Capture-safety, (defined as the avoidance of timing error due to unduly high launch switching activity in capture mode during at-speed scan testing), is critical in avoiding test induced yield loss. Although several sophisticated techniques are available for reducing capture IR-drop, there are few complete capture-safe test generation flows. This paper addresses the problem by proposing a novel and practical capture-safe test generation flow, featuring (1) a complete capture-safe test generation flow; (2) reliable capture-safety checking; and (3) effective capture-safety improvement by combining X-bit identification & X-filling with low launch-switching-activity test generation. The proposed flow minimizes test data inflation and is compatible with existing automatic test pattern generation (ATPG) flow. The techniques proposed in the flow achieve capture-safety without changing the circuit-under-test or the clocking scheme.

We investigated the effect of a high-speed power line communication (PLC) signal induced into a very high-speed digital subscriber line (VDSL) system by conductive coupling based on a network model. Four electronic devices with AC mains and telecommunication ports were modeled using a 4-port network, and the parameters of the network were obtained from measuring impedance and transmission loss. We evaluated the decoupling factor from the mains port to the telecommunication port of a VDSL modem using these parameters for the four electric and electronic devices. The results indicate that the mean value of the decoupling factor for the differential and common mode signals were more than 88 and 62 dB, respectively, in the frequency range of a PLC system. Taking the following parameters into consideration; decoupling factor Ld, the average transmission signal powers of VDSL and PLC, desired and undesired (DU) ratio, and transmission loss of a typical 300-m-long indoor telecommunication line, the VDSL system cannot be disturbed by the PLC signal induced into the VDSL modem from the AC mains port in normal installation.

In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker's voice into an arbitrary target speaker's voice. An eigenvoice Gaussian mixture model (EV-GMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored target speakers. The EV-GMM can be adapted to new target speakers using only a few of their arbitrary utterances by estimating a small number of adaptive parameters. In the adaptation process, several parameters of the EV-GMM to be fixed for different target speakers strongly affect the conversion performance of the adapted model. In order to improve the conversion performance in one-to-many EVC, we propose an adaptive training method of the EV-GMM. In the proposed training method, both the fixed parameters and the adaptive parameters are optimized by maximizing a total likelihood function of the EV-GMMs adapted to individual pre-stored target speakers. We conducted objective and subjective evaluations to demonstrate the effectiveness of the proposed training method. The experimental results show that the proposed adaptive training yields significant quality improvements in the converted speech.

This paper describes non-reference objective quality evaluation for noise-reduced speech. First, a subjective test is conducted in accordance with ITU-T Rec. P.835 to obtain the speech quality, the noise quality, and the overall quality of noise-reduced speech. Based on the results, we then propose an overall quality estimation model. The unique point of the proposed model is that the estimation of the overall quality is done only using the previously estimated speech quality and noise quality, in contrast to conventional models, which utilize the acoustical features extracted. Finally, we propose a non-reference objective quality evaluation method using the proposed model. The results of an experiment with different noise reduction algorithms and noise types confirmed that the proposed method gives more accurate estimates of the overall quality compared with the method described in ITU-T Rec. P.563.

In this paper, we propose a novel method based on the second-order conditional maximum a posteriori (CMAP) to improve the performance of the global soft decision in speech enhancement. The conventional global soft decision scheme is found through investigation to have a disadvantage in that the global speech absence probability (GSAP) in that scheme is adjusted by a fixed parameter, which could be a restrictive assumption in the consecutive occurrences of speech frames. To address this problem, we devise a method to incorporate the second-order CMAP in determining the GSAP, which is clearly different from the previous approach in that not only current observation but also the speech activity decisions of the previous two frames are exploited. Performances of the proposed method are evaluated by a number of tests in various environments and show better results than previous work.

In this paper the SNR estimation is performed frame by frame, during the speech activity. For this purpose, the fourth-order moments of the real and imaginary parts of frequency components are extracted, for both the speech and noise, separately. For each noisy frame, the mentioned fourth-order moments are also estimated. Making use of the proposed formulas, the signal-to-noise ratio is estimated in each frequency index of the noisy frame. These formulas also predict the overall signal-to-noise ratio in each noisy frame. What makes our method outstanding compared to conventional approaches is that this method takes into consideration both the speech and noise identically. It estimates the negative SNR almost as well as the positive SNR.

