An invertible length preserving transducer is called a weakly invertible finite automaton (WIFA for short). If the first letter of any input string of length τ + 1 is uniquely determined by the corresponding output string by a WIFA and its initial state, it is called a WIFA with delay τ. The composition of two WIFAs is the natural concatenation of them. The composition is also a WIFA whose delay is less than or equal to the sum of the delays of the two WIFAs. In this paper we derive various results on a decomposition of a WIFA into WIFAs with smaller delays. The motivation of this subject is from theoretical interests as well as an application to cryptosystems. In order to capture the essence of the decomposability problem, we concentrate on WIFAs such that their input alphabets and their output alphabets are identical. A WIFA with size n of the input and output alphabet is denoted by an n-WIFA. We prove that for any n > 1, there exists an n-WIFA with delay 2 which cannot be decomposed into two n-WIFAs with delay 1. A one-element logic memory cell is a special WIFA with delay 1, and it is called a delay unit. We show that for any prime number p, every strongly connected p-WIFA with delay 1 can be decomposed into a WIFA with delay 0 and a delay unit, and that any 2-WIFA can be decomposed into a WIFA wiht delay 0 and a sequence of k delay units if and only if every state of the 2-WIFA has delay k.

This paper discusses a software verification support environment Vega which is based on a model-theoretic methodology that enables verification support for the temporal properties of protocol specifications described in the formal description technique LOTOS. In the methodology of Vega, a protocol specification is defined through the LOTOS process reflecting its practical system structure. The temporal properties to be verified are given as the requirement which the protocol needs to satisfy from the viewpoint of events and are formulated by using the branching time temporal logic defined in this paper. Verification is done by determining whether or not the given temporal properties are satisfied by the model, which corresponds to the transition system derived from the LOTOS specification of the protocol. Vega is provided with an effective interface function, as well as the function of simple model checking based on the above methodology, to give some degree of flexibility for the expression of temporal properties to be verified. Specifically, it allows the user to define useful expressions by combining builtin temporal logic formulas and enter them in Vega for use at any time. With the provision of these functions, Vega is expected to serve as a very powerful and flexible verification support tool.

In this paper we analyze the reliability of a simple broadcasting scheme for hypercubes (HCCAST) with random faults. We prove that HCCAST (n) (HCCAST for the n-dimensional hypercube) can tolerate Θ(2ⁿ/n) random faulty nodes with a very high probability although it can tolerate only n - 1 faulty nodes in the worst case. By showing that most of the f-fault configurations of the n dimensional hypercube cannot make HCCAST (n) fail unless f is too large, we illustrate that hypercubes are inherently strong enough for tolerating random faults. For a realistic n, the reliability of HCCAST (n) is much better than that of the broadcasting algorithm described in [6] although the latter can asymptotically tolerate faulty links of a constant fraction of all the links. Finally, we compare the fault-tolerant performance of the two broadcasting schemes for n = 15, 16, 17, 18, 19, 20, and we find that for those practical valuse, HCCAST (n) is very reliable.

This paper presents a generalized new approach for testing interconnects (for boundary scan architectures) as well as field programmable interconnect chips (FPICs). This approach relies on a structured walking-1 test set in the sense that a structural analysis based on the layout of the interconnect system, is carried out. The proposed structural test method differs from previous approaches as it explicitly avoids aliasing and confounding and is applicable to dense as well as sparse layouts and in the presence of faults in the programmable devices of a FPIC. The proposed method is applicable to both one-step and two-step test generation and diagnosis. Two algorithms with an execution complexity of O(n²), where n is the number of nets in the interconnect, are given. New criteria for test vector compaction are proposed; a greedy condition is exploited to compact test vectors for one-step and two-step diagnosis. For a given interconnect, the two-step diagnosis algorithm requires a number of tests as a function of the number of faults present, while the one-step algorithm requires a fixed number of tests. Simulation results for benchmark and randomly generated layouts show a substantial reduction in the number of tests using the proposed approaches compared with previous approaches. The applicability of the proposed approach to FPICs as manufactured by [1] is discussed and evaluated by simulation.

In this paper, we propose a new method of dialogue data collection which can be used to evaluate modules of a spoken dialogue system. To evaluate the module, it is necessary to use suitable data. Human-human dialogue data have not been appropriate to module evaluation, because spontaneous data usually include too much specific phenomena such as fillers, restarts, pauses, and hesitations. Human-machine dialogue data have not been appropriate to module evaluation, because the dialogue was unnatural and the available vocabularies were limited. Here, we propose 'Hybrid method' for the collection of spoken dialogue data. The merit is that, the collected data can be used as test data for the evaluation of a spoken dialogue system without any modification. In our method a human takes the role of some modules of the system and the system, also, works as the other part of the system together. For example, humans works as the speech recognition module and the dialogue management and a machine does the other part, response generation module. The collected data are good for the evaluation of the speech recognition and the dialogue management modules. The reasons are as follows. (1) Lexicon: The lexicon was composed of limited words and dependent on the task. (2) Grammar: The intention expressed by the subjects were concise and clear. (3) Topics: There were few utterances outside the task domain. The collected data can be used test data for the evaluation of a spoken dialogue system without any modification.

