In optical burst switching (OBS) networks, burst with different numbers of hops experience unfairness in terms of the burst loss probability. In this paper, we propose a preemptive scheme based on the number of transit hops in OBS networks. In our proposed scheme, preemption is performed with two thresholds; one is for the total number of hops of a burst and the other is for the number of transit hops the burst has passed through. We evaluate the performance of the scheme by simulation, and numerical examples show that the proposed scheme improves the fairness among the bursts with different numbers of hops, keeping the overall burst loss probability the same as that for the conventional OBS transmission without preemption.

In this paper, we propose a novel sound field reproduction system that uses the simultaneous perturbation (SP) method as well as two fast convergence techniques. Sound field reproduction systems that reproduce any desired signal at listener's ear generally use fixed preprocessing filters that are determined by the transfer functions from loudspeakers to control points in advance. However, control point movement results in severe localization errors. Our solution is a sound field reproduction system, based on the SP method, which uses only an error signal to update the filter coefficients. The SP method can track all control point movements but suffers from slow convergence. Hence, we also propose two methods that offer improved convergence speeds. One is a delay control method that compensates the delay caused by back-and-forth control point movements. The other is a compensation method that offsets the localization error caused by head rotation. Simulations demonstrate that the proposed methods can well track control point movements while offering reasonable convergence speeds.

In this paper, we describe a 6-bit 1.6-GS/s flash analog to digital converter (ADC). To reduce the power consumption and active area, we propose a new interpolation architecture using a symmetric three-input comparator. This ADC achieves 5.56 effective bits for input frequencies up to 220 MHz at 1.6 GS/s, and almost five effective bits for 660 MHz input at 1.6 GS/s. Peak INL and DNL are less than 0.5 LSB and 0.45 LSB, respectively. This ADC consumes 85 mW from 1.8 V at 1.6 GS/s and occupies an active area of 0.27 mm2. It is fabricated in 0.18-µm CMOS.

This study deals with two linear precoders: the maximization of the minimum Euclidean distance between received symbol-vectors, called here max-dmin, and the maximization of the post-processing signal-to-noise ratio termed max-SNR or beamforming. Both have been designed for reliable MIMO transmissions operating over uncorrelated Rayleigh fading channels. Here, we will explain why performances in terms of bit error rates show a significant enhancement of the max-dmin over the max-SNR whenever the number of antennas is increased. Then, from theoretical developments, we will demonstrate that, like the max-SNR precoder, the max-dmin precoder achieves the maximum diversity order, which is warrant of reliable transmissions. The current theoretical knowledge will be applied to the case-study of a system with two transmit- or two receive-antennas to calculate the probability density functions of two channel parameters directly linked to precoder performances for uncorrelated Rayleigh fading channels. At last, this calculation will allow us to quickly get the BER of the max-dmin precoder further to the derivation of a tight semi-theoretical approximation.

This letter presents new delayed perturbation bounds (DPBs) for stabilizing receding horizon H∞ control (RHHC). The linear matrix inequality (LMI) approach to determination of DPBs for the RHHC is proposed. We show through a numerical example that the RHHC can guarantee an H∞ norm bound for a larger class of systems with delayed perturbations than conventional infinite horizon H∞ control (IHHC).

We present a new framework for checking safety failures. The approach is based on the conservative inference of the internal states of a system by the observation of the interaction with its environment. It is based on two similar mechanisms : forward implication, which performs the analysis of the consequences of an input applied to the system, and backward implication, that performs the same task for an output transition. While being a very simple approach, it is general and we believe it can yield efficient algorithms in different safety-failure checking problems. As a case study, we have applied this framework to an existing problem, the hazard checking in (speed-independent) asynchronous circuits. Our new methodology yields an efficient algorithm that performs better or as well as all existing algorithms, while being more general than the fastest one.

We propose a stability-guaranteed width control (SGWC) for the hot strip finishing mill. It is shown that the proposed SGWC guarantees the stability of the width controller by the universal approximation of the neural network. It is shown through the field test in the hot strip mill of POSCO that the stability of the width controller is guaranteed by the proposed control scheme.

This paper presents a novel high-speed low-complexity pipelined degree-computationless modified Euclidean (pDCME) algorithm architecture for high-speed RS decoders. The pDCME algorithm allows elimination of the degree-computation so as to reduce hardware complexity and obtain high-speed processing. A high-speed RS decoder based on the pDCME algorithm has been designed and implemented with 0.13-µm CMOS standard cell technology in a supply voltage of 1.1 V. The proposed RS decoder operates at a clock frequency of 660 MHz and has a throughput of 5.3 Gb/s. The proposed architecture requires approximately 15% fewer gate counts and a simpler control logic than architectures based on the popular modified Euclidean algorithm.

