Break arcs are generated between pure silver electrical contacts in a DC high-voltage resistive circuit. The break arc is driven by the external magnetic field of a permanent magnet from horizontal direction of contacts. Electrical contacts are separated at constant opening speed at 75 mm/s. The maximum supply voltage is 300 V. The maximum circuit current when electrical contacts are closed is 20 A. The maximum output power of the supply is limited to 6.0 kW. The gap between the contacts and the magnet is defined as x. The gap is varied from 2.5 mm to 10.0 mm to change the magnetic flux density that affects the break arc. The break arc is observed with a high-speed camera. The effect of the magnetic field on the arc duration was examined. As a result, break arcs are successfully extinguished by the transverse magnetic field when the gap x is 2.5 mm. Then the length of the break arc just before lengthening of the break arc L and the Lorentz force that affects the break arc F are examined. The length L was almost constant for each gap x and independent of the circuit current I and the Lorentz force F. The break arc is driven by the magnetic field when the arc length reached a certain length that was determined by the strength of the magnetic flux density.

We present a new discriminative method of acoustic model adaptation that deals with a task-dependent speech variability. We have focused on differences of expressions or speaking styles between tasks and set the objective of this method as improving the recognition accuracy of indistinctly pronounced phrases dependent on a speaking style. The adaptation appends subword models for frequently observable variants of subwords in the task. To find the task-dependent variants, low-confidence words are statistically selected from words with higher frequency in the task's adaptation data by using their word lattices. HMM parameters of subword models dependent on the words are discriminatively trained by using linear transforms with a minimum phoneme error (MPE) criterion. For the MPE training, subword accuracy discriminating between the variants and the originals is also investigated. In speech recognition experiments, the proposed adaptation with the subword variants reduced the word error rate by 12.0% relative in a Japanese conversational broadcast task.

Since a radio frequency identification (RFID) transponder (tag) generates both location and time information when it enters and leaves a reader, the trajectory of a moving, tagged object can be traced. Due to the time intervals between entries to successive readers, during which tags are not tracked, accurate tracing of complete trajectories can be difficult. To overcome this problem, we propose a tag trajectory indexing scheme called TR-tree (R-tree-based tag trajectory index) that can trace tags by combining the local trajectories at each reader. In experiments, this scheme showed superior performance compared with other indices.

In order to clarify the physics of contact life time, the relationship between heat capacity of holder and shape of bridge (length and diameter) is discussed in this paper. The AgPd60 alloy is chosen as electrode material. Two holders with different heat capacity are comprised of copper plate and cylinder. The shape of the bridge at the low speed breaking contact is observed by using the high speed digital camera. It was demonstrated that the shape of the bridge is changed by the response and distribution of the temperature.

We propose novel tree construction algorithms for multicast communication in photonic networks. Since multicast communications consume many more link resources than unicast communications, effective algorithms for route selection and wavelength assignment are required. We propose a novel tree construction algorithm, called the Weighted Steiner Tree (WST) algorithm and a variation of the WST algorithm, called the Composite Weighted Steiner Tree (CWST) algorithm. Because these algorithms are based on the Steiner Tree algorithm, link resources among source and destination pairs tend to be commonly used and link utilization ratios are improved. Because of this, these algorithms can accept many more multicast requests than other multicast tree construction algorithms based on the Dijkstra algorithm. However, under certain delay constraints, the blocking characteristics of the proposed Weighted Steiner Tree algorithm deteriorate since some light paths between source and destinations use many hops and cannot satisfy the delay constraint. In order to adapt the approach to the delay-sensitive environments, we have devised the Composite Weighted Steiner Tree algorithm comprising the Weighted Steiner Tree algorithm and the Dijkstra algorithm for use in a delay constrained environment such as an IPTV application. In this paper, we also give the results of simulation experiments which demonstrate the superiority of the proposed Composite Weighted Steiner Tree algorithm compared with the Distributed Minimum Hop Tree (DMHT) algorithm, from the viewpoint of the light-tree request blocking.

