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.

We present a TCP flow level performance evaluation on error rate aware scheduling algorithms in Evolved UTRA and UTRAN networks. With the introduction of the error rate, which is the probability of transmission failure under a given wireless condition and the instantaneous transmission rate, the transmission efficiency can be improved without sacrificing the balance between system performance and user fairness. The performance comparison with and without error rate awareness is carried out dependant on various TCP traffic models, user channel conditions, schedulers with different fairness constraints, and automatic repeat request (ARQ) types. The results indicate that error rate awareness can make the resource allocation more reasonable and effectively improve the system and individual performance, especially for poor channel condition users.

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.

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.

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 conventional AOA estimation schemes assume multiple antennas, which usually causes high-cost estimation systems. Moreover, the schemes are very vulnerable to multi-path interferences. In this letter, a novel scheme is proposed for the efficient AOA estimation. The scheme is based on cyclic pilot symbols, which guarantees the use of a single antenna and the robustness over multi-path interferences.

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.

The optimal alignment of two given biosequences is mathematically optimal, but it may not be a biologically optimal one. To investigate more possible alignments with biological meaning, one can relax the scoring functions to get near-optimal alignments. Though the near optimal alignments increase the possibility of finding the correct alignment, they may confuse the biologists because the size of candidates is large. In this paper, we present the filter scheme for the near-optimal alignments. An easy method for tracing the near-optimal alignments and an algorithm for filtering those alignments are proposed. The time complexity of our algorithm is O(dmn) in the worst case, where d is the maximum distance between the near-optimal alignments and the optimal alignment, and m and n are the lengths of the input sequences, respectively.

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.

Orbit estimation of space debris, objects of no inherent value orbiting the earth, is a task that is important for avoiding collisions with spacecraft. The Kamisaibara Spaceguard Center radar system was built in 2004 as the first radar facility in Japan devoted to the observation of space debris. In order to detect the smaller debris, coherent integration is effective in improving SNR (Signal-to-Noise Ratio). However, it is difficult to apply coherent integration to real data because the motions of the targets are unknown. An effective algorithm is proposed for echo detection and orbit estimation of the faint echoes from space debris. The characteristics of the evaluation function are utilized by the algorithm. Experiments show the proposed algorithm improves SNR by 8.32 dB and enables estimation of orbital parameters accurately to allow for re-tracking with a single radar.

In this paper, the development, long-term operation and portability of a practical ASR application in a real environment is investigated. The target application is a speech-oriented guidance system installed at the local community center. The system has been exposed to ordinary people since November 2002. More than 300 hours or more than 700,000 inputs have been collected during four years. The outcome is a rare example of a large scale real-environment speech database. A simulation experiment is carried out with this database to investigate how the system's performance improves during the first two years of operation. The purpose is to determine empirically the amount of real-environment data which has to be prepared to build a system with reasonable speech recognition performance and response accuracy. Furthermore, the relative importance of developing the main system components, i.e. speech recognizer and the response generation module, is assessed. Although depending on the system's modeling capacities and domain complexity, experimental results show that overall performance stagnates after employing about 10-15 k utterances for training the acoustic model, 40-50 k utterances for training the language model and 40 k-50 k utterances for compiling the question and answer database. The Q&A database was most important for improving the system's response accuracy. Finally, the portability of the well-trained first system prototype for a different environment, a local subway station, is investigated. Since collection and preparation of large amounts of real data is impractical in general, only one month of data from the new environment is employed for system adaptation. While the speech recognition component of the first prototype has a high degree of portability, the response accuracy is lower than in the first environment. The main reason is a domain difference between the two systems, since they are installed in different environments. This implicates that it is imperative to take the behavior of users under real conditions into account to build a system with high user satisfaction.

Chinese is a representative tonal language, and it has been an attractive topic of how to process tone information in the state-of-the-art large vocabulary speech recognition system. This paper presents a novel way to derive an efficient phoneme set of tone-dependent units to build a recognition system, by iteratively merging a pair of tone-dependent units according to the principle of minimal loss of the Mutual Information (MI). The mutual information is measured between the word tokens and their phoneme transcriptions in a training text corpus, based on the system lexical and language model. The approach has a capability to keep discriminative tonal (and phoneme) contrasts that are most helpful for disambiguating homophone words due to lack of tones, and merge those tonal (and phoneme) contrasts that are not important for word disambiguation for the recognition task. This enables a flexible selection of phoneme set according to a balance between the MI information amount and the number of phonemes. We applied the method to traditional phoneme set of Initial/Finals, and derived several phoneme sets with different number of units. Speech recognition experiments using the derived sets showed its effectiveness.