Motion estimation (ME) is a computation-intensive module in video coding system. In MPEG-2 to H.264 transcoding, motion vector (MV) from MPEG-2 reused as search center in H.264 encoder is a simple but effective technique to simplify ME processing. However, directly applying MPEG-2 MV as search center will bring difficulties on application of data reuse method in hardware design, because the irregular overlapping of search windows between successive macro block (MB). In this paper, we propose a search window reuse scheme for transcoding, especially for HDTV application. By utilizing the similarity between neighboring MV, overlapping area of search windows can be regularized. Experiment results show that our method achieves average 93.1% search window reuse-rate in HDTV720p sequence with almost no video quality degradation. Compared to transcoding method without any data reuse scheme, bandwidth of the proposed method can be reduced to 40.6% of that.

This paper proposes a new algorithm named Adaptive Running Spectrum Filtering (ARSF) to restore the amplitude spectra of speech corrupted by additive noises. Based on the pre-hand noise estimation, adaptive filtering is used in speech modulation spectra according to the noise conditions. The periodic structures in the amplitude spectra are kept against noise distortion. Since the amplitude spectral structures contain the information of fundamental frequency, which is the inverse of pitch period, ARSF algorithm is added into robust pitch detection to increase the accuracy. Compared with the conventional methods, experimental results show that the proposed method significantly improves the robustness of pitch detection against noise conditions with several types and SNRs.

We report on suppressing adjacent-frequency interference (AFI) by using a RF receive bandpass-filter (BPF) with high-selectivity. By considering a high temperature superconducting (HTS) multi-pole BPF as a high selective BPF, the effect was estimated by numerical simulations. The simulations of the RF signals with an OFDM modulation transmitted to the demodulator via the BPF were carried out using the HTS BPF for 5 GHz band. The results confirmed the improvement of the bit error rate (BER) characteristic with the assumed HTS BPF with the high multi-poles under a strong AFI.

Fetal electrocardiogram (FECG) extraction is of vital importance in biomedical signal processing. A promising approach is blind source extraction (BSE) emerging from the neural network fields, which is generally implemented in a semi-blind way. In this paper, we propose a robust extraction algorithm that can extract the clear FECG as the first extracted signal. The algorithm exploits the fact that the FECG signal's kurtosis value lies in a specific range, while the kurtosis values of other unwanted signals do not belong to this range. Moreover, the algorithm is very robust to outliers and its robustness is theoretically analyzed and is confirmed by simulation. In addition, the algorithm can work well in some adverse situations when the kurtosis values of some source signals are very close to each other. The above reasons mean that the algorithm is an appealing method which obtains an accurate and reliable FECG.

We developed an Nb-based fabrication process for single flux quantum (SFQ) circuits in a Japanese government project that began in September 2002 and ended in March 2007. Our conventional process, called the Standard Process (SDP), was improved by overhauling all the process steps and routine process checks for all wafers. Wafer yield with the improved SDP dramatically increased from 50% to over 90%. We also developed a new fabrication process for SFQ circuits, called the Advanced Process (ADP). The specifications for ADP are nine planarized Nb layers, a minimum Josephson junction (JJ) size of 11 µm, a line width of 0.8 µm, a JJ critical current density of 10 kA/cm2, a 2.4 Ω Mo sheet resistance, and vertically stacked superconductive contact holes. We fabricated an eight-bit SFQ shift register, a one million SQUID array and a 16-kbit RAM by using the ADP. The shift register was operated up to 120 GHz and no short or open circuits were detected in the one million SQUID array. We confirmed correct memory operations by the 16-kbit RAM and a 5.7 times greater integration level compared to that possible with the SDP.

This paper proposes a technique for building an effective speech corpus with lower cost by utilizing a statistical multidimensional scaling method. The statistical multidimensional scaling method visualizes multiple HMM acoustic models into two-dimensional space. At first, a small number of voice samples per speaker is collected; speaker adapted acoustic models trained with collected utterances, are mapped into two-dimensional space by utilizing the statistical multidimensional scaling method. Next, speakers located in the periphery of the distribution, in a plotted map are selected; a speech corpus is built by collecting enough voice samples for the selected speakers. In an experiment for building an isolated-word speech corpus, the performance of an acoustic model trained with 200 selected speakers was equivalent to that of an acoustic model trained with 533 non-selected speakers. It means that a cost reduction of more than 62% was achieved. In an experiment for building a continuous word speech corpus, the performance of an acoustic model trained with 500 selected speakers was equivalent to that of an acoustic model trained with 1179 non-selected speakers. It means that a cost reduction of more than 57% was achieved.

