This paper describes the method of using multi-template unsupervised speaker adaptation based on HMM-Sufficient Statistics to push up the adaptation performance while keeping adaptation time within few seconds with just one arbitrary utterance. This adaptation scheme is mainly composed of two processes. The first part is done offline which involves the training of multiple class-dependent acoustic models and the creation of speakers' HMM-Sufficient Statistics based on gender and age. The second part is performed online where adaptation begins using the single utterance of a test speaker. From this utterance, the system will classify the speaker's class and consequently select the N-best neighbor speakers close to the utterance using Gaussian Mixture Models (GMM). The classified speakers' class template model is then adopted as a base model. From this template model, the adapted model is rapidly constructed using the N-best neighbor speakers' HMM-Sufficient Statistics. Experiments in noisy environment conditions with 20 dB, 15 dB and 10 dB SNR office, crowd, booth, and car noise are performed. The proposed multi-template method achieved 89.5% word accuracy rate compared with 88.1% of the conventional single-template method, while the baseline recognition rate without adaptation is 86.4%. Moreover, experiments using Vocal Tract Length Normalization (VTLN) and supervised Maximum Likelihood Linear Regression (MLLR) are also compared.

This paper proposes a new compensation method of acoustic scores in the Viterbi search for robust speech recognition. This method introduces noise models to represent a wide variety of noises and realizes robust decoding together with conventional techniques of subtraction and adaptation. This method uses likelihoods of noise models in two ways. One is to calculate a confidence factor for each input frame by comparing likelihoods of speech models and noise models. Then the weight of the acoustic score for a noisy frame is reduced according to the value of the confidence factor for compensation. The other is to use the likelihood of noise model as an alternative that of a silence model when given noisy input. Since a lower confidence factor compresses acoustic scores, the decoder rather relies on language scores and keeps more hypotheses within a fixed search depth for a noisy frame. An experiment using commentary transcriptions of a broadcast sports program (MLB: Major League Baseball) showed that the proposed method obtained a 6.7% relative word error reduction. The method also reduced the relative error rate of key words by 17.9%, and this is expected lead to an improvement metadata extraction accuracy.

Multiple-input multiple-output (MIMO) eigenbeam space division multiplexing that uses adaptive modulations for substreams is a promising technology for improving transmission capacity. A fundamental drawback of this approach is that the modulation levels determined from the carrier-to-noise ratio at each substream are sometimes overly optimistic so the use of these modulation levels results in transmission errors and diminished transmission performance. A novel method of determining substream modulation levels is proposed that alleviates this degradation. In the proposed method, the expected bit error rates for possible modulations of each substream are calculated from delay profiles. Simulation results indicate that transmission capacity is improved by 30% using the new method compared with the conventional method.

Electromagnetic (EM) wave absorbers have been used for improving the EM environment of an electronic toll collection (ETC) system on an express highway or a wireless local area network (LAN) system in an indoor environment. In this paper, an efficient multi-ray propagation model, which uses 3D geometry and image techniques to trace multiple signal rays from transmitter to receiver, is employed to analyze the EM environment of a dedicated short-range communication (DSRC) system on an express highway. The validity of the model employed is discussed by the comparison with the results obtained by an experiment on the highways. The analysis shows that the possible undesired communications between onboard equipment that acts as a receiver and DSRC beacon which transmits the radio signals, is improved by increasing the EM wave-absorption capability of the sidewalls and the pavement of the highway. Another advantage of the employed model is that it is effective for investigating the location of wave absorbers to be set up on the highway, and it takes only a fraction of a minute for computation.

In this paper, a novel projection-based method is presented to register partial 3D point clouds, acquired from a multi-view camera, for 3D reconstruction of an indoor scene. In general, conventional registration methods for partial 3D point clouds require a high computational complexity and much time for registration. Moreover, these methods are not robust for 3D point cloud which has a low precision. To overcome these drawbacks, a projection-based registration method is proposed. Firstly, depth images are refined based on both temporal and spatial properties. The former involves excluding 3D points with large variation, and the latter fills up holes referring to four neighboring 3D points, respectively. Secondly, 3D point clouds acquired from two views are projected onto the same image plane, and two-step integer mapping is applied to search for correspondences through the modified KLT. Then, fine registration is carried out by minimizing distance errors based on adaptive search range. Finally, we calculate a final color referring to the colors of corresponding points and reconstruct an indoor scene by applying the above procedure to consecutive scenes. The proposed method not only reduces computational complexity by searching for correspondences on a 2D image plane, but also enables effective registration even for 3D points which have a low precision. Furthermore, only a few color and depth images are needed to reconstruct an indoor scene. The generated model can be adopted for interaction with as well as navigation in a virtual environment.

