We propose a blind dereverberation method based on spectral subtraction using a multi-channel least mean squares (MCLMS) algorithm for distant-talking speech recognition. In a distant-talking environment, the channel impulse response is longer than the short-term spectral analysis window. By treating the late reverberation as additive noise, a noise reduction technique based on spectral subtraction was proposed to estimate the power spectrum of the clean speech using power spectra of the distorted speech and the unknown impulse responses. To estimate the power spectra of the impulse responses, a variable step-size unconstrained MCLMS (VSS-UMCLMS) algorithm for identifying the impulse responses in a time domain is extended to a frequency domain. To reduce the effect of the estimation error of the channel impulse response, we normalize the early reverberation by cepstral mean normalization (CMN) instead of spectral subtraction using the estimated impulse response. Furthermore, our proposed method is combined with conventional delay-and-sum beamforming. We conducted recognition experiments on a distorted speech signal simulated by convolving multi-channel impulse responses with clean speech. The proposed method achieved a relative error reduction rate of 22.4% in relation to conventional CMN. By combining the proposed method with beamforming, a relative error reduction rate of 24.5% in relation to the conventional CMN with beamforming was achieved using only an isolated word (with duration of about 0.6 s) to estimate the spectrum of the impulse response.

AC-waveform synthesis with quantum-mechanical accuracy has been attracting many researchers, especially metrologists in national metrology institutes, not only for its scientific interest but its potential benefit to industries. We describe the current status at National Metrology Institute of Japan of development of a Josephson arbitrary waveform synthesizer based on programmable and pulse-driven Josephson junction arrays.

This paper proposes a new method to reduce the cost of nearest neighbor searches in metric spaces. Many similarity search indexes recursively divide a region into subregions by using pivots, and construct a tree-structured index. Most of recently developed indexes focus on pruning objects and do not pay much attention to the tree balancing. As a result, indexes having imbalanced tree-structure may be constructed and the search cost is degraded. We propose a similarity search index called the Partitioning Capacity (PC) Tree. It selects the optimal pivot in terms of the PC that quantifies the balance of the regions partitioned by a pivot as well as the estimated effectiveness of the search pruning by the pivot. As a result, PCTree reduces the search cost for various data distributions. We experimentally compared PCTree with four indexes using synthetic data and five real datasets. The experimental results shows that the PCTree successfully reduces the search cost.

In this paper, we propose a method for robot self-position identification by active sound localization. This method can be used for autonomous security robots working in room environments. A system using an AIBO robot equipped with two microphones and a wireless network is constructed and used for position identification experiments. Differences in arrival time to the robot's microphones are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head-position measurement method is proposed. The position of robot can be identified by the intersection of circles restricted using the azimuth differences among different sound beacon pairs. By localizing three or four loudspeakers as sound beacons positioned at known locations, the robot can identify its position with an average error of 7 cm in a 2.53.0 m2 working space in the horizontal plane. We propose adjusting the arrival time differences (ATDs) to reduce the errors caused when the sound beacons are high mounted. A robot navigation experiment was conducted to demonstrate the effectiveness of the proposed position-identification system.

A new selective retransmission method for HARQ (Hybrid Automatic Repeat reQuest) systems is proposed. This method can avoid the blindness of symbols retransmission by the transformation of lostmap matrix and simulation results show that the proposed method can reduce the number of retransmissions effectively.

We describe the fabrication processes and electrical characteristics of two types of NbN junctions. One is a self-shunted NbN/NbNx/AlN/NbN Josephson junction, which is expected to improve the density of integrated circuits; the other is an underdamped NbN/AlNx/NbN tunnel junction with radical-nitride AlNx barriers, which has highly controllable junction characteristics. In the former, the junction characteristics were changed from underdamped to overdamped by varying the thickness of the NbNx layer. Overdamped junctions with a 6-nm-thick NbNx film exhibited a characteristic voltage of Vc = 0.8 mV and a critical current density of Jc = 22 A/cm2 at 4.2 K. In the junctions with radical-nitride AlNx barriers, Jc could be controlled in the range 0.01-3 kA/cm2 by varying the process conditions, and good uniformity of the junction characteristics was obtained.