Hwang-Lo-Lin proposed a user identification scheme [3] based on the Maurer-Yacobi scheme [6] that is suitable for application to the mobile environment. Hwang-Lo-Lin argued that their scheme is secure against any attack. Against the Hwang-Lo-Lin argument, Liu-Horng-Liu showed that the Hwang-Lo-Lin scheme is insecure against a Liu-Horng-Liu attack mounted by an eavesdrop attacker. However, Liu-Horng-Liu did not propose any improved version of the original identification scheme which is still secure against the Liu-Horng-Liu attack. In this paper, we propose an identification scheme that can solve this problem and a non-interactive public key distribution scheme also.

In MC-CDMA systems, subcarriers can be shared by different users. In this letter, we exploit the shared nature of subcarriers and propose a user grouping and subcarrier allocation algorithm for grouped MC-CDMA systems. The scheme aims at maximizing the total system throughput while providing bandwidth-fairness among groups. Simulation results are given to demonstrate the performance of the proposed algorithm in terms of sum capacity and per-user throughput.

This paper includes different approaches for analysis of a thin-wire antenna in the presence of de-ionized water box at different temperatures as a high-permittivity three-dimensional dielectric body. In continuation with the previous work of authors, first, the coupled tensor-volume/line integral equations is solved by using Galerkin-based moment method (MoM) consisting of a combination of entire-domain and sub-domain basis functions including three-dimensional polynomials with different degrees. Then, the accuracy of such MoM, specifically for a high-permittivity dielectric scatterer, is substantiated by comparing its numerical results with that of FDTD method and some experimental data.

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.

We investigate the MIMO broadcast channels with imperfect channel knowledge due to estimation error and much more users than transmit antennas to exploit multiuser diversity. The channel estimation error causes the interference among users, resulting in the sum-rate loss. A tight upper bound of this sum-rate loss based on zeroforcing beamforming is derived theoretically. This bound only depends on the channel estimation quality and transmit antenna number, but not on the user number. Based on this upper bound, we show this system maintains full multiuser diversity, and always benefits from the increasing transmit power.

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.

Analytical expressions based on the Gauss-Chebyshev quadrature (GCQ) rule technique are derived to evaluate the bit-error rate (BER) for the time-hopping pulse position modulation (TH-PPM) ultra-wide band (UWB) systems under a Nakagami-m fading channel. The analyses are validated by the simulation results and adopted to assess the accuracy of the commonly used Gaussian approximation (GA) method. The influence of the fading severity on the BER performance of TH-PPM UWB system is investigated.

We present a novel method of integrating the likelihoods of multiple feature streams, representing different acoustic aspects, for robust speech recognition. The integration algorithm dynamically calculates a frame-wise stream weight so that a higher weight is given to a stream that is robust to a variety of noisy environments or speaking styles. Such a robust stream is expected to show discriminative ability. A conventional method proposed for the recognition of spoken digits calculates the weights from the entropy of the whole set of HMM states. This paper extends the dynamic weighting to a real-time large-vocabulary continuous speech recognition (LVCSR) system. The proposed weight is calculated in real-time from mutual information between an input stream and active HMM states in a search space without an additional likelihood calculation. Furthermore, the mutual information takes the width of the search space into account by calculating the marginal entropy from the number of active states. In this paper, we integrate three features that are extracted through auditory filters by taking into account the human auditory system's ability to extract amplitude and frequency modulations. Due to this, features representing energy, amplitude drift, and resonant frequency drifts, are integrated. These features are expected to provide complementary clues for speech recognition. Speech recognition experiments on field reports and spontaneous commentary from Japanese broadcast news showed that the proposed method reduced error words by 9.2% in field reports and 4.7% in spontaneous commentaries relative to the best result obtained from a single stream.