We developed flexible LC devices using coat-debond polyimide substrates with a low birefringence and etched post spacers, and clarified that flexible LCDs using post spacers with small spacer distance have a high flexibility without degradation of the image quality. This result ensured the feasibility of flexible LCDs using coat-debond method.

Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.

In PQCrypto 2013, Yasuda, Takagi and Sakurai proposed a new signature scheme as one of multivariate public key cryptosystems (MPKCs). This scheme (called YTS) is based on the fact that there are two isometry classes of non-degenerate quadratic forms on a vector space with a prescribed dimension. The advantage of YTS is its efficiency. In fact, its signature generation is eight or nine times faster than Rainbow of similar size. For the security, it is known that the direct attack, the IP attack and the min-rank attack are applicable on YTS, and the running times are exponential time for the first and the second attacks and sub-exponential time for the third attack. In the present paper, we give a new attack on YTS whose approach is to use the diagonalization of matrices. Our attack works in polynomial time and it actually recovers equivalent secret keys of YTS having 140-bits security against min-rank attack in around fifteen seconds.

This letter proposes a novel post-processing method for self-similarity based super-resolution (SR). Existing back-projection (BP) methods enhance SR images by refining the reconstructed coarse high-frequency (HF) information. However, it causes artifacts due to interpolation and excessively smoothes small HF signals, particularly in texture regions. Motivated by these observations, we propose a novel post-processing method referred to as middle-frequency (MF) based refinement. The proposed method refines the reconstructed HF information in the MF domain rather than in the spatial domain, as in BP. In addition, it does not require an internal interpolation process, so it is free from the side-effects of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both the quantity of reproduced HF information and the visual quality.

A broadband mode transition between grounded coplanar waveguide (GCPW) and post-wall waveguide (PWW) is proposed. The transition is composed of GCPW, microstrip line (MSL) and PWW, where the GCPW and PWW are connected via the MSL. The transition is fabricated on liquid crystal polymer (LCP) substrate because of its low dielectric loss and cost effectiveness based on a roll-to-roll fabrication process. Center strip of the GCPW is sandwiched by two ground pads in each of which two through-holes and a rectangular slit are structured. Broadband impedance matching is achieved by this structure thanks to an addition of lumped inductance and capacitance to the transition. A part of the MSL is tapered for the broadband operation. A 25% impedance bandwidth for |S11| less than -15dB is achieved in measurement of a fabricated transition. Loss of the GCPW ground-signal-ground (GSG) pad of 0.12dB and that of the MSL-PWW transition of 0.29dB at 60GHz are evaluated from the measurement. Fabrication error and the caused tolerance on performance are also evaluated and small variation in production is confirmed. The mode transition can be used for low loss antenna-in-package in millimeter-wave applications.

Postcopy live migration is a promising alternative of virtual machine (VM) migration, which transfers memory pages after switching the execution host of a VM. It allows a shorter and more deterministic migration time than precopy migration. There is, however, a possibility that postcopy migration would degrade VM performance just after switching the execution host. In this paper, we propose a performance improvement technique of postcopy migration, extending the para-virtualized page fault mechanism of a virtual machine monitor. When the guest operating system accesses a not-yet-transferred memory page, our proposed mechanism allows the guest kernel to defer the execution of the current process until the page data is transferred. In parallel with the page transfer, the guest kernel can yield VCPU to other active processes. We implemented the proposed technique in our postcopy migration mechanism for Qemu/KVM. Through experiments, we confirmed that our technique successfully alleviated performance degradation of postcopy migration for web server and database benchmarks.

Appropriate turn-taking is important in spoken dialogue systems as well as generating correct responses. Especially if the dialogue features quick responses, a user utterance is often incorrectly segmented due to short pauses within it by voice activity detection (VAD). Incorrectly segmented utterances cause problems both in the automatic speech recognition (ASR) results and turn-taking: i.e., an incorrect VAD result leads to ASR errors and causes the system to start responding though the user is still speaking. We develop a method that performs a posteriori restoration for incorrectly segmented utterances and implement it as a plug-in for the MMDAgent open-source software. A crucial part of the method is to classify whether the restoration is required or not. We cast it as a binary classification problem of detecting originally single utterances from pairs of utterance fragments. Various features are used representing timing, prosody, and ASR result information. Experiments show that the proposed method outperformed a baseline with manually-selected features by 4.8% and 3.9% in cross-domain evaluations with two domains. More detailed analysis revealed that the dominant and domain-independent features were utterance intervals and results from the Gaussian mixture model (GMM).

Ultra high definition (UHD) imaging systems have attracted much attention as a next generation television (TV) broadcasting service and video streaming service. However, the state of the art video coding standards including H.265/HEVC has not enough compression rate for streaming, broadcasting and storing UHD. Existing coding standard such as H.265/HEVC normaly use RGB-YCbCr color transform before compressing RGB color image since that procedure can decorrelate color components well. However, there is room for improvement on the coding efficiency for color image based on an observation that the luminance and chrominance components changes in same locations. This observation inspired us to propose a new post-processing method for compressed images by using weighted least square (WLS) filter with coded luminance component as a guide image, for refining the edges of chrominance components. Since the computational cost of WLS tends to superlinearly increase with increasing image size, it is difficult to apply it to UHD images. To overcome this problem, we propose slightly overlapped block partitioning and a new variant of WLS (constrained WLS, CWLS). Experimental results of objective quality comparison and subjective assessment test using 4K images show that our proposed method can outperform the conventional method and reduce the bit amount for chrominance component drastically with preserving the subjective quality.

