In this paper, a new encoding/decoding scheme of multiple-valued separable balanced codes is presented. These codes have 2・m information digits and m・ (R - 2) check digits in radices R 4, 2・m - 1 information digits and m + 1 check digits in R = 3, where code-length n = R・m. In actual use of code-lengths and radices, it is shown that the presented codes are relatively efficient in comparison with multiple-valued Berger codes which are known as optimal unordered codes. Meanwhile, the optimality of multiple-valued Berger codes is discussed.

Recursive Diagonal Torus (RDT) is a class of interconnection network for massively parallel computers with 2¹⁶ nodes. In this paper, message transfer algorithms on the RDT are proposed and discussed. First, a simple one-to-one message routing algorithm called the vector routing is introduced and its practical extension called the floating vector routing is proposed. In the floating vector routing both the diameter and average distance are improved compared with the fixed vector routing. Next, broadcasting and hypercube emulation algorithm scheme on the RDT are shown. Finally, deadlock-free message routing algorithms on the RDT are discussed. By a simple modification of the e-cube routing and a small numbers of additional virtual channels, both one-to-one message transfer and broadcast can be achieved without deadlock.

Intelligent Network (IN) is a distributed architecture allowing telecom companies to create and to customize services. Network and services have to be integrated in the management process provided by the IN. The integration of the Intelligent Network and the TMN (Telecommunications Management Networks) can be useful to achieve the management process. Our proposal is an evolution towards a more intelligent management structure provided through the Distributed Artificial Intelligence concepts.

A trained sparse conjugate codebook is proposed for improving the speech quality of CELP-based coding in a noisy environment. Although CELP coding provides high quality at a low bit rate in a silent environment (creating clean speech), it cannot provide a satisfactory quality in a noisy environment because the conventional fixed codebook is designed to be suitable for clean speech. The proposed codebook consists of two sub-codebooks; each sub-codebook consists of a random component and a trained component. Each component has excitation vectors consisting of a few pulses. In the random component, pulse position and amplitude are determined randomly. Since the radom component does not depend on the speech characteristics, it handles noise better than the trained one. The trained component maintains high quality for clean speech. Since excitation vector is the sum of the two sub-excitation vectors, this codebook handles various speech conditions by selecting a sub-vector from each component. This codebook also reduces the computational complexity of a fixed codebook search and memory requirements compared with the conventional codebook. Subjective testing (absolute category rating (ACR) and degradation category rating (DCR)) indicated that this codebook improves speech quality compared with the conventional trained codebook for noisy speech. The ACR test showed that the quality of the 8 kbit/s CELP coder with this codebook is equivalent to that of the 32 kbit/s ADPCM for clean speech.

A learning algorithm is presented for nearest neighbor pattern classifiers for the cases where mixed supervised and unsupervised training data are given. The classification rule includes rejection of outlier patterns and fuzzy classification. This partially supervised learning problem is formulated as a multiobjective program which reduces to purely super-vised case when all training data are supervised or to the other extreme of fully unsupervised one when all data are unsupervised. The learning, i. e. the solution process of this program is performed with a gradient method for searching a saddle point of the Lagrange function of the program.

This paper introduces a new image representation method that is named the projective image representation (PIR). We consider an image as a collage of symmetric segments each of which can be well represented by its projection data of a single orientation. A quadtree-based method is adopted to decompose an image into variable sized segments according to the complexity within it. Also, we deal with the application of the PIR to the image compression and propose an efficient algorithm, the quadtree-structured projection vector quantization (QTPVQ) which combines the PIR with the VQ. As the VQ is carried out on the projection data instead of the pixel intensities of the segment, the QTPVQ successfully overcomes the drawbacks of the conventional VQ algorithms such as the blocking artifact and the difficulty in manipulating the large dimension. Above all, the QTPVQ improves the subjective quality greatly, especially at low bit rate, which makes it applicable to low bit rate image coding.

Aczel's theory of structured objects is extended under the assumption that a structured object may belong to a sort and that these sorts are partially ordered. Based on the assumption, the forms of required objects can be specified more precisely and concisely. The resulting theory provides a general principle for the construction of order-sorted ontologies and universes of structured objects. It is applicable to systems with structured objects, such as situation theory, feature-based grammars, knowledge representation, constraint logic programming and object-oriented systems.

Existing edge detection methods provide unsatisfactory results when contrast changes largely within an image due to non-uniform illumination. Koch et al. developed an energy function based upon the Hopfield neural network, whose coefficients were fixed by trial and error, and remain constant for the entire image, irrespective of the differences in intensity level. This paper presents an improved edge detection method for non-uniformly illuminated images. We propose that the energy function coefficients for an image with inconsistent illumination should not remain fixed, rather should vary as a second-order function of the intensity differences between pixels, and actually use a schedule of changing coefficients. The results, compared with those of existing methods, suggest a better strategy for edge detection depending upon both the dynamic range of the original image pixel values as well as their contrast.

In this paper, we give a direct proof of the result of Latteux and Turakainen that the full class of recursively enumerable languages can be obtained from minimal linear languages (which are generated by linear context-free grammars with only one nonterminal symbol) by Dyck reductions (which reduce pairs of parentheses to the empty word).

This letter shows that a portable visual stimulator for MEG measurements can be realized using an optical fiber bundle and a CRT display system offering high brightness and high speed raster scanning, and that MEGs with neither magnetic contamination nor jitter can be measured by the stimulator.