Assuming application to the mobile multiple-access communication, chip-asynchronous mobile-to-base performances of FH/FTH (Frequency-Time-Hopped)-MFTSK (Multi-level Frequency-Time Shift Keying) systems are investigated. Analytical expressions are obtained for the probabilities of false detection and missed detection of signal elements, assuming independent and asynchronous arrival of each of the signal elements with Rayleigh fading and optional AWG noise. Using the result or by simulation and employing dual-k coding, parameter optimization was carried out to obtain the maximum spectrum efficiency. The results of the noisy case analysis and simulation show high noise-robustness of the FTH systems. For a given value of information transmission rate the optimized FTH-MFTSK gives an effectively constant spectrum efficiency for a wide range of the number Kf of frequency chips. As a result, FTH-MFTSK well outperforms FTH-MFSK at any, especially small value of Kf. Relative to the overall optimum FH-MFSK, FTH-MFSK systems show typically around 20% of degradation in spectrum efficiency even with one-eighth of Kf. Compared with FH-MFSK, accordingly, FTH-MFTSK systems allow the designer to reduce, without any degradation in multiple-access performances, the number of frequency chips to the minimum value tolerated by the frequency selective fading characteristics and the time chip duration requirement imposed by the signal-to-noise ratio margin and the transmitter peak power rating.

A feedback pattern recognition method using an inverse recall neural network model is proposed. The feedback method can adjust processing parameter values adaptively to individual patterns so as to produce reliable recognition results. In order to apply an adaptive control technique to such pattern recognition processings, the evaluation value for recognition uncertainty is determined to be a function with regard to an input pattern and processing parameters. In its feedback phase, the input pattern is fixed and processing parameters are adjusted to decrease the recognition uncertainty. The proposed neural network model implements two functions in this feedback recognition method. One is a discrimination as a kind of multi-layer feedforward model. The other is to generate an input modification so as to decrease the recognition uncertainty. The modification values indicate parts which are important for more certain recognition but are missed in the original input to the nerwork. The proposed feedback method can adjust prcessing parameter values in order to detect the important parts shown by the inverse recall network model. As explained in this paper, feature extraction parameter values are adaptively adjusted by this feedback method. After the inverse recall model and the feedback function are implemented, features are extracted again by using the modified feature extraction parameter values. The feature is classified by the feedforward function of the network model. The feedforward and feedback processings are repeated until a certain recognition result is obtained. This method was examined for hadwritten alpha-numerics with rotation distortion. The feedback method was found to decrease the rejection ratio at the same substitution error ratio with high efficiency.

This paper shows that when a pattern matching method used in optical character readers is highly accurate, it can be used effectively in on-line Japanese character recognition. Stroke matching methods used in previous conventional on-line character recognition have restricted the number and the order of strokes. On the other hand, orientation-feature pattern matching methods avoid these restrictions. The authors have improved a pattern matching method with the development in the flexible pattern matching (FPM) method, based on nonlinear shape normalization and nonlinear pattern matching, which includes the normalization-cooperative feature extraction (NCFE) method. These improvements have increased the recognition rate from 81.9% to 95.9%, when applied to the off-line database ETL-9 from the Electrotechnical Laboratory, Japan. When applied on-line to the examination of 151,533 Kanji and Hiragana characters in 3,036 categories, the recognition rate achieved 94.0%, while the cumulative recognition rate within the best ten candidates was 99.1%.

This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.