The thin film transistor liquid crystal display (TFT-LCD) image has nonuniform brightness, which is a major difficulty in finding the Mura defect region. To facilitate Mura segmentation, globally widely varying background signal must be flattened and then Mura signal must be enhanced. In this paper, Mura signal enhancement and background-signal-flattening methods using wavelet coefficient processing are proposed. The wavelet approximation coefficients are used for background-signal flattening, while wavelet detail coefficients are employed to magnify the Mura signal on the basis of an adapted contrast sensitivity function (CSF). Then, for the enhanced image, trimodal thresholding segmentation technique and a false-region elimination method based on the human visual system (HVS) are employed for reliable Mura segmentation. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding Muras in a TFT-LCD image.

This paper describes near-zero ultra-flattened chromatic dispersion and low confinement loss that can be achieved from a decagonal photonic crystal fiber (D-PCF). The finite difference method with anisotropic perfectly matched boundary layer (PML) is used for the numerical analysis. It is demonstrated that it is possible to design a four-ring D-PCF with ultra-flattened dispersion of 0 0.69 ps/(nm-km) in a 1.30 to 1.75 µm wavelength range and 0 0.22 ps/(nm-km) in a 1.35 to 1.65 µm wavelength range with very low confinement losses of order 0.0011 dB/km. The proposed D-PCF shows promising dispersion tolerance.

Eye tracking is a useful tool for accurately mapping where and for how long an individual learner looks at a video/image, in order to obtain immediate information regarding the distribution of a learner's attention among the elements of a video/image. This paper describes a quantitative investigation into the effect of voice navigation in web-based learning materials.

A method for measuring the luminance distribution of an electron beam spot was described, which is fundamental to evaluate the resolution of a color display tube. First, to achieve high sensitivity and wide dynamic range identical to those of visual inspection, we proposed the use of an ICCD camera for imaging and two levels of sensitivity. With that method, we were able to measure the luminance distribution of an electron beam spot over a range of currents that extends from the extremely weak cathode current region to large current that correspond to the peak luminance. Specifically, we were able to measure the entire distribution shape from the base to the peak for beam spots in the cathode current range from 20 µA to 300 µA, while compensating the absolute luminance level. Second, a reconstruction algorithm of entire beam distribution from the shape of the masked part of the beam was also proposed, in which shift error is compensated to reduce the variance in measurement results caused by jitter noise in the conventional image processing method. That algorithm improves the reproducibility of repeated measurements. Specifically, a function for estimating the actual shift from the first-order moment of the image was incorporated into the spot shape reconstruction algorithm, resulting in a reduction of the standard deviation for repeated measurements of the horizontal beam spot diameter at 5% intensity from 0.02 mm to 0.005 mm.

The objective of this paper is to analyze a pull type multi-product, multi-line and multi-stage flexible manufacturing system whose resources are subject to planned and unplanned breakdown conditions. To ensure a continual supply of the finished products, under breakdown conditions, parts/materials flow through alternate routes exhibiting routing flexibility. The machine resources are flexible in this study and are capable of producing more than one item. Every machining and assembly station has been equipped with automated inspection units to ensure the quality of the products. The system is modelled through coloured Petri net methodology and the impact of input factors have been shown on the performance of the system. The study has been extended to explore near-optimal conditions of the system using design of experiment and response surface methods.

We propose a filtering method for optimal estimation of TFT-LCD's surface region except defect's region. To estimate the non-uniform intensity variation on TFT-LCD surface region, the 4-directional Gaussian filter based on image pyramid structure is proposed. The experimental result verified the proposed method's performance

In this letter, we propose a novel approach to speech enhancement, which incorporates a new criterion based on residual noise shaping. In the proposed approach, our goal is to make the residual noise perceptually comfortable instead of making it less audible. A predetermined `comfort noise' is provided as a target for the spectral shaping. Based on some assumptions, the resulting spectral gain function turns out to be a slight modification of the Wiener filter while requiring very low computational complexity. Subjective listening test shows that the proposed algorithm outperforms the conventional spectral enhancement technique based on soft decision and the noise suppression implemented in IS-893 Selectable Mode Vocoder.