Free probability theory, which has become a main branch of random matrix theory, is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large matrices. In this paper, using asymptotic free probability theory, a new cooperative scheme for spectrum sensing is proposed, which shows how the asymptotic free behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for cognitive radio. Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance than the energy detection techniques and the Maximum-minimum eigenvalue (MME) scheme even for the case of a small sample of observations.

A new estimation algorithm of clock drift in symbol duration for high precision ranging, based on multiple symbols of chirp spread spectrum (CSS) is proposed. Since the permissible error of a crystal oscillator in CSS is relatively high given the need to lower device costs, ranging results are perturbed by clock drift. We establish the phenomenon of clock drift in multiple symbols of CSS, and estimate the clock drift in symbol duration based on phase difference between adjacent symbols. The proposed algorithm is analyzed, and verified by Monte Carlo simulations.

This paper proposes a pattern partitioning algorithm that maps multiple target patterns onto homogeneous memory-based string matchers. The proposed algorithm adopts the greedy search based on lexicographical sorting. By mapping as many target patterns as possible onto each string matcher, the memory requirements are greatly reduced.

This paper presents a CMOS signal detection circuit for 2.5 Gb/s serial data communication system over FR-4 backplane. This overcomes characteristics deviation of full-wave rectifier-based simple power detection circuits due to data pattern and temperature by using an edge detector and a sample-hold circuit.

Traditionally, popular synonym acquisition methods are based on the distributional hypothesis, and a metric such as Jaccard coefficients is used to evaluate the similarity between the contexts of words to obtain synonyms for a query. On the other hand, when one tries to compile and clean a thesaurus, one often already has a modest number of synonym relations at hand. Could something be done with a half-built thesaurus alone? We propose the use of spectral methods and discuss their relation to other network-based algorithms in natural language processing (NLP), such as PageRank and Bootstrapping. Since compiling a thesaurus is very laborious, we believe that adding the proposed method to the toolkit of thesaurus constructors would significantly ease the pain in accomplishing this task.

LCD Automatic Vision Inspection (AVI) systems automatically detect defect features and measure their sizes via camera vision. AVI systems usually report different measurements on the same defect with some variations on position or rotation mainly because we get different images. This is caused by possible variations in the image acquisition process including optical factors, non-uniform illumination, random noise, and so on. For this reason, conventional area based defect measuring method has some problems in terms of robustness and consistency. In this paper, we propose a new defect size measuring method to overcome these problems. We utilize volume information which is completely ignored in the area based conventional defect measuring method. We choose a bell shape as a defect model for experiment. The results show that our proposed method dramatically improves robustness of defect size measurement. Given proper modeling, the proposed volume based measuring method can be applied to various types of defect for better robustness and consistency.

Bugs are becoming unavoidable in complex integrated circuit design. It is imperative to identify the bugs as soon as possible through post-silicon debug. For post-silicon debug, observability is one of the biggest challenges. Scan-based debug mechanism provides high observability by reusing scan chains. However, it is not feasible to scan dump cycle-by-cycle during program execution due to the excessive time required. In fact, it is not necessary to scan out the error-free states. In this paper, we introduce Suspect Window to cover the clock cycle in which the bug is triggered. Then, we present an efficient approach to determine the suspect window. Based on Suspect Window, we propose a novel debug mechanism to locate the bug both temporally and spatially. Since scan dumps are only taken in the suspect window with the proposed mechanism, the time required for locating the bug is greatly reduced. The approaches are evaluated using ISCAS'89 and ITC'99 benchmark circuits. The experimental results show that the proposed mechanism can significantly reduce the overall debug time compared to scan-based debug mechanism while keeping high observability.