In this paper, we propose an acoustic model that is robust to multiple noise environments, as well as a method for adapting the acoustic model to an environment to improve the model. The model is called "the multi-mixture model," which is based on a mixture of different HMMs each of which is trained using speech under different noise conditions. Speech recognition experiments showed that the proposed model performs better than the conventional multi-condition model. The method for adaptation is based on the aspect model, which is a "mixture-of-mixture" model. To realize adaptation using extremely small amount of adaptation data (i.e., a few seconds), we train a small number of mixture models, which can be interpreted as models for "clusters" of noise environments. Then, the models are mixed using weights, which are determined according to the adaptation data. The experimental results showed that the adaptation based on the aspect model improved the word accuracy in a heavy noise environment and showed no performance deterioration for all noise conditions, while the conventional methods either did not improve the performance or showed both improvement and degradation of recognition performance according to noise conditions.

In this paper, the robustness of the posterior-based confidence measures is improved by utilizing entropy information, which is calculated for speech-unit-level posteriors using only the best recognition result, without requiring a larger computational load than conventional methods. Using different normalization methods, two posterior-based entropy confidence measures are proposed. Practical details are discussed for two typical levels of hidden Markov model (HMM)-based posterior confidence measures, and both levels are compared in terms of their performances. Experiments show that the entropy information results in significant improvements in the posterior-based confidence measures. The absolute improvements of the out-of-vocabulary (OOV) rejection rate are more than 20% for both the phoneme-level confidence measures and the state-level confidence measures for our embedded test sets, without a significant decline of the in-vocabulary accuracy.

This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices usually sound unnatural compared with normal speech. To improve the intelligibility and naturalness of esophageal speech, we propose a voice conversion method from esophageal speech into normal speech. A spectral parameter and excitation parameters of target normal speech are separately estimated from a spectral parameter of the esophageal speech based on Gaussian mixture models. The experimental results demonstrate that the proposed method yields significant improvements in intelligibility and naturalness. We also apply one-to-many eigenvoice conversion to esophageal speech enhancement to make it possible to flexibly control the voice quality of enhanced speech.

Requirements changes sometimes cause a project to fail. A lot of projects now follow incremental development processes so that new requirements and requirements changes can be incorporated as soon as possible. These processes are called integrated requirements processes, which function to integrate requirements processes with other developmental processes. We have quantitatively and qualitatively investigated the requirements processes of a specific project from beginning to end. Our focus is to clarify the types of necessary requirements based on the components contained within a certain portion of the software architecture. Further, each type reveals its typical requirements processes through its own rationale. This case study is a system to manage the orders and services of a restaurant. In this paper, we introduce the case and categorize its requirements processes based on the components of the system and the qualitative characteristics of ISO-9126. We could identify seven categories of the typical requirements process to be managed and/or controlled. Each category reveals its typical requirements processes and their characteristics. The case study is our first step of practical integrated requirements engineering.

IEEE 802.15.4a standard enables location-aided routing or topology control in ZigBee networks, since it uses time-of-arrival (TOA)-based ranging technique. However, TOA based techniques may yield location error due to the non-line-of-sight (NLOS) effects, and hence degrade the network performance. In this letter, we demonstrate the impact of NLOS on the localization performance and propose a location error detection and compensation algorithm for IEEE 802.15.4a networks. The proposed algorithm detects NLOS by using the min-max algorithm and compensates the location error by using the Kalman filter. Experimental results show that the proposed algorithm significantly reduces the localization errors in indoor environments.

An assume-guarantee verification method has been recognized as a promising approach to verify component-based software by model checking. This method is not only fitted to component-based software but also has a potential to solve the state space explosion problem in model checking. The method allows us to decompose a verification target into components so that we can model check each of them separately. In this method, assumptions are seen as the environments needed for the components to satisfy a property and for the rest of the system to be satisfied. The number of states of the assumptions should be minimized because the computational cost of model checking is influenced by that number. Thus, we propose a method for generating minimal assumptions for the assume-guarantee verification of component-based software. The key idea of this method is finding the minimal assumptions in the search spaces of the candidate assumptions. The minimal assumptions generated by the proposed method can be used to recheck the whole system at much lower computational cost. We have implemented a tool for generating the minimal assumptions. Experimental results are also presented and discussed.