In order to reduce the test cost, built-in self test (BIST) is widely used. One of the serious problems of BIST is that the compacted signature in BIST has very little information for fault diagnosis. Especially, it is difficult to determine which tests detect a fault. Therefore, it is important to develop an efficient fault diagnosis method by using incompletely identified pass/fail information. Where the incompletely identified pass/fail information means that a failing test block consists of at least one failing test and some passing tests, and all of the tests in passing test blocks are the passing test. In this paper, we propose a method to locate open faults by using incompletely identified pass/fail information. Experimental results for ISCAS'85 and ITC'99 benchmark circuits show that the number of candidate faults becomes less than 5 in many cases.

With the increasing complexity of LSI, Built-In Self Test (BIST) is a promising technique for production testing. We herein propose a method for diagnosing multiple stuck-at faults based on the compressed responses from BIST. We refer to fault diagnosis based on the ambiguous test pattern set obtained by the compressed responses of BIST as post-BIST fault diagnosis [1]. In the present paper, we propose an effective method by which to perform post-BIST fault diagnosis for multiple stuck-at faults. The efficiency of the success ratio and the feasibility of diagnosing large circuits are discussed.

This paper describes a robust automatic speech recognition (ASR) system with less computation. Acoustic models of a hidden Markov model (HMM)-based classifier include various types of hidden factors such as speaker-specific characteristics, coarticulation, and an acoustic environment, etc. If there exists a canonicalization process that can recover the degraded margin of acoustic likelihoods between correct phonemes and other ones caused by hidden factors, the robustness of ASR systems can be improved. In this paper, we introduce a canonicalization method that is composed of multiple distinctive phonetic feature (DPF) extractors corresponding to each hidden factor canonicalization, and a DPF selector which selects an optimum DPF vector as an input of the HMM-based classifier. The proposed method resolves gender factors and speaker variability, and eliminates noise factors by applying the canonicalzation based on the DPF extractors and two-stage Wiener filtering. In the experiment on AURORA-2J, the proposed method provides higher word accuracy under clean training and significant improvement of word accuracy in low signal-to-noise ratio (SNR) under multi-condition training compared to a standard ASR system with mel frequency ceptral coeffient (MFCC) parameters. Moreover, the proposed method requires a reduced, two-fifth, Gaussian mixture components and less memory to achieve accurate ASR.

We consider a data set in which each example is an n-dimensional Boolean vector labeled as true or false. A pattern is a co-occurrence of a particular value combination of a given subset of the variables. If a pattern appears frequently in the true examples and infrequently in the false examples, we consider it a good pattern. In this paper, we discuss the problem of determining the data size needed for removing "deceptive" good patterns; in a data set of a small size, many good patterns may appear superficially, simply by chance, independently of the underlying structure. Our hypothesis is that, in order to remove such deceptive good patterns, the data set should contain a greater number of examples than that at which a random data set contains few good patterns. We justify this hypothesis by computational studies. We also derive a theoretical upper bound on the needed data size in view of our hypothesis.

Superconductivity and superconducting electronics have quite a prominent place in the European research environment and can look back onto a successful history. In recent years the European Framework programs helped to enhance the interaction between the different national research institutions, universities and industry. For applications of superconductivity this was accomplished by the European Network of Excellence SCENET and its sister organization ESAS. In this context a virtual European foundry network was established (Fluxonics), which forms a platform for the superconducting electronics activities in Europe and realizes support for the design and the fabrication of superconducting circuits for research laboratories and industry. Lately quite some development on the digital side and the cooling of superconducting electronics devices has taken place in Europe; most of it within the Fluxonics network. Some of these advances will be reported in this overview article.

The extraction of acoustic features for robust speech recognition is very important for improving its performance in realistic environments. The bi-spectrum based on the Fourier transformation of the third-order cumulants expresses the non-Gaussianity and the phase information of the speech signal, showing the dependency between frequency components. In this letter, we propose a method of extracting short-time bi-spectral acoustic features with averaging features in a single frame. Merged with the conventional Mel frequency cepstral coefficients (MFCC) based on the power spectrum by the principal component analysis (PCA), the proposed features gave a 6.9% relative lower a word error rate in Japanese broadcast news transcription experiments.