Automatic adjustment methods for efficient, precise positioning and release of a virtual 3D object by direct hand manipulation in an immersive virtual reality environment are described and evaluated. The proposed methods are release adjustment, position adjustment, viewpoint adjustment, and virtual hand size adjustment. Combining these methods enables users to manipulate a virtual object efficiently and precisely. An experimental evaluation showed that these methods were effective and useful in terms of the number of task completions and the subjective preference, particularly for a small virtual target.

This paper describes key design technology issues as general ideas, rather than for specific fields, with a view to realizing better technology for the future. This paper also discusses the scope of the vision we should adopt, the factors we should be conscious of, and how we should design future systems. The key ideas arise from the belief that technology should be designed in the context of the environment with intellect behind it.

Characteristics of conductive elastomer that is composed of silicone rubber and dispersed carbon black particles show conductive and elastic properties in one simple material. This material has been widely applied to make-break contacts of panel switches and connectors of liquid crystal panels. However, since surface state of the contact is very soft, it is difficult to remove contaminant films of contaminated opposite side contact surface and to obtain low contact resistance owing to break the film. This is an important problem to be solved not only for the application of make-break switching contact but also static connector contacts. This study has been conducted to examine some complex structures of the elastomer which indicate removal characteristics for contaminant films and low contact resistance. As specimens, six different types of elastomer contacts composed of different type of dispersed materials as carbon and metal fibers, metal mesh, and plated surfaces were used. The contacts of opposite side were Au and Sn plated contact surface on a printed circuit board (PCB) which is usually used in the static connector and make-break contacts. In order to contaminate contact surfaces of PCB, the surfaces were subjected to exposure in an SO2 gas environment. The elastomeric contacts contained hard materials showed lower contact resistance than only dispersed carbon particles in the elastomer matrix for both contaminated PCB contact surfaces.

In this paper, we propose a novel solution to improving wireless channel quality of wireless local area networks (WLANs) in fast-mobile environments, which uses a media-access-control (MAC) layer approach: adaptive frame-length control and block acknowledgement (ACK). In fast-mobile environments, using short frame lengths can suppress channel estimation error and decrease frame errors. However, it increases the MAC overhead, resulting in decreased throughput. To solve this tradeoff, we combined block ACK, which is specified in IEEE802.11e as an optional function, with adaptive frame-length control. Although adaptive frame-length control considering this tradeoff has previously been investigated, the targets were different from WLANs using orthogonal frequency division multiplexing (OFDM) in fast-mobile environments. The MAC-overhead reduction using block ACK is suitable for our frame-length control because it does not change the frame format in the physical layer. Also, it is a new idea to use block ACK as a solution to improving channel quality in fast-mobile environments. In this paper, we evaluate our method through computer simulations and verify the effectiveness of adaptive frame-length control that can accommodate relative speeds.

This paper proposes an antenna selection method for terminal antennas employing orthogonal polarizations and patterns, which is suitable for outdoor MultiUser Multi-Input Multi-Output (MU-MIMO) systems. In addition, this paper introduces and verifies two other antenna selection methods for comparison. For the sake of simplicity, three orthogonal dipoles are considered, and this antenna configuration using the proposed selection method is compared to an antenna configuration with three vertical or horizontal dipoles. In the proposed antenna selection method, we always choose the vertical dipole, and choose one of two horizontal dipoles, which are orthogonal to each other, based on the Signal-to-Noise Ratio (SNR). We measured the MU-MIMO transmission properties and found that the proposed selection method employing the antenna with orthogonal polarizations and patterns can offer fairly high channel capacity in a multiuser scenario.

