Voltage holding property of liquid crystal (LC) cell for long period was investigated and the experimantal results were analyzed using a microscopic model considered the movement of ions in LC layer. The time dependent voltage decay curve observed in the experiment, which is not driven by the analysis with the conventional equivalent circuit comprised of the capacitance and the resistance, can be well explained by the microscopic model.

Experimental and theoretical studies of light coupling to various magnetic nanostructured media and nanocomposites are briefly reported. Enhancement of the magneto-optical response is shown to occur when the constitutive materials of photonic crystals are magnetic. Transmission and reflection types of 1D magnetophotonic crystals (MPCs) have been studied. New possibility to enhance the magneto-optical response has been found when utilizing localized surface plasmon resonances in bismuth-substituted yttrium iron garnet (Bi:YIG) films impregnated with Au nanoparticles. Examples of integrated optic devices are discussed in which functional elements are 1D and 2D magnetophotonic crystals.

We investigated an analogue modulation of magneto-optic spatial light modulator (MOSLM). For enhancement of the modulation from the voltage-driving MOSLM, magnetostriction and saturation magnetization of magnetic garnet films and piezoelectric constant of PZT films were investigated. The performance was expected to be improved by using Bismuth, Dysprosium and Aluminum substituted Yttrium Iron garnet, which effective magnetic field showed 20 times higher than Yttrium Iron garnet.

Wireless body area networks (BANs) are attracting great attention as a future technology of wireless networks for healthcare and medical applications. Wireless BANs can generally be divided into two categories, i.e., wearable BANs and implant BANs. However, the performance requirements and channel propagation characteristics of these two kinds of BANs are quite different from each other, that is, wireless signals are approximately transmitted along the human body as a surface wave in wearable BANs, on the other hand, the signals are transmitted through the human tissues in implant BANs. As an effective solution for this problem, this paper first introduces a dual-mode communication system, which is composed of transmitters for in-body and on-body communications and a receiver for both communications. Then, we evaluate the bit error rate (BER) performance of the dual-mode communication system via computer simulations based on realistic channel models, which can reasonably represent the propagation characteristics of on-body and in-body communications. Finally, we conduct a link budget analysis based on the derived BER performances and discuss the link parameters including system margin, maximum link distance, data rate and required transmit power. Our computer simulation results and analysis results demonstrate the feasibility of the dual-mode communication system in wireless BANs.

A solution to the problem of improving robustness to noise in automatic speech recognition is presented in the framework of multi-band, multi-SNR, and multi-path approaches. In our word recognizer, the whole frequency band is divided into seven-overlapped sub-bands, and then sub-band noisy phoneme HMMs are trained on speech data mixed with the filtered white Gaussian noise at multiple SNRs. The acoustic model of a word is built as a set of concatenations of clean and noisy sub-band phoneme HMMs arranged in parallel. A Viterbi decoder allows a search path to transit to another SNR condition at a phoneme boundary. The recognition scores of the sub-bands are then recombined to give the score for a word. Experiments show that the overlapped seven-band system yields the best performance under nonstationary ambient noises. It is also shown that the use of filtered white Gaussian noise is advantageous for training noisy phoneme HMMs.

Syntax and prosody are closely related to each other. This paper is concerned with the problem of exploiting pause information for recovering dependency structures of read Japanese sentences. Our parser can handle both symbolic information such as dependency rule and numerical information such as the probability of dependency distance of a phrase in a unified way as linguistic information. In our past work, post-phrase pause that immediately succeeds a phrase in question was employed as prosodic information. In this paper, we employed two kinds of pauses in addition to the post-phrase pause: post-post-phrase pause that immediately succeeds the phrase that follows a phrase in question, and pre-phrase pause that immediately precedes a phrase in question. By combining the three kinds of pause information linearly with the optimal combination weights that were determined experimentally, the parsing accuracy was improved compared to the case where only the post-phrase pause was used as in our previous work. Linear combination of pause and fundamental frequency information yielded further improvement of parsing accuracy.

A microwave power amplifier with independently biased InGaP/GaAs HBTs is proposed, and its superior performance is confirmed. Using harmonic balance simulation, the optimal bias conditions for an amplifier with two independently biased InGaP/GaAs HBTs were investigated with the aim of achieving high-efficiency low-distortion performance. A 1.9-GHz-band cascode power amplifier was designed and fabricated. Power efficiencies and third-order intermodulation distortions (IMD3) for the fabricated amplifier were estimated. The collector bias voltage of the first stage transistor mainly affects power-added efficiency (PAE). The base bias current of the first-stage HBT mainly affects IMD3 characteristics, and that of the second-stage HBT mainly affects PAE. The proposed amplifier shows superior performance when compared to a conventional cascode amplifier. The amplifier achieved a maximum PAE of 68.0% with an output power of 14.8dBm, and IMD3 better than -35dBc with a PAE of 25.1%, for a maximum output power of 10.25dBm at 1.9GHz. A PAE of more than 60% was achieved from 1.87 to 1.98GHz.