With the aim of improving speaker identification, we propose a likelihood-based integration method to combine the speaker identification results obtained through multiple microphones. In many cases, the composite result has lower error rate than that by any single channel. The proposed integration method can achieve more reliable identification performance in the ubiquitous robot companion (URC) environment in which the robot is connected to a server through an extremely high broadband penetration rate.

Automatic prosody labeling is the task of automatically annotating prosodic labels such as syllable stresses or break indices into speech corpora. Prosody-labeled corpora are important for speech synthesis and automatic speech understanding. However, the subtleness of physical features makes accurate labeling difficult. Since errors in the prosodic labels can lead to incorrect prosody estimation and unnatural synthetic sound, the accuracy of the labels is a key factor for text-to-speech (TTS) systems. In particular, mora accent labels relevant to pitch are very important for Japanese, since Japanese is a pitch-accent language and Japanese people have a particularly keen sense of pitch accents. However, the determination of the mora accents of Japanese is a more difficult task than English stress detection in a way. This is because the context of words changes the mora accents within the word, which is different from English stress where the stress is normally put at the lexical primary stress of a word. In this paper, we propose a method that can accurately determine the prosodic labels of Japanese using both acoustic and linguistic models. A speaker-independent linguistic model provides mora-level knowledge about the possible correct accentuations in Japanese, and contributes to reduction of the required size of the speaker-dependent speech corpus for training the other stochastic models. Our experiments show the effectiveness of the combination of models.

Machine learning techniques have long been applied in many fields and have gained a lot of success. The purpose of learning processes is generally to obtain a set of parameters based on a given data set by minimizing a certain objective function which can explain the data set in a maximum likelihood or minimum estimation error sense. However, most of the learned parameters are highly data dependent and rarely reflect the true physical mechanism that is involved in the observation data. In order to obtain the inherent knowledge involved in the observed data, it is necessary to combine physical models with learning process rather than only fitting the observations with a black box model. To reveal underlying properties of human speech production, we proposed a learning process based on a physiological articulatory model and a coarticulation model, where both of the models are derived from human mechanisms. A two-layer learning framework was designed to learn the parameters concerned with physiological level using the physiological articulatory model and the parameters in the motor planning level using the coarticulation model. The learning process was carried out on an articulatory database of human speech production. The learned parameters were evaluated by numerical experiments and listening tests. The phonetic targets obtained in the planning stage provided an evidence for understanding the virtual targets of human speech production. As a result, the model based learning process reveals the inherent mechanism of the human speech via the learned parameters with certain physical meaning.

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach.

Dividing a lecture speech into segments and providing those segments as learning objects are quite general and convenient way to construct e-learning resources. However it is difficult to assign an appropriate title to each object that reflects its content. Since there are various aspects of analyzing discourse segments, it is inevitable that researchers will face the diversity when describing the "meanings" of discourse segments. In this paper, we propose the assignment of discourse segment titles from the representation of their "meanings." In this assigning procedure, we focus on the speaker's evaluation for the event or the speech object. To verify the effectiveness of our idea, we examined identification of the segment boundaries from the titles that were described in our procedure. We confirmed that the result of the identification was more accurate than that of intuitive identification.

This paper presents a method to automatically acquire a given noun's telic and agentive roles from corpus data. These relations form part of the qualia structure assumed in the generative lexicon, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. Our proposed method employs a supervised machine-learning technique which makes use of template-based contextual features derived from token instances of each noun. The output of our method is a ranked list of verbs for each noun, across the different qualia roles. We also propose a variant of Spearman's rank correlation to evaluate the correlation of two top-N ranked lists. Using this correlation method, we represent the ability of the proposed method to identify qualia structure relative to a conventional template-based method.

