Recently, many algorithms have been proposed for fast full search motion estimation. Among them, successive elimination algorithm (SEA) and its modified algorithms significantly speed up the performance of the full search algorithm. By introducing the inequality equation between the norm and the mean absolute difference (MAD) of two matching blocks, the SEA can successively eliminate invalid candidate blocks without any loss in estimation accuracy. In this paper, we propose a partial norm based early rejection algorithm (PNERA) for fast block motion estimation. The proposed algorithm employs the sum of partial norms from several subblocks of the block. Applying the sum of partial norms to the inequality equation, we can significantly reduce the computational complexity of the full search algorithm. In an attempt to reduce the computational load further, the modified algorithms using partial norm distortion elimination (PNDE) and subsampling methods are also proposed. Experimental results show that the proposed algorithm is about 4 to 9 times faster than the original exhaustive full search, and is about 3 to 4 times faster than the SEA.

This paper investigates the parameter tying structures of a mixture of factor analyzers (MFA) and discriminative training of MFA for speaker identification. The parameters of factor loading matrices or diagonal matrices are shared in different mixtures of MFA. Then, minimum classification error (MCE) training is applied to the MFA parameters to enhance the discrimination ability. The result of a text-independent speaker identification experiment shows that MFA outperforms the conventional Gaussian mixture model (GMM) with diagonal or full covariance matrices and achieves the best performance when sharing the diagonal matrices, resulting in a relative gain of 26% over the GMM with diagonal covariance matrices. The improvement is more significant especially in sparse training data condition. The recognition performance is further improved by MCE training with an additional gain of 3% error reduction.

This paper describes techniques of scoring prosodic proficiency of English sentences spoken by Japanese. The multiple regression model predicts the prosodic proficiency using new prosodic measures based on the characteristics of Japanese novice learners of English. Prosodic measures are calculated by comparing prosodic parameters, such as F0, power and duration, of learner's and native speaker's speech. The new measures include the approximation error of the fitting line and the comparison result of prosodic parameters for a limited segment of the word boundary rather than the whole utterance. This paper reveals that the introduction of the new measures improved the correlation by 0.1 between the teachers' and automatic scores.

This paper presents an analysis of the applicability of Sparse Kernel Principal Component Analysis (SKPCA) for feature extraction in speech recognition, as well as, a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is an usual context in speech recognition systems. Although the KPCA (Kernel Principal Component Analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. This data reduction is important to avoid computational unfeasibility and/or an extremely high computational burden related to the feature representation step of the training and the test data evaluations. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA, which nevertheless is not realizable for large training databases. The proposed approach consists in clustering the training data and applying to these clusters a SKPCA like data reduction technique generating the reduced data clusters. These reduced data clusters are merged and reduced in a recursive procedure until just one cluster is obtained, making the SKPCA approach realizable for a large amount of training data. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.

Asynchronous optical packet switching (OPS) is a promising solution to support the continuous growth of transmission capacity demand. It has been, however, quite difficult to implement key functions needed at the node of such networks with all-optical approaches. We have proposed a new optoelectronic system composed of a packet-by-packet optical clock-pulse generator (OCG), an all-optical serial-to-parallel converter (SPC), a photonic parallel-to-serial converter (PSC), and CMOS circuitry. The system makes it possible to carry out various required functions such as buffering (random access memory), optical packet compression/decompression, and optical label swapping for high-speed asynchronous optical packets.

Paralleled converter system with synchronous rectifiers (SRs) causes several problems such as surge voltage, inhalation current and circulating current. Generally, the system stops operation of the SRs in light load to avoid these problems. However, simultaneously, large voltage fluctuations in the output of the modules are occurred due to forward voltage drop of diode. The fluctuations cause serious faults to the semiconductor devices working in very low voltage such as CPU and VLSI. Moreover, the voltage fluctuations generate unstable current fluctuations in the paralleled converter system with current-sharing control. This paper proposes new switching control methods for rectifiers to reduce the voltage and current fluctuations. The effectiveness of the proposed methods is confirmed by computer simulation and experimental results.