Although larger scale integration enhances the practicability of superconducting Josephson circuits, several technical problems begin to emerge during its progress. One of the problems is the increase of current through a ground plane (ground current). Excess ground current produces additional magnetic field and reduces operation margins of the circuits, because superconducting Josephson devices are very sensitive to magnetic field. In this paper, we evaluate current distribution in a superconducting ground plane by means of both experiments and numerical calculation. We also verify two methods for suppressing the ground current. One is a slot structure in the ground plane, and the other is alignment of the current-extraction point. Suppression of the ground current is quantitatively evaluated.

We propose a neutron diffractometer system based on MgB2 thin film detectors and an SFQ signal processor. Small dimensions of MgB2 thin film detectors and high processing capability of the single flux quantum (SFQ) circuits enable us to handle several thousand or more detectors in a cryocooler, leading to a very compact system. In addition, the system can provide many diffraction patterns for different kinetic energies simultaneously. Kinetic energy is determined for individual neutrons by means of the time-of-flight method by using SFQ time-to-digital converters (TDCs). Digital outputs of the TDCs are multiplexed in time domain and sent to room-temperature electronics with reduced number of cables. A dual-input SFQ signal processor including TDCs and a multiplexer has been successfully demonstrated with a time resolution of 20 ns and power consumption of 400 µW. These values show high feasibility of the neutron diffraction system proposed here.

This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.

This study shows a fast simulation method for turbo codes over an additive white class A noise (AWAN) channel. The reduction of the estimation time is achieved by applying importance sampling (IS) which is one of the variance reduction simulation methods. In order to adapt the AWAN channel, we propose a design method of a simulation probability density function (PDF) utilized in IS. The proposed simulation PDF is related to the Bhattacharyya bound to evaluate wider area of the signal space than the conventional method. Since the mean translation method, which is a conventional design method of the simulation PDF used in IS, is optimized for an additive white Gaussian noise channel, the evaluation time of the error performance of turbo codes over the AWAN channel can not be reduced. To evaluate BER of 10-8, the simulation time of the proposed method can be reduced to 1/104 under the condition of the same accuracy of the traditional Monte Carlo simulation method.

A critical problem with wireless LAN-based positioning systems is the degradation in position estimation accuracy due to complex variation in the strength of the received signal, which originates in the nature of the underlying wireless channel. Handling such variation effectively is essential for the creation of practical wireless LAN-based positioning systems. In this paper, we propose a collaborative mechanism for correcting position estimation errors. The main objective is to assist users in correcting estimation errors manually by providing access to a shared body of accumulated information on corrections made by many other users. In particular, the mechanism is designed to enable any group of users to collaboratively build upon this body of information. Finally, we evaluate the effectiveness of the proposed mechanism through experiments. The results confirm that the proposed mechanism can provide effective estimation error correction in a realistic environment.

A class of balanced semi-bent functions with an even number of variables is proposed. It is shown that they include one subclass of semi-bent functions with maximum algebraic degrees. Furthermore, an example of semi-bent functions in a small field is given by using the zeros of some Kloosterman sums. Based on the result given by S.Kim et al., an example of infinite families of semi-bent functions is also obtained.

Bandwidth is an extremely valuable and scarce resource in multimedia networks. Therefore, efficient bandwidth management is necessary in order to provide high Quality of Service (QoS) to users. In this paper, a new QoS-aware bandwidth allocation algorithm is proposed for the efficient use of available bandwidth. By using the multi-objective optimization technique and Talmud allocation rule, the bandwidth is adaptively controlled to maximize network efficiency while ensuring QoS provisioning. In addition, we adopt the online feedback strategy to dynamically respond to current network conditions. With a simulation study, we demonstrate that the proposed algorithm can adaptively approximate an optimized solution under widely diverse traffic load intensities.

