We developed test data compression scheme for scan-based BIST, aiming to compress test stimuli and responses by more than 100 times. As scan-BIST architecture, we adopt BIST-Aided Scan Test (BAST), and combines four techniques: the invert-and-shift operation, run-length compression, scan address partitioning, and LFSR pre-shifting. Our scheme achieved a 100x compression rate in environments where Xs do not occur without reducing the fault coverage of the original ATPG vectors. Furthermore, we enhanced the masking logic to reduce data for X-masking so that test data is still compressed to 1/100 in a practical environment where Xs occur. We applied our scheme to five real VLSI chips, and the technique compressed the test data by 100x for scan-based BIST.

In this paper, a fast mode decision method for intra-prediction is proposed to reduce the computational complexity of H.264/AVC encoders. With edge information, we propose a novel fast estimation algorithm to reduce the computation overhead of H.264/AVC for mode selection, where the edge direction of each coding block is detected from only part of the transformed coefficients. Hence, the computation complexity is greatly reduced. Experimental results show that the proposed fast mode decision method can eliminate about 81.34% encoding time for all intra-frame sequences with acceptable degradation of averaged PSNR and bitrates.

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.

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.

This paper proposes a robust omnidirectional audio-visual (AV) talker localizer for AV applications. The proposed localizer consists of two innovations. One of them is robust omnidirectional audio and visual features. The direction of arrival (DOA) estimation using an equilateral triangular microphone array, and human position estimation using an omnidirectional video camera extract the AV features. The other is a dynamic fusion of the AV features. The validity criterion, called the audio- or visual-localization counter, validates each audio- or visual-feature. The reliability criterion, called the speech arriving evaluator, acts as a dynamic weight to eliminate any prior statistical properties from its fusion procedure. The proposed localizer can compatibly achieve talker localization in a speech activity and user localization in a non-speech activity under the identical fusion rule. Talker localization experiments were conducted in an actual room to evaluate the effectiveness of the proposed localizer. The results confirmed that the talker localization performance of the proposed AV localizer using the validity and reliability criteria is superior to that of conventional localizers.

This paper presents a quadrature injection locked frequency divider (ILFD) employing tunable active inductors (TAIs), which are used is to extend the locking range and to reduce die area. The CMOS ILFD is based on a new quadrature voltage-controlled oscillator (VCO) with cross-coupled switching pairs and TAI-C tanks, and was fabricated in the 0.18-µm 1P6M CMOS technology. The divide-by-2 LC-tank ILFD is performed by adding injection MOSFETs between the differential outputs of the VCO. Measurement results show that at the supply voltage of 1.8 V, the divider free-running frequency is tunable from 1.34 GHz to 3.07 GHz, and at the incident power of 0 dBm the locking range is about 6 GHz (137%), from the incident frequency 1.37 GHz to 7.38 GHz. The core power consumption is 22.8 mW. The die area is 0.630.55 mm2.

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 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.

We developed a 44 SFQ network switch prototype system and demonstrated its operation at 10 Gbps. The system's core is composed of two SFQ chips: a 44 switch and a 6-channel voltage driver. The 44 switch chip contained both a switch fabric (i.e. a data path) and a switch scheduler (i.e. a controller). Both chips were attached to a multi-chip-module (MCM) carrier, which was then installed in a cryocooled system with 32 10-Gbps ports. Each chip contained about 2100 Josephson junctions on a 5-mm5-mm die. An NEC standard 2.5-kA/cm2 fabrication process was used for the switch chip. We increased the critical current density to 10 kA/cm2 for the driver chip to improve speed while maintaining wide bias margins. MCM implementation enabled us to use a hybrid critical current density technology. Voltage pulses were transferred between two chips through passive transmission lines on the MCM carrier. The cryocooled system was cooled down to about 4 K using a two-stage 1-W cryocooler. We correctly operated the whole system at 10 Gbps. The switch scheduler, which is driven by an on-chip clock generator, operated at 40 GHz. The speed gap between SFQ and room temperature devices was filled by on-chip SFQ FIFO buffers or shift registers. We measured the bit error rate at 10 Gbps and found that it was on the order of 10-13 for the 44 SFQ switch fabric. In addition, using semiconductor interface circuitry, we built a four-port SFQ Ethernet switch. All the components except for a compressor were installed in a standard 19-inch rack, filling a space 21 U (933.5 mm or 36.75 inches) in height. After four personal computers (PCs) were connected to the switch, we have successfully transferred video data between them.

Users require speech recognition systems that offer rapid response and high accuracy concurrently. Speech recognition accuracy is degraded by additive noise, imposed by ambient noise, and convolutional noise, created by space transfer characteristics, especially in distant talking situations. Against each type of noise, existing model adaptation techniques achieve robustness by using HMM-composition and CMN (cepstral mean normalization). Since they need an additive noise sample as well as a user speech sample to generate the models required, they can not achieve rapid response, though it may be possible to catch just the additive noise in a previous step. In the previous step, the technique proposed herein uses just the additive noise to generate an adapted and normalized model against both types of noise. When the user's speech sample is captured, only online-CMN need be performed to start the recognition processing, so the technique offers rapid response. In addition, to cover the unpredictable S/N values possible in real applications, the technique creates several S/N HMMs. Simulations using artificial speech data show that the proposed technique increased the character correct rate by 11.62% compared to CMN.