System-level fault diagnosis deals with the problem of identifying faulty nodes (processors) in a multiprocessor system. Each node is faulty or fault-free, and it can test other nodes in the system, and outputs the test results. The test result from a node is reliable if the node is fault-free, but the result is unreliable if it is faulty. In this paper, we prove that four variants of the hypercube: the crossed cube, the twisted cube, the Mobius cube, and the enhanced cube, are adaptively diagnosed using at most 4 parallel testing rounds, with at most n faulty nodes (for the enhanced cube, with at most n + 1 faulty nodes), where each processor participates in at most one test in each round. Furthermore, we propose another diagnosis algorithm for the n-dimensional enhanced cube with at most n + 1 faulty nodes, and show that it is adaptively diagnosed with at most 5 rounds in the worst case, but with at most 3 rounds if the number of existing faulty nodes is at most n -log(n + 1).

Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a novel method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. Experimental results reveal that the parsing accuracy reached 87.0%, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information.

This paper proposes a time alignment control (TAC) for reducing an influence of multiple access interference (MAI) due to propagation delays (PDs) in uplink transmission from multiple mobile stations (MSs) to an access point (AP) for an orthogonal frequency division multiple access (OFDMA) based mobile communication system. In addition, this paper presents our evaluation of the proposed TAC as applied to dynamic parameter control orthogonal frequency and time division multiple access (DPC-OF/TDMA) which has been suggested for use in new generation mobile communication system. This paper also proposes several formats for an activation slot (ACTS) in which the GIs are lengthened in order to avoid the MAI because the TAC cannot be performed yet in an initial registration of the MSs. Computer simulation elucidates that lengthening the GIs of data symbols in the ACTS adequately to compensate a maximum delay improves the transmission performance of the ACTS at the initial registration without PDs compensation. The simulation also elucidates that the proposed TAC is performed to reduce the influence of the MAI effectively and that updating the estimates of the PDs every certain period is needed to compensate the PDs accurately under high-mobility environment.

An unsupervised method for word-sense disambiguation using bilingual comparable corpora was developed. First, it extracts word associations, i.e., statistically significant pairs of associated words, from the corpus of each language. Then, it aligns word associations by consulting a bilingual dictionary and calculates correlation between senses of a target polysemous word and its associated words, which can be regarded as clues for identifying the sense of the target word. To overcome the problem of disparity of topical coverage between corpora of the two languages as well as the problem of ambiguity in word-association alignment, an algorithm for iteratively calculating a sense-vs.-clue correlation matrix for each target word was devised. Word-sense disambiguation for each instance of the target word is done by selecting the sense that maximizes the score, i.e., a weighted sum of the correlations between each sense and clues appearing in the context of the instance. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora together with the EDR bilingual dictionary showed that the new method has promising performance; namely, the F-measure of its sense selection was 74.6% compared to a baseline of 62.8%. The developed method will possibly be extended into a fully unsupervised method that features automatic division and definition of word senses.

This paper presents a new conic section extraction approach that can extract all conic sections (lines, circles, ellipses, parabolas and hyperbolas) simultaneously. This approach is faster than the conventional approaches with a computational complexity that is O(n), where n is the number of edge pixels, and is robust in the presence of moderate levels of noise. It has been combined with a classification tree to produce an offline character recognition system that is invariant to scale, rotation, and translation. The system was tested with synthetic images and with images scanned from real world sources with good results.

Recently Azou et al. proposed a method of model reduction for discrete systems based on a new impulse response Gramian. The reduced model was derived by first approximating the low-order impulse response Gramian, and then matching some Markov parameters and time-moments of an original model. In this note a modified method is presented so that the reduced model exactly preserves the low-order impulse response Gramian together with a slightly different set of Markov parameters and time-moments of the original model.

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the face search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to handle it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experiments on a set of 42 test images show the effectiveness of the proposed approach. Moreover, the effect of PSO parameter settings on the search performance was investigated.

In this letter, parallel interference cancellation (PIC) in code division multiple access (CDMA) was performed with two different structures by using a neural network (NN). In the first structure (receiver-1) the NN was used as a front-end stage of a one stage PIC circuit. In the second structure (receiver-2), the NN was used instead of the one stage PIC circuit and it was trained as a multiple access interference (MAI) detector to perform the PIC process by subtracting the MAI from the outputs of the matched filter. The PIC is a classical technique in multi user detection process and its bit error rate (BER) performance is not good in one stage for most of the applications. For improving its BER performance, generally a multi stage PIC which has the high computational complexity is used. In this study, we have gotten a better BER performance than a three stages PIC receiver with both proposed receivers that have the lower computational complexity.