This paper describes the circuit design and measured performance of a high-speed digital-to-analog converter (DAC) for the next generation of coherent optical communications systems. To achieve high-speed and low-power operation, we used an R-2R current-steering architecture and devised timing alignment and waveform improvement techniques. A 6-bit DAC test chip was fabricated with InP HBT technology, which yields a peak ft of 175 GHz and a peak fmax of 260 GHz. The measured differential and integral non-linearity (DNL and INL) are within +0.61/-0.07 LSB and +0.27/-0.52 LSB, respectively. The measured spurious-free dynamic range (SFDR) is 44.7 dB for a sinusoidal output of 72.5 MHz at a sampling rate of 13.5 GS/s, which was the limit of our measurement setup. The expected ramp-wave outputs at a sampling rate of 24 GS/s are also obtained. The total power consumption is as low as 0.88 W with a supply voltage of -4.0 V. This DAC can provide low-power operation and a higher sampling rate than any other previously reported DAC with a resolution of 5 bits or more.

In this study, we construct an analytical model to investigate the system throughput of 802.15.3c WPAN by examining hybrid slotted CSMA/CA-TDMA and slotted CSMA/CA multiple access methods. Our analysis clearly shows the differences between the system throughputs of both multiple access methods. The obtained results show that the hybrid slotted CSMA/CA-TDMA can achieve a considerably higher system throughput compared to the slotted CSMA/CA; the difference between the two access methods is especially pronounced as the increase in the number of devices contending for the network increase. The system throughput comparisons have established why the hybrid slotted CSMA/CA-TDMA is preferred over the slotted CSMA/CA for high-speed wireless communications of the 802.15.3c WPAN.

Novel thermopiles based on modulation doped AlGaAs/InGaAs, AlGaN/GaN, and ZnMgO/ZnO heterostructures are proposed and designed for the first time, for uncooled infrared image sensor application. These devices are expected to offer high performances due to both the superior Seebeck coefficient and the excellently high mobility of 2DEG and 2DHG due to high purity channel layers at the heterojunction interface. The AlGaAs/InGaAs thermopile has the figure-of-merit Z of as large as 1.110-2/K (ZT = 3.3 over unity at T = 300 K), and can be realized with a high responsivity R of 15,200 V/W and a high detectivity D* of 1.8109 cmHz1/2/W with uncooled low-cost potentiality. The AlGaN/GaN and the ZnMgO/ZnO thermopiles have the advantages of high sheet carrier concentration due to their large polarization charge effects (spontaneous and piezo polarization charges) as well as of a high Seebeck coefficient due to their strong phonon-drag effect. The high speed response time τ of 0.9 ms with AlGaN/GaN, and also the lower cost with ZnMgO/ZnO thermopiles can be realized. The modulation-doped heterostructure thermopiles presented here are expected to be used for uncooled infrared image sensor applications, and for monolithic integrations with other photon detectors such as InGaAs, GaN, and ZnO PiN photodiodes, as well as HEMT functional integrated circuit devices.

This paper presents a novel noise-robust feature extraction method for speech recognition. It is based on making the Minimum Variance Distortionless Response (MVDR) power spectrum estimation method robust against noise. This robustness is obtained by modifying the distortionless constraint of the MVDR spectral estimation method via weighting the sub-band power spectrum values based on the sub-band signal to noise ratios. The optimum weighting is obtained by employing the experimental findings of psychoacoustics. According to our experiments, this technique is successful in modifying the power spectrum of speech signals and making it robust against noise. The above method, when evaluated on Aurora 2 task for recognition purposes, outperformed both the MFCC features as the baseline and the MVDR-based features in different noisy conditions.