High-speed intercity railways have grown into profitable business, achieving a renaissance in rail transport. High-speed railways need constant updating to new systems if they are to be winners in this age of competing transportation modes. In view of that situation, JR East started an R&D project to achieve even faster speed--more than 300 km/h. A test train that can run at an operational speed of 360 km/h is under development, and JR East plans to commence high-speed tests in the summer of 2005.

This paper discusses clock skew due to manufacturing variability and environmental change. In clock tree design, transition time constraint is an important design parameter that controls clock skew and power dissipation. In this paper, we evaluate clock skew under several variability models, and demonstrate relationship among clock skew, transition time constraint and power dissipation. Experimental results show that constraint of small transition time reduces clock skew under manufacturing and supply voltage variabilities, whereas there is an optimum constraint value for temperature gradient. Our experiments in a 0.18 µm technology indicate that clock skew is minimized when clock buffer is sized such that the ratio of output and input capacitance is four.

The advent of mobile IP communication has opened up several new areas of mission critical communication applications. But the bandwidth and reliability constraints coupled with handover latency are posing some hurdles which need to be overcome before real world mobile IP applications, with low tolerance for data loss, can be deployed. In this paper, we analyze the unreliability of existing information collection methods in the real-world MobileIP environment. We focus on this problem and propose a novel network management model that anticipates the wireless mobile entities and uses SNMP. The key idea of this model is the introduction of a store-and-forward type Managed Object (MO). During the period of unreachability between the Manager and the agent, the data is cached at the agent until the connectivity recovers. In our experiment we used a prototype implementation in real-world wireless communication field, and showed the effectiveness of our proposed method.

Ubiquitous computing (ubicomp) is a computing para-digm which utilizes human-centric systems and applications. With the widespread use of information appliances, robots and sensors, the ubicomp paradigm is expected to become a reality in the near future. Because close interaction between a person and the computing environment is required for ubicomp, autonomous decentralized control will play an important role. In this paper, we discuss autonomous decentralized control in ubicomp from the viewpoint of typical ubicomp applications, smart environments and context-awareness.

In this letter we propose a new transmission technique to realize both high speed data transmission and high data quality by decreasing the difference in BER performance due to the phase difference of direct paths for 22 MIMO systems using spatial multiplexing in LOS environments.

A block-matching-based self-organizing map (BMSOM) is presented. Finding a winner is carried out for each block, which is a set of neurons arranged in square. The proposed learning process updates the reference vectors of all of the neurons in a winner block. Then, the degrees of vector modifications are mainly controlled by the size (i.e., the number of neurons) of the winner block. To prevent a single cluster with neurons from splitting into some disjointed clusters, the restriction of the block size is imposed in the beginning of learning. At the main stage, this restriction is canceled. In BMSOM learning, the size of a winner block does not always decrease monotonically. The formula used to update the reference vectors is basically uncontrolled by time. Therefore, even if a map is in a nonstationary environment, training the map is probably pursued without interruption to adjust time-controlled parameters such as learning rate. Numerical results demonstrate that the BMSOM makes it possible to improve the plasticity of maps in a nonstationary environment and incremental learning.

In this paper, a refined analytic model is presented for the IEEE 802.11 distributed coordination function (DCF) in a time-varying channel environment. In the proposed model, the channel is modelled using a finite-state Markov (FSM) chain. The saturation throughput and average packet delay are analyzed from the proposed model. It is shown using OPNETTM and UltraSANTM simulations that the proposed model accurately predicts the performance of the IEEE 802.11 DCF.

The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. Furthermore, the observations in the sound environment are often in the level-quantized form. In this paper, a method for estimating the specific signal for stochastic systems with unknown structure and the quantized observation is proposed by introducing a system model of the conditional probability type. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem of psychological evaluation for the sound environment.