This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

Functional Electrical Stimulation (FES) can be effective in assisting or restoring paralyzed motor functions. The purpose of this study is to examine experimentally the fuzzy controller based on cycle-to-cycle control for FES-induced gait. A basic experimental test was performed on controlling maximum knee extension angle with normal subjects. In most of control trials, the joint angle was controlled well compensating changes in muscle responses to electrical stimulation. The results show that the fuzzy controller would be practical in clinical applications of gait control by FES. An automatic parameter tuning would be required practically for quick responses in reaching the target and in compensating the change in muscle responses without causing oscillating responses.

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 this paper, we propose an image enlargement method by using morphological operators. Our enlargement method is based on the nonlinear frequency extrapolation method (Greenspan et al., 2000) by using a Laplacian pyramid image representation. In this method, the sampling process of input images is modeled as the Laplacian pyramid. A high resolution image is obtained with the finer scale Laplacian that is extrapolated by a nonlinear operation from a low resolution Laplacian. In this paper, we propose a novel nonlinear operation for extrapolation of the finer scale Laplacian. Our nonlinear operation is realized by morphological operators and is capable of generating the finer scale Laplacian, the amplitude of which is proportional to contrasts of edges that appear in the low resolution image. In experiments, the enlargement results given by the proposed method are demonstrated. Compared with the Greenspan's method, the proposed method can recover sharp intensity transients of image edges with small artifacts.

The accuracy of automatic speech recognition in a car is significantly degraded in a very low SNR (Signal to Noise Ratio) situation such as "Fan high" or "Window open". In such cases, speech signals are often buried in broadband noise. Although several existing noise reduction algorithms are known to improve the accuracy, other approaches that can work with them are still required for further improvement. One of the candidates is enhancement of the harmonic structures in human voices. However, most conventional approaches are based on comb filtering, and it is difficult to use them in practical situations, because their assumptions for F0 detection and for voiced/unvoiced detection are not accurate enough in realistic noisy environments. In this paper, we propose a new approach that does not rely on such detection. An observed power spectrum is directly converted into a filter for speech enhancement, by retaining only the local peaks considered to be harmonic structures in the human voice. In our experiments, this approach reduced the word error rate by 17% in realistic automobile environments. Also, it showed further improvement when used with existing noise reduction methods.

A sound localization method in the proximal region is proposed, which is based on a low-cost 3D sound localization algorithm with the use of head-related transfer functions (HRTFs). The auditory parallax model is applied to the current algorithm so that more accurate HRTFs can be used for sound localization in the proximal region. In addition, head-shadowing effects based on rigid-sphere model are reproduced in the proximal region by means of a second-order IIR filter. A subjective listening test demonstrates the effectiveness of the proposed method. Embedded system implementation of the proposed method is also described claiming that the proposed method improves sound effects in the proximal region only with 5.1% increase of memory capacity and 8.3% of computational costs.

To achieve high recognition performance for a wide variety of noise and for a wide range of signal-to-noise ratio, this paper presents methods for integration of four noise reduction algorithms: spectral subtraction with smoothing of time direction, temporal domain SVD-based speech enhancement, GMM-based speech estimation and KLT-based comb-filtering. In this paper, we proposed two types of combination methods of noise suppression algorithms: selection of front-end processor and combination of results from multiple recognition processes. Recognition results on the CENSREC-1 task showed the effectiveness of our proposed methods.

Convolutional compactors offer a promising technique of compacting test responses. In this study we expand the architecture of convolutional compactor onto a Galois field in order to improve compaction ratio as well as reduce X-masking probability, namely, the probability that an error is masked by unknown values. While each scan chain is independently connected by EOR gates in the conventional arrangement, the proposed scheme treats q signals as an element over GF(2q), and the connections are configured on the same field. We show the arrangement of the proposed compactors and the equivalent expression over GF(2). We then evaluate the effectiveness of the proposed expansion in terms of X-masking probability by simulations with uniform distribution of X-values, as well as reduction of hardware overheads. Furthermore, we evaluate a multi-weight arrangement of the proposed compactors for non-uniform X distributions.

By utilizing the second and fourth order linear prediction errors, a novel estimator for a single noisy sinusoid is devised. The frequency estimate is obtained from a solving a cubic equation and a simple root selection procedure is provided. Asymptotical variance of the estimated frequency is derived and confirmed by computer simulations. It is demonstrated that the proposed estimator is superior to the reformed Pisarenko harmonic decomposer, which is the improved version of Pisarenko harmonic decomposer.

If a test set for more complex faults than stuck-at faults is generated, higher defect coverage would be obtained. Such a test set, however, would have a large number of test vectors, and hence the test costs would go up. In this paper we propose a method to detect bridge defects with a test set initially generated for stuck-at faults in a full scan sequential circuit. The proposed method doesn't add new test vectors to the test set but modifies test vectors. Therefore there are no negative impacts on test data volume and test application time. The initial fault coverage for stuck-at faults of the test set is guaranteed with modified test vectors. In this paper we focus on detecting as many as possible non-feedback AND-type, OR-type and 4-way bridging faults, respectively. Experimental results show that the proposed method increases the defect coverage.