracts a set of stroke substructures as features. These features are used to discriminate the similar characters in the final candidate set and hence improve the recognition rate. Tested by using a large set of characters in a handprinted Chinese character database, the proposed HCCR system is robust and can achieve 96 percent accuracy for characters in the first 100 variations of the database.

This paper describes an automatic region segmentation method which is detectable nuclei regions from hematoxylin and eosin (HE)-stained breast tumor images using artificial organisms. In this model, the stained images are treated as virtual environments which consist of nuclei, interstitial tissue and background regions. The movement characteristics of each organism are controlled by the gene and the adaptive behavior of each organism is evaluated by calculating the similarities of the texture features before and after the movement. In the nuclei regions, the artificial organisms can survive, obtain energy and produce offspring. Organisms in other regions lose energy by the movement and die during searching. As a result, nuclei regions are detected by the collective behavior of artificial organisms. The method developed was applied to 9 cases of breast tumor images and detection of nuclei regions by the artificial organisms was successful in all cases. The proposed method has the following advantages: (1) the criteria of each organism's texture feature values (supervised values) for the evaluation of nuclei regions are decided automatically at the learning stage in every input image; (2) the proposed algorithm requires only the similarity ratio as the threshold value when each organism evaluates the environment; (3) this model can successfully detect the nuclei regions without affecting the variance of color tones in stained images which depends on the tissue condition and the degree of malignancy in each breast tumor case.