An improved method for extracting translation equivalents from bilingual comparable corpora according to contextual similarity was developed. This method has two main features. First, a seed bilingual lexicon--which is used to bridge contexts in different languages--is adapted to the corpora from which translation equivalents are to be extracted. Second, the contextual similarity is evaluated by using a combination of similarity measures defined in opposite directions. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora, together with the EDR bilingual dictionary, demonstrated the effectiveness of the method; it produced lists of candidate translation equivalents with an accuracy of around 30% for frequently occurring unknown words. The method thus proved to be useful for improving the coverage of a bilingual lexicon.

Magnetic immunoassays utilizing magnetic marker and high Tc superconducting quantum interference device (SQUID) have been performed. In this magnetic method, binding-reaction between an antigen and its antibody is detected by measuring the magnetic field from the magnetic marker. First, we discuss the magnetic property of the marker, and show that Fe3O4 particles with diameter of 25 nm can be used for remanence measurement. We also show a design of the SQUID for sensitive detection of the magnetic signal from the marker. Next, we developed a measurement system utilizing the SQUID and a reaction chamber with very low magnetic contamination. Finally, we conducted an experiment on the detection of the biological materials called IL8 and IgE. At present, a few atto-mol of IL8 and IgE has been detected, which shows the high sensitivity of the present method.

Two methods using comparable corpora to select translation equivalents appropriate to a domain were devised and evaluated. The first method ranks translation equivalents of a target word according to similarity of their contexts to that of the target word. The second method ranks translation equivalents according to the ratio of associated words that suggest them. An experiment using the EDR bilingual dictionary together with Wall Street Journal and Nihon Keizai Shimbun corpora showed that the method using the ratio of associated words outperforms the method based on contextual similarity. Namely, in a quantitative evaluation using pseudo words, the maximum F-measure of the former method was 86%, while that of the latter method was 82%. The key feature of the method using the ratio of associated words is that it outputs selected translation equivalents together with representative associated words, enabling the translation equivalents to be validated.

This paper discusses real-time language recognition by 1-dimensional one-way cellular automata (OCAs) and two-way cellular automata (CAs), focusing on limitations of the parallel computation power. To clarify the limitations, we investigate real-time recognition of cyclic strings of the form uk with u {0,1}+ and k 2. We show a version of pumping lemma for recognizing cyclic strings by OCAs, which can be used for proving that several languages are not recognizable by OCAs in real time. The paper also discusses the real-time language recognition of CAs by prefix and postfix computation, in which every prefix or postfix of an input string is also accepted, if the prefix or postfix is in the language. It is shown that there are languages L Σ+ such that L is not recognizable by OCA in real-time and the reversal of L and the concatenation LΣ* are recognizable by CA in real-time.

We show that a problem of deciding whether a formula for a multivariate polynomial of n variables over a finite field of characteristic 2 has degree n when reduced modulo a certain Boolean ideal belongs to P. When the formula is allowed to have succinct representations as sums of monomials, the problem becomes P-complete.

In this study, we propose an object-based multimedia model for specifying the QoS (quality of service) requirements, such as the maximum data-dropping rate or the maximum data-delay rate. We also present a resource allocation model, called the net-profit model, in which the satisfaction of user's QoS requirements is measured by the benefit earned by the system. Based on the net-profit model, the system is rewarded if it can allocate enough resources to a multimedia delivery request and fulfill the QoS requirements specified by the user. At the same time, the system is penalized if it cannot allocate enough resources to a multimedia delivery request. We first investigate the problem of how to allocate resources efficiently, so that the QoS satisfaction is maximized. However, the net-profit may be distributed unevenly among the multimedia delivery requests. Thus, the second problem discusses how to allocate the resource efficiently so that the net-profit difference is minimized between any two multimedia requests. A dynamic programming based algorithm is proposed to find such an optimal solution with the minimum net-profit differences.