We achieved the first 10 Gb/s WDM transmission at 1064 and 1550 nm over 24 km of photonic crystal fiber (PCF). We confirmed an improvement in the bit error rate (BER) performance after the transmission, namely "negative power penalties" of -0.5 and -0.3 dB at 1064 and 1550 nm, respectively. Our experimental result and theoretical estimation revealed that the signal degradation induced by the chromatic dispersion can be effectively suppressed by employing the pre-chirp technique with a conventional Z-cut lithium niobate (LN) modulator. We also show theoretically that we can expect to realize 10 Gb/s transmission over a 24 km PCF with negligible BER degradation in the 1060 to 1600 nm wavelength range by using the pre-chirp technique.

We design M(≥3)-phase spreading sequences of Markov chains optimal in terms of bit error probabilities in asynchronous SSMA (spread spectrum multiple access) communication systems. To this end, we obtain the distributions of the normalized MAI (multiple access interference) for such systems and find a necessary and sufficient condition that the distributions become independent of the phase shifts.

In conventional road-vehicle communication systems, user terminals in the vehicles have to directly connect to wireless access points (APs). However, vehicle speeds are so fast that the channel condition between the terminals and the APs constantly changes because of changing path loss and time-varying fading. In this paper, to compensate for such deterioration, we propose to reduce the relative speed between the terminals and the APs by an inter-vehicle packet relay technique. If a terminal can send data via other vehicles running at lower speeds, the relative speed will decrease, which suppresses the dynamic range of path loss and deterioration by fading. We, first, validate our method by a numerical analysis using a statistical path-loss model. The numerical analysis verifies that our method is able to suppress deterioration caused by path loss and time-varying fading. However, in the numerical analysis, geometric propagation of paths is not considered; instantaneous and rapid loss changes are not considered. Therefore, we evaluate our method by computer simulations using a geometric propagation model. In the simulations, phase difference between multiple paths and loss fluctuation within one frame duration affect the performance. From the results of the simulations, we validate our method. Furthermore, we investigate the combination of our method and the selection diversity technique, which can suppress channel fluctuation and may enhance the performance of our method. Moreover, we measure interference in the overlapped zone between two AP areas. From the measurement, we show that our packet relays do not cause a problem in interference between areas.

In this paper, we extend our framework of speculative computation in multi-agent systems by introducing default constraints. In research on multi-agent systems, handling incomplete information due to communication failure or due to other agents' delay in communication is a very important issue. For a solution to this problem, we previously proposed speculative computation based on abduction in the context of master-slave multi-agent systems and gave a procedure in abductive logic programming. In our previous proposal, a master agent prepares a default value for a yes/no question in advance, and it performs speculative computation using the default without waiting for a reply to the question. This computation is effective unless the contradictory reply to the default is returned. In this paper, we formalize speculative constraint processing, and propose a correct operational model for such computation so that we can handle not only yes/no questions, but also more general types of questions.

We present MobileOBJ, a formal framework for specifying and verifying mobile systems. Based on hidden algebra, the components of a mobile system are specified as behavioral objects or Observational Transition Systems, a kind of transition system, enriched with special action and observation operators related to the distinct characteristics of mobile computing systems. The whole system comes up as the concurrent composition of these components. The implementation of the abstract model is achieved using CafeOBJ, an executable, industrial strength algebraic specification language. The visualization of the specification can be done using CafeOBJ graphical notation. In addition, invariant and behavioral properties of mobile systems can be proved through theorem proving techniques, such as structural induction and coinduction that are fully supported by the CafeOBJ system. The application of the proposed framework is presented through the modeling of a mobile computing environment and the services that need to be supported by the former.

A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.

DTW-based text-dependent speaker verification technology is an effective scheme for protecting personal information in personal electronic products from others. To enhance the performance of a DTW-based system, an impostor database covering all possible passwords is generally required for the matching scores normalization. However, it becomes impossible in our practical application scenario since users are not restricted in their choice of password. We propose a method to generate pseudo-impostor data by employing an acoustic codebook. Based on the pseudo-impostor data, two normalization algorithms are developed. Besides, a template compression approach based on the codebook is introduced. Some modifications to the conventional DTW global constraints are also made for the compressed template. Combining the normalization and template compression methods, we obtain more than 66% and 35% relative reduction in storage and EER, respectively. We expect that other DTW-based tasks may also benefit from our methods.