In time-varying fading environments, the performance of multiple-input multiple-output (MIMO) systems applying an eigenbeam-space division multiplexing (E-SDM) technique may be degraded due to a channel change during the time interval between the transmit weight matrix determination and the actual data transmission. To compensate for the channel change, we have proposed some channel prediction methods. Simulation results based on computer-generated channel data showed that better performance can be obtained when using the prediction methods in Rayleigh fading environments assuming the Jakes model with rich scatterers. However, actual MIMO systems may be used in line-of-sight (LOS) environments, and even in a non-LOS case, scatterers may not be uniformly distributed around a receiver and/or a transmitter. In addition, mutual coupling between antennas at both the transmitter and the receiver cannot be ignored as it affects the system performance in actual implementation. We conducted MIMO channel measurement campaigns at a 5.2 GHz frequency band to evaluate the channel prediction techniques. In this paper, we present the experiment and simulation results using the measured channel data. The results show that robust bit-error rate performance is obtained when using the channel prediction methods and that the methods can be used in both Rayleigh and Rician fading environments, and do not need to know the maximum Doppler frequency.

This paper illustrates a feasible health informatics domain knowledge management process which helps gather useful technology information and reduce many knowledge misunderstandings among engineers who have participated in the IBM mainframe rightsizing project at National Taiwan University (NTU) Hospital. We design an asynchronously sharing mechanism to facilitate the knowledge transfer and our health informatics domain knowledge management process can be used to publish and retrieve documents dynamically. It effectively creates an acceptable discussion environment and even lessens the traditional meeting burden among development engineers. An overall description on the current software development status is presented. Then, the knowledge management implementation of health information systems is proposed.

In this paper, we propose a two-microphone noise reduction method to deal with non-stationary interfering noises in multiple-noise-source environments in which the traditional two-microphone algorithms cannot function well. In the proposed algorithm, multiple interfering noise sources are regarded as one virtually integrated noise source in each subband, and the spectrum of the integrated noise is then estimated using its virtual direction of arrival. To do this, we suggest a direction finder for the integrated noise using only two microphones that performs well even in speech active periods. The noise spectrum estimate is further improved by integrating a single-channel noise estimation approach and then subtracted from that of the noisy signal, finally enhancing the desired target signal. The performance of the proposed algorithm is evaluated and compared with the traditional algorithms in various conditions. Experimental results demonstrate that the proposed algorithm outperforms the traditional algorithms in various conditions in terms of objective and subjective speech quality measures.

In this paper, we present a new three-dimensional (3D) virtual environment (3DVE) system named "QuViE/P", which can enhance quality of service (QoS), that users actually feel, as good as possible when resources of computers and networks are limited. To realize this, we focus on characteristics of user's perceptual quality evaluation on 3D objects. We propose an effective QoS control scheme for QuViE/P by introducing relationships between system's internal quality parameters and user's perceptual quality parameters. This scheme can appropriately maintain the QoS of the 3DVE system and it is expected to improve convenience when using 3DVE system where resources are insufficient. We designed and implemented a prototype of QuViE/P using a multiagent framework. The experiment results show that even when the computer resource is reduced to 20% of the required amount, the proposed scheme can maintain the quality of important objects to a certain level.

The MIMO system can meet the growing demand for higher capacity in wireless communication fields. So far, the authors have reported that, based on channel measurements, uncoded performance of narrowband MIMO spatial multiplexing in indoor line-of-sight (LOS) environments generally outperforms that in non-LOS (NLOS) ones under the same transmit power condition. In space-frequency coded MIMO-OFDM spatial multiplexing, however, we cannot expect high space-frequency diversity gain in LOS environments because of high fading correlations and low frequency selectivity of channels so that the performance may degrade unlike uncoded cases. In this letter, we present the practical performance of coded MIMO-OFDM spatial multiplexing based on indoor channel measurements. The results show that an LOS environment tends to provide lower space-frequency diversity effect whereas the MIMO-OFDM spatial multiplexing performance is still better in the environment compared with an NLOS environment.

This paper proposes a security violation detection method for RBAC based interoperation to meet the requirements of secure interoperation among distributed systems. We use role mappings between RBAC systems to implement trans-system access control, analyze security violation of interoperation with role mappings, and formalize definitions of secure interoperation. A minimum detection method according to the feature of RBAC system in distributed environment is introduced in detail. This method reduces complexity by decreasing the amount of roles involved in detection. Finally, we analyze security violation further based on the minimum detection method to help administrators eliminate security violation.