Many immersive displays developed in previous researches are strongly influenced by the design concept of the CAVE, which is the origin of the immersive displays. In the view of human-scale interactive system for virtual environment (VE), the existing immersive systems are not enough to use the potential of a human sense further extent. The displays require more complicated structure for flexible extension, and are more restrictive to user's movement. Therefore we propose a novel multi-projector display for immersive VE with haptic interface for more flexible and dynamic interaction. The display part of our system named "D-vision" has a hybrid curved screen which consist of compound prototype with flat and curve screen. This renders images seamlessly in real time, and generates high-quality stereovision by PC cluster and two-pass technology. Furthermore a human-scale string-based haptic device will integrate with the D-vision for more interactive and immersive VE. In this paper, we show an overview of the D-vision and technologies used for the human-scale haptic interface.

This paper describes continuous speech recognition incorporating the additional complement information, e.g., voice characteristics, speaking styles, linguistic information and noise environment, into HMM-based acoustic modeling. In speech recognition systems, context-dependent HMMs, i.e., triphone, and the tree-based context clustering have commonly been used. Several attempts to utilize not only phonetic contexts, but additional complement information based on context (factor) dependent HMMs have been made in recent years. However, when the additional factors for testing data are unobserved, methods for obtaining factor labels is required before decoding. In this paper, we propose a model integration technique based on general factor dependent HMMs for decoding. The integrated HMMs can be used by a conventional decoder as standard triphone HMMs with Gaussian mixture densities. Moreover, by using the results of context clustering, the proposed method can determine an optimal number of mixture components for each state dependently of the degree of influence from additional factors. Phoneme recognition experiments using voice characteristic labels show significant improvements with a small number of model parameters, and a 19.3% error reduction was obtained in noise environment experiments.

Process-centered software engineering environments (PSEEs) facilitate controlling complicated software processes. Traditional PSEEs are generally centrally controlled, which may result in the following drawbacks: (1) the server may become a bottleneck and (2) when the server is down, processes need to be suspended. To overcome the drawbacks, we developed a decentralized process engine ADPE (agent-based decentralized process engine). ADPE can be embedded in any PSEE to decentralize the PSEE. This paper presents ADPE.

Process-centered software engineering environments (PSEEs) facilitate controlling software processes. Many issues related to PSEEs such as process evolution support have been addressed. We identify an unsolved issue, which is preventing information leakage when the process is being enacted. We developed a model called PsACL for the prevention. This paper proposes PsACL, which offers the following features: (a) controlling both read and write access of software products, (b) preventing indirect information leakage, (c) managing role associations, (d) managing role hierarchies, (e) enforcing static and simple dynamic separation-of-duty constraints, (f) allowing declassification of products, and (g) allowing access control information exchange among software processes.

This paper addresses the problem of channel estimation for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM)-based wireless networks with frequency-reuse. Firstly, the optimal time-domain training sequences are derived for the multiple cells with the same frequency group and a set of suitable sequences are also presented for practical implementation. Secondly, a low complexity iterative algorithm is combined with the time domain channel estimation to suppress the co-channel interferences (CCIs). The channel estimation method is applied to synchronous and asynchronous cellular and the MSE performance of the estimator is also analyzed. Simulation results demonstrate that the presented channel estimation approach can substantially suppress CCIs and outperform the conventional LS MIMO-OFDM channel estimation over multipath fading channels in multicell environments.

Direct manipulation by hand is an intuitive and simple way of positioning objects in an immersive virtual environment. However, this technique is not suitable for making precise adjustments to virtual objects in an immersive environment because it is difficult to hold a hand unsupported in midair and to then release an object at a fixed point. We therefore propose an alternative technique using a virtual 3D gearbox widget that we have designed, which enables users to adjust values precisely. We tested the technique in a usability study along with the use of hand manipulation and a slider. The results showed that the gearbox was the best of the three techniques for precise adjustment of small targets, in terms of both performance data and subject preference.

Propagation modeling and advanced channel characterization techniques represent integral parts of significant impact in advancing progress in enabling next generation wireless communication technology and realizing its much anticipated broader application and economic benefits. In this paper we describe advances in developing computationally efficient ray-tracing channel modeling procedures, and also describe recent results in characterizing challenging propagation environments including transmission through windows and propagation through walls of complex structures. The impact of these realistic propagation environments as well as the antenna mutual coupling effects on the estimation of channel capacity in a MIMO-based communication system is also evaluated. Significant difference between realistic and statistical channel models are identified and quantified for the special cases of the channels modeled in this study.