This paper presents the performance prediction method on sentence recognition system which uses a finite state word automaton. When each word is uttered separately, the relationship between word recognition score and sentence recognition score can be approximated using the number of word sequences at a minimum distance from each sentence in the task. But it is not clear that how we get this number when the finite state word automaton is used as linguistic information. Therefore, we propose the algorithm to calculate this number in polynomial time. Then we carry out the prediction using this method and the simulation to compare with the prediction on the task of Japanese text editor commands. And it is shown that our method approximates the lower limit of sentence recognition score.

This paper proposes a Japanese continuous speech recognition mechanism in which a full-sentence-level context-free-grammar (CFG) and one kind of semantic constraint called dependency relationships between two bunsetsu (a kind of phrase) in Japanese" are used during speech recognition in an integrated way. Each dependency relationship is a modification relationship between two bunsetsu; these relationships include the case-frame relationship of a noun bunsetsu to a predicate bunsetsu, or adnominal modification relationships such as a noun bunsetsu to a noun bunsetsu. To suppress the processing overhead caused by using relationships of this type during speech recognition, no rigorous semantic analysis is performed. Instead, a simple matching with examples" approach is adopted. An experiment was carried out and results were compared with a case employing only CFG constraints. They show that the speech recognition accuracy is improved and that the overhead is small enough.

In previous papers the building of hierarchical networks made up of components using fuzzy rules was presented. It was demonstrated that this approach could be used to construct networks to solve classification problems, and that in many cases these networks were computationally less expensive and performed at least as well as existing approaches based on feedforward neural networks. It has also been demonstrated how this approach could be extended to real-valued problems, such as function approximation and time series prediction. This paper investigates the problem of choosing the best network for real-valued approximation problems. Firstly, the nature of the network parameters, how they are interrelated, and how they affect the performance of the system are clarified. Then we address the problem of choosing the best values of these parameters. We present two model selection tools in this regard, the first using a simple statistical model of the network, and the second using structural information about the network components. The resulting network selection methods are demonstrated and their performance tested on several benchmark and applied problems. The conclusions look at future research issues for further improving the performance of the clustering network.

Semi-Orthogonally Associative Memory neural network model (SAM) uses the orthogonal vectors in Uⁿ = {-1, 1}ⁿ as its characteristic patterns. It is necessary to select the optimum characteristic parameter n so as to increase the efficiency of this model used. This paper investigates the dynamic behavior and error correcting capability of SAM by statistical neurodynamics, and demonstrates that there exists a convergence criterion in tis recalling processes. And then, making use of these results, its optimum characteristic parameter is deduced. It is proved that, in the statistical sense, its recalling outputs converge to the desired pattern when the initial similar probability is larger than the convergence criterion and not true otherwise. For a SAM with N neurons, when its characteristic parameter is optimum, its memory capacity is N/2 ln ln N, the information storage capacity per connection weight is larger than 9/23 (bits/weight) and the radius of attractive basin of non-spurious stable state is about 0.25N. Computer simulations are done on this model and the simulation results are consistent with the results of theoretical analyses.

Caenorhabditis elegans during feeding gives good moving biological images",in which motions of several pulsing organs are superposed on its head swing. A powerful method to extract dynamic features is presented. First step is to use a variance picture VAG4 in order to pick up active pixel coordinates of concerned moving objects. Superiority of VAG4 over usual variance picture VAG2 is shown quantitatively by a model of moving particles. Pulsing areas of C. elegans, are exhibited more clearly in VAG4 than VAG2. Second step is use of a new subtraction method to extract main frequency bands. FFT spectra are averaged in active positions where VAG4 is above threshold THVR in the square with 88 pixels (ONA). The power spectra averaged in the enlarged squares (ELA) are subtracted from those in ONA, in which ELA includes ONA in its centre position. Large peak bands emerge in the subtracted power spectra. The subtraction eliminates the effect of head swing by spatial averagings in ELA. This new emphasizing method is compared to another subtraction method. The characteristic frequency of periodical moving organs coincides well with the values observed by other research groups and our visual estimation of replayed VTR images. Thus the proposed extraction method is verified to work well in double superposed motions.