In this letter, we propose a shared adaptive packet loss concealment scheme for the high quality guaranteed Internet telephony service which connects multiple users. In order to recover packet loss efficiently in the all-IP based convergence environment, we provide a robust signal recovery scheme which is based on the shared adaptive both-side information utilization. This scheme is provided according to the average magnitude variation across the frames and the pitch period replication on the 1-port gateway (G/W) system. The simulated performance demonstrates that the proposed scheme has the advantages of low processing times and high recovery rates in the all-IP based ubiquitous environment.

In this paper, we evaluate BER (bit error rate) performance and diversity gain when employing a transmission technique utilizing LC (Linear Combination) diversity using 2 time slots with QPSK channels in 2 2 MIMO (Multiple-Input Multiple-Output) spatial multiplexing systems by comparing it with the upper and lower bound on BER. This evaluation shows that this transmission technique realizes high diversity gain and high transmission rate in LOS (line-of-sight) and NLOS (non line-of-sight) environments.

This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

In a distant-talking environment, the length of channel impulse response is longer than the short-term spectral analysis window. Conventional short-term spectrum based Cepstral Mean Normalization (CMN) is therefore, not effective under these conditions. In this paper, we propose a robust speech recognition method by combining a short-term spectrum based CMN with a long-term one. We assume that a static speech segment (such as a vowel, for example) affected by reverberation, can be modeled by a long-term cepstral analysis. Thus, the effect of long reverberation on a static speech segment may be compensated by the long-term spectrum based CMN. The cepstral distance of neighboring frames is used to discriminate the static speech segment (long-term spectrum) and the non-static speech segment (short-term spectrum). The cepstra of the static and non-static speech segments are normalized by the corresponding cepstral means. In a previous study, we proposed an environmentally robust speech recognition method based on Position-Dependent CMN (PDCMN) to compensate for channel distortion depending on speaker position, and which is more efficient than conventional CMN. In this paper, the concept of combining short-term and long-term spectrum based CMN is extended to PDCMN. We call this Variable Term spectrum based PDCMN (VT-PDCMN). Since PDCMN/VT-PDCMN cannot normalize speaker variations because a position-dependent cepstral mean contains the average speaker characteristics over all speakers, we also combine PDCMN/VT-PDCMN with conventional CMN in this study. We conducted the experiments based on our proposed method using limited vocabulary (100 words) distant-talking isolated word recognition in a real environment. The proposed method achieved a relative error reduction rate of 60.9% over the conventional short-term spectrum based CMN and 30.6% over the short-term spectrum based PDCMN.

Speech recognition in a noisy environment is one of the hottest topics in the speech recognition research. Noise-tolerant acoustic models or noise reduction techniques are often used to improve recognition accuracy. In this paper, we propose a method to improve accuracy of spoken dialog system from a language model point of view. In the proposed method, the dialog system automatically changes its language model and dialog strategy according to the estimated recognition accuracy in a noisy environment in order to keep the performance of the system high. In a noise-free environment, the system accepts any utterance from a user. On the other hand, the system restricts its grammar and vocabulary in a noisy environment. To realize this strategy, we investigated a method to avoid the user's out-of-grammar utterances through an instruction given by the system to a user. Furthermore, we developed a method to estimate recognition accuracy from features extracted from noise signals. Finally, we realized a proposed dialog system according to these investigations.