Experiments were conducted to examine an approach from language modeling side to improving noisy speech recognition performance. By adopting appropriate word strings as new units of processing, speech recognition performance was improved by acoustic effects as well as by test-set perplexity reduction. Three kinds of word string language models were evaluated, whose additional lexical entries were selected based on combinations of part of speech information, word length, occurrence frequency, and log likelihood ratio of the hypotheses about the bigram frequency. All of the three word string models reduced errors in broadcast news speech recognition, and also lowered test-set perplexity. The word string model based on log likelihood ratio exhibited the best improvement for noisy speech recognition, by which deletion errors were reduced by 26%, substitution errors by 9.3%, and insertion errors by 13%, in the experiments using the speaker-dependent, noise-adapted triphone. Effectiveness of word string models on error reduction was more prominent for noisy speech than for studio-clean speech.

In the present paper, we attempt to show that the information about input patterns must be as small as possible for improving the generalization performance under the condition that the network can produce targets with appropriate accuracy. The information is defined with respect to the hidden unit activity and we suppose that the hidden unit has a crucial role to store the information content about input patterns. The information is defined by the difference between uncertainty of the hidden unit at the initial stage of the learning and the uncertainty of the hidden unit at the final stage of the learning. After having formulated an update rule for the information minimization, we applied the method to a problem of language acquisition: the inference of the past tense forms of regular and irregular verbs. Experimental results confirmed that by our method, the information was significantly decreased and the generalization performance was greatly improved.

There are various difficulties in processing spoken dialogs because of acoustic, phonetic, and grammatical ill-formedness, and because of interactions among participants. This paper describes temporal characteristics of utterances in human-human task-oriented dialogs and interactions between the participants, analyzed in relation to the topic structure of the dialog. We analyzed 12 task-oriented simulated dialogs of ASJ continuous speech corpus conducted by 13 different participants whose total length being 66 minutes. Speech data was segmented into utterance units each of which is a speech interval segmented by pauses. There were 3876 utterance units, and 38.9% of them were interjections, fillers, false starts and chiming utterances. Each dialog consisted of 6 to 15 topic segments in each of which participants exchange specific information of the task. Eighty-six out of 119 new topic segments started with interjectory utterances and filled pauses. It was found that the durations of turn-taking interjections and fillers including the preceding silent pause were significantly longer in topic boundaries than the other positions. The results indicate that the duration of interjection words and filled pauses is a sign of a topic shift in spoken dialogs. In natural conversations, participants' speaking modes change dynamically as the conversation develops. Response time of both client and agent role speakers became shorter as the dialog proceeded. This indicates that interactions between the participants become active as the dialog proceeds. Speech rate was also affected by the dialog structure. It was generally fast in the initiating and terminating parts where most utterances are of fixed expressions, and slow in topic segments of the body part of the dialog where both client and agent participants stalled to speak in order to retrieve task knowledge. The results can be utilized in man-machine dialog systems, e.g., in order to detect topic shifts of a dialog, and to make the speech interface of dialog systems more natural to a human participant.

This paper is concerned with improving noise-robustness of a multi-SNR multi-band speaker identification system by introducing automatic adjustment of subband likelihood recombination weights. The adjustment is performed on the basis of subband power calculated from the noise observed just before the speech starts in the input signal. To evaluate the noise-robustness of this system, text-independent speaker identification experiments were conducted on speech data corrupted with noises recorded in five environments: "bus," "car," "office," "lobby," and "restaurant". It was found that the present method reduces the identification error by 15.9% compared with the multi-SNR multi-band method with equal recombination weights at 0 dB SNR. The performance of the present method was compared with a clean fullband method in which a speaker model training is performed on clean speech data, and spectral subtraction is applied to the input signal in the speaker identification stage. When the clean fullband method without spectral subtraction is taken as a baseline, the multi-SNR multi-band method with automatic adjustment of recombination weights attained 56.8% error reduction on average, while the average error reduction rate of the clean fullband method with spectral subtraction was 11.4% at 0 dB SNR.