Differing from the long-term prediction used in the modern speech codec, the standard of the internet low bit rate codec (iLBC) independently encodes the residual of the linear predictive coding (LPC) frame by frame. In this paper, a complexity scalability design is proposed for the coding of the dynamic codebook search in the iLBC speech codec. In addition, a trade-off between the computational complexity and the speech quality can be achieved by dynamically setting the parameter of the proposed approach. Simulation results show that the computational complexity can be effectively reduced with imperceptible degradation of the speech quality.

Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.

Multipath routing and the ability to simultaneously use multiple network paths has long been proposed as a means for meeting the reliability and performance improvement goals of a next generation Internet. However, its use causes out-of-order packet delivery, which is well known to hinder TCP performance. While next-generation transport protocols will no doubt better cope with this phenomenon, a complete switch to these new protocols cannot be made on all devices "overnight"; the reality is that we will be forced to continue using TCP on such multipath networks well after deployment of a future Internet is complete. In this paper, we investigate the use of best-effort packet reordering -- an optional network layer service for improving the performance of any TCP session in the presence of out-of-order packet delivery. Such a service holds the promise of allowing unmodified TCP to take advantage of the reliability and performance gains offered by a future multipath-enabled Internet without suffering the adverse performance effects commonly associated with out-of-order packet delivery. Our experiments test the performance of two common TCP variants under packet dispersion with differing numbers of paths and amounts of inter-path latency variance. They were conducted using multipath network and packet reorderer implementations implemented within the Emulab testbed. Our results demonstrate that a simple best-effort reordering service can insulate TCP from the type of reordering that might be expected from use of packet dispersion over disjoint paths in a wide-area network, and is capable of providing significant performance benefits with few ill side-effects.

We experimentally studied the polarization mode dispersion (PMD) tolerance of an feed-forward equalizer (FFE) electronic dispersion compensation (EDC) IC in the absence of adaptive control, in 43-Gbit/s RZ-DQPSK transmission. Using a 3-tap FFE IC composed of InP HBTs, differential group delay (DGD) tolerance at a 2-dB Q penalty is shown to be extended from 25 ps to up to 29 ps. When a polarization scrambler is used, the tolerance is further extended to 31 ps. This value is close to the tolerance obtained with adaptive control, without a polarization scrambler.

With the emergence of bandwidth-greedy application services, high-speed transport protocols are expected to effectively and aggressively use large amounts of bandwidth in current broadband and multimedia networks. However, when high-speed transport protocols compete with other standard TCP flows, they can occupy most of the available bandwidth leading to disruption of service. To deploy high-speed transport protocols on the Internet, such unfair situations must be improved. In this paper, therefore, we propose a method to improve fairness, called Kyushu-TCP (KTCP), which introduces a non-aggressive period in the congestion avoidance phase to give other standard TCP flows more chances of increasing their transmission rates. This method improves fairness in terms of the throughput by estimating the stably available bandwidth-delay product and adjusting its transmission rate based on this estimation. We show the effectiveness of the proposed method through simulations.

To improve speech recognition performance, feature transformation based on discriminant analysis has been widely used to reduce the redundant dimensions of acoustic features. Linear discriminant analysis (LDA) and heteroscedastic discriminant analysis (HDA) are often used for this purpose, and a generalization method for LDA and HDA, called power LDA (PLDA), has been proposed. However, these methods may result in an unexpected dimensionality reduction for multimodal data. It is important to preserve the local structure of the data when reducing the dimensionality of multimodal data. In this paper we introduce two methods, locality-preserving HDA and locality-preserving PLDA, to reduce dimensionality of multimodal data appropriately. We also propose an approximate calculation scheme to calculate sub-optimal projections rapidly. Experimental results show that the locality-preserving methods yield better performance than the traditional ones in speech recognition.

The multilevel dual-channel (MLDC) not-AND (NAND) flash memories cell structures with asymmetrically-doped channel regions between the source and the drain were proposed to enhance read and program verifying speeds. The channel structure of the MLDC flash memories consisted of two different doping channel regions. The technical computer aided design simulation results showed that the designed MLDC NAND flash cell with asymmetrically-doped channel regions provided the high-speed multilevel reading with a wider current sensing margin and the high-speed program verifying due to the sensing of the discrete current levels. The proposed unique MLDC NAND flash memory device can be used to increase read and program verifying speed.