In this paper, the development, long-term operation and portability of a practical ASR application in a real environment is investigated. The target application is a speech-oriented guidance system installed at the local community center. The system has been exposed to ordinary people since November 2002. More than 300 hours or more than 700,000 inputs have been collected during four years. The outcome is a rare example of a large scale real-environment speech database. A simulation experiment is carried out with this database to investigate how the system's performance improves during the first two years of operation. The purpose is to determine empirically the amount of real-environment data which has to be prepared to build a system with reasonable speech recognition performance and response accuracy. Furthermore, the relative importance of developing the main system components, i.e. speech recognizer and the response generation module, is assessed. Although depending on the system's modeling capacities and domain complexity, experimental results show that overall performance stagnates after employing about 10-15 k utterances for training the acoustic model, 40-50 k utterances for training the language model and 40 k-50 k utterances for compiling the question and answer database. The Q&A database was most important for improving the system's response accuracy. Finally, the portability of the well-trained first system prototype for a different environment, a local subway station, is investigated. Since collection and preparation of large amounts of real data is impractical in general, only one month of data from the new environment is employed for system adaptation. While the speech recognition component of the first prototype has a high degree of portability, the response accuracy is lower than in the first environment. The main reason is a domain difference between the two systems, since they are installed in different environments. This implicates that it is imperative to take the behavior of users under real conditions into account to build a system with high user satisfaction.

Development of an ASR application such as a speech-oriented guidance system for a real environment is expensive. Most of the costs are due to human labeling of newly collected speech data to construct the acoustic model for speech recognition. Employment of existing models or sharing models across multiple applications is often difficult, because the characteristics of speech depend on various factors such as possible users, their speaking style and the acoustic environment. Therefore, this paper proposes a combination of unsupervised learning and selective training to reduce the development costs. The employment of unsupervised learning alone is problematic due to the task-dependency of speech recognition and because automatic transcription of speech is error-prone. A theoretically well-defined approach to automatic selection of high quality and task-specific speech data from an unlabeled data pool is presented. Only those unlabeled data which increase the model likelihood given the labeled data are employed for unsupervised training. The effectivity of the proposed method is investigated with a simulation experiment to construct adult and child acoustic models for a speech-oriented guidance system. A completely human-labeled database which contains real-environment data collected over two years is available for the development simulation. It is shown experimentally that the employment of selective training alleviates the problems of unsupervised learning, i.e. it is possible to select speech utterances of a certain speaker group but discard noise inputs and utterances with lower recognition accuracy. The simulation experiment is carried out for several selected combinations of data collection and human transcription period. It is found empirically that the proposed method is especially effective if only relatively few of the collected data can be labeled and transcribed by humans.

An effective feature compensation method is developed for reliable speech recognition in real-life in-vehicle environments. The CU-Move corpus, used for evaluation, contains a range of speech and noise signals collected for a number of speakers under actual driving conditions. PCGMM-based feature compensation, considered in this paper, utilizes parallel model combination to generate noise-corrupted speech model by combining clean speech and the noise model. In order to address unknown time-varying background noise, an interpolation method of multiple environmental models is employed. To alleviate computational expenses due to multiple models, an Environment Transition Model is employed, which is motivated from Noise Language Model used in Environmental Sniffing. An environment dependent scheme of mixture sharing technique is proposed and shown to be more effective in reducing the computational complexity. A smaller environmental model set is determined by the environment transition model for mixture sharing. The proposed scheme is evaluated on the connected single digits portion of the CU-Move database using the Aurora2 evaluation toolkit. Experimental results indicate that our feature compensation method is effective for improving speech recognition in real-life in-vehicle conditions. A reduction of 73.10% of the computational requirements was obtained by employing the environment dependent mixture sharing scheme with only a slight change in recognition performance. This demonstrates that the proposed method is effective in maintaining the distinctive characteristics among the different environmental models, even when selecting a large number of Gaussian components for mixture sharing.

Automated capture and retrieval of experiences at home is interesting due to the wide variety and personal significance of such experiences. We present a system for retrieval and summarization of continuously captured multimedia data from Ubiquitous Home, a two-room house consisting of a large number of cameras and microphones. Data from pressure based sensors on the floor are analyzed to segment footsteps of different persons. Video and audio handover are implemented to retrieve continuous video streams corresponding to moving persons. An adaptive algorithm based on the rate of footsteps summarizes these video streams. A novel method for audio segmentation using multiple microphones is used for video retrieval based on sounds with high accuracy. An experiment, in which a family lived in this house for twelve days, was conducted. The system was evaluated by the residents who used the system for retrieving their own experiences; we report and discuss the results.