The proposed automated scoring system for English writing tests provides an assessment result including a score and diagnostic feedback to test-takers without human's efforts. The system analyzes an input sentence and detects errors related to spelling, syntax and content similarity. The scoring model has adopted one of the statistical approaches, a regression tree. A scoring model in general calculates a score based on the count and the types of automatically detected errors. Accordingly, a system with higher accuracy in detecting errors raises the accuracy in scoring a test. The accuracy of the system, however, cannot be fully guaranteed for several reasons, such as parsing failure, incompleteness of knowledge bases, and ambiguous nature of natural language. In this paper, we introduce an error-weighting technique, which is similar to term-weighting widely used in information retrieval. The error-weighting technique is applied to judge reliability of the errors detected by the system. The score calculated with the technique is proven to be more accurate than the score without it.

In this paper, we propose a new method that employs two novel features, correlation density (Cd) and fractal dimension (Fd), to recognize emotional states contained in speech. The former feature obtained by a list of parametric filters reflects the broad frequency components and the fine structure of lower frequency components, contributed by unvoiced phones and voiced phones, respectively; the latter feature indicates the non-linearity and self-similarity of a speech signal. Comparative experiments based on Hidden Markov Model and K Nearest Neighbor methods are carried out. The results show that Cd and Fd are much more closely related with emotional expression than the features commonly used.

The use of the in-phase synthetic method is proposed for antenna calibration using the three-antenna method (TAM) in order to make the TAM applicable even in a semi-anechoic chamber (SAC) or on an open-area test site. Suitable antenna arrangements are theoretically investigated for this improved calibration method. Experimental analyses demonstrate that the in-phase synthetic method can remarkably reduce unwanted effects of the ground-reflected wave. Therefore, even on a metal ground plane, the proposed TAM with the in-phase synthetic method can yield an accurate actual gain of a double ridged guide antenna at frequencies from 4 GHz to 14 GHz with differences of +0.16/-0.37 dB from the results of the conventional TAM performed in an fully anechoic room (FAR).

IEEE 802.16m is an advanced air interface standard which is under development for IMT-Advanced systems, known as 4G systems. IEEE 802.16m is designed to provide a high data rate and a Quality of Service (QoS) level in order to meet user service requirements, and is especially suitable for mobilized environments. There are several factors that have great impact on such requirements. As one of the major factors, we mainly focus on latency issues. In IEEE 802.16m, an enhanced layer 2 handover scheme, described as Entry Before Break (EBB) was proposed and adopted to reduce handover latency. EBB provides significant handover interruption time reduction with respect to the legacy IEEE 802.16 handover scheme. Fast handovers for mobile IPv6 (FMIPv6) was standardized by Internet Engineering Task Force (IETF) in order to provide reduced handover interruption time from IP layer perspective. Since FMIPv6 utilizes link layer triggers to reduce handover latency, it is very critical to jointly design FMIPv6 with its underlying link layer protocol. However, FMIPv6 based on new handover scheme, EBB has not been proposed. In this paper, we propose an improved cross-layering design for FMIPv6 based on the IEEE 802.16m EBB handover. In comparison with the conventional FMIPv6 based on the legacy IEEE 802.16 network, the overall handover interruption time can be significantly reduced by employing the proposed design. Benefits of this improvement on latency reduction for mobile user applications are thoroughly investigated with both numerical analysis and simulation on various IP applications.

The construction of annotated corpora requires considerable manual effort. This paper presents a pragmatic method to minimize human intervention for the construction of Korean part-of-speech (POS) tagged corpus. Instead of focusing on improving the performance of conventional automatic POS taggers, we devise a discriminative POS tagger which can selectively produce either a single analysis or multiple analyses based on the tagging reliability. The proposed approach uses two decision rules to judge the tagging reliability. Experimental results show that the proposed approach can effectively control the quality of corpus and the amount of manual annotation by the threshold value of the rule.