Flash SSDs are being incorporated in many enterprise storage platforms recently and expected to play a notable role for IO-intensive applications. However, the IO characteristics of flash SSDs are very different from those of hard disks. Since existent storage subsystems are designed on the basis of characteristics of hard disks, the IO performance of flash SSDs may not be obtained as expected. This paper provides an evaluation of flash SSDs in transaction processing systems with TPC-C benchmark. We present performance results with various configurations and describe our observations of the IO behaviors at different levels along the IO path, which helps to understand the performance of flash-based transaction processing systems and provides certain references to build flash-based systems for IO-intensive applications.

This letter introduces a closed form expression for the channel capacity increase achieved by adding a new pair of transmit and receive antennas. By analyzing this expression, an iterative transmit/receive antenna selection algorithm of low computational complexity is proposed. The new algorithm has higher computational complexity than some existing algorithms, but as the results show, the performance improvement of the proposed algorithm approaching more to the optimal algorithm.

Joint source-channel coding (JSCC) is a method to jointly allocate the given total transmission bitrate to the source coding and channel coding to maximize the video quality at the receiving end. In this paper, we propose a practical model for efficiently determining a near-optimal code rate for JSCC in real-time video communications. The conventional code rate decision schemes using analytical source coding distortion model and channel-induced distortion model are usually complex, and typically employ the process of model parameter training which involves potentially high computational complexity and implementation cost. To avoid the complex modeling procedure, we introduce a very simple video quality model based on the playable bitrate which is defined as the total bit amount per unit time that is not affected by the channel loss during transmission including correctly recovered bits by the channel decoder. Because the video quality at the receiving end is clearly commensurate with the playable bitrate, we can easily determine the quality-oriented near-optimal code rate by finding the code rate that maximizes the playable bitrate at the sender side. The proposed playable bitrate model is very simple because it does not require the complex training procedure for obtaining model parameters, which is usually required in the conventional code rate decision method. It is shown by simulations that the proposed code rate decision scheme based on the playable bitrate model can efficiently determine the near-optimal code rate for JSCC in terms of high accuracy on the optimal code rate.

Based on sliding-window rule application and extraction filtering, we present a framework for extracting multi-slot frames describing chemical reactions from Thai free text with unknown target-phrase boundaries. A supervised rule learning algorithm is employed for automatic construction of pattern-based extraction rules from hand-tagged training phrases. A filtering method is devised for removal of incorrect extraction results based on features observed from text portions appearing between adjacent slot fillers in source documents. Extracted reaction frames are represented as concept expressions in description logics and are used as metadata for document indexing. A document knowledge base supporting semantics-based information retrieval is constructed by integrating document metadata with domain-specific ontologies.

We report on the enhancement of system detection efficiency in a superconducting nanowire single-photon detector (SNSPD) by applying the optical cavity structure. The nanowire was made using 4-nm-thick NbN thin films and covered with an SiO cavity and Au mirror designed for 1300-1600 nm wavelengths. The device is mounted into fiber-coupled packages, and installed in a practical multichannel system based on GM cryocoolers. System detection efficiency depends on the absorptance of cavity structure, and reached 28% and 40% at 1550 nm and 1310 nm wavelengths, respectively. These values were considerably higher than an SNSPD without optical cavity.

One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.

This paper presents a framework of supporting system for a drug formulation. We designed ontology to represent the related knowledge for reusable and sharing purposes. The designed ontology is applied with operation rules to suggest an appropriate generic drug production based on information of original drug. The system also provides a validation module to preliminarily approve a pharmaceutical equivalence of the suggested result. Preliminary testing with four random samples shows potential to reformulate a generic product by returning a satisfactory and acceptable of the system suggestions for all samples.