Leakage resilient cryptography is often considered in the presence of a very strong leakage oracle: An adversary may submit arbitrary efficiently computable function f to the leakage oracle to receive f(x), where x denotes the entire secret that a party possesses. This model is somewhat too strong in the setting of public-key encryption (PKE). It is known that no secret-key leakage resilient PKE scheme exists if the adversary may have access to the secret-key leakage oracle to receive only one bit after it was given the challenge ciphertext. Similarly, there exists no sender-randomness leakage resilient PKE scheme if one-bit leakage occurs after the target public key was given to the adversary. At TCC 2011, Halevi and Lin have broken the barrier of after-the-fact leakage, by proposing the so-called split state model, where a secret key of a party is explicitly divided into at least two pieces, and the adversary may have not access to the entire secret at once, but each divided pieces, one by one. In the split-state model, they have constructed post-challenge secret-key leakage resilient CPA secure PKEs from hash proof systems, but the construction of CCA secure post-challenge secret-key leakage PKE has remained open. They have also remained open to construct sender-randomness leakage PKE in the split state model. This paper provides a solution to the open issues. We also note that the proposal of Halevi and Lin is post-challenge secret-key leakage CPA secure against a single challenge ciphertext; not against multiple challenges. We present an efficient generic construction that converts any CCA secure PKE scheme into a multiple-challenge CCA secure PKE that simultaneously tolerates post-challenge secret-key and sender-randomness leakage in the split state model, without any additional assumption. In addition, our leakage amount of the resulting schemes is the same as that of Halevi and Lin CPA PKE, i.e., (1/2+γ)l/2 where l denotes the length of the entire secret (key or randomness) and γ denotes a universal (possitive) constant less than 1/2. Our conversion is generic and available for many other public-key primitives. For instance, it can convert any identity-based encryption (IBE) scheme to a post-challenge master-key leakage and sender-randomness leakage secure IBE.

We introduce a MIMO channel estimation method that exploits the channel's spatiotemporal correlation without the aid of a priori channel statistical information. A simplified Gauss-Markov model that has fewer parameters to be estimated is presented for the Kalman filter. In order to obtain statistical parameters on the time evolution of the channel, considering that the time evolution is a latent statistical variable, the expectation-maximization (EM) algorithm is applied for accurate estimation. Numerical simulations reveal that the proposed method is able to enhance estimation capability by exploiting spatiotemporal correlations, and the method works well even if the forgetting factor is small.

Rainbow is one of signature schemes based on the problem solving a set of multivariate quadratic equations. While its signature generation and verification are fast and the security is presently sufficient under suitable parameter selections, the key size is relatively large. Recently, Quaternion Rainbow — Rainbow over a quaternion ring — was proposed by Yasuda, Sakurai and Takagi (CT-RSA'12) to reduce the key size of Rainbow without impairing the security. However, a new vulnerability emerges from the structure of quaternion ring; in fact, Thomae (SCN'12) found that Quaternion Rainbow is less secure than the same-size original Rainbow. In the present paper, we further study the structure of Quaternion Rainbow and show that Quaternion Rainbow is one of sparse versions of the Rainbow. Its sparse structure causes a vulnerability of Quaternion Rainbow. Especially, we find that Quaternion Rainbow over even characteristic field, whose security level is estimated as about the original Rainbow of at most 3/4 by Thomae's analysis, is almost as secure as the original Rainbow of at most 1/4-size.

In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.

Post-Silicon Tunable (PST) buffers are widely adopted in high-performance integrated circuits to fix timing violations introduced by process variations. In typical optimization procedures, the statistical timing analysis of the circuits with PST clock buffers will be executed more than 2000 times for large scale circuits. Therefore, the efficiency of the statistical timing analysis is crucial to the PST clock buffer optimization algorithms. In this paper, we propose a stochastic collocation based efficient statistical timing analysis method for circuits with PST buffers. In the proposed method, we employ the Howard algorithm to calculate the clock periods of the circuits on less than 100 deterministic sparse-grid collocation points. Afterwards, we use these obtained clock periods to derive the yield of the circuits according to the stochastic collocation theory. Compared with the state-of-the-art statistical timing analysis method for the circuits with PST clock buffers, the proposed method achieves up to 22X speedup with comparable accuracy.

We proposed a through-hole based transformer between microstrip line and post-wall waveguide (PWW). The transformer is based on a through-hole which is electrically separated from top and bottom broad walls of a waveguide by ring-shaped spaces. The proposed transformer shows a considerable merit compared with a conventional one based on blind-via in terms of fabrication and cost because it can be realized by through-holes in general printed-circuit-board technology. We selected liquid crystal polymer (LCP) as the material of substrates because of its lower dielectric loss and easy fabrication. The transformer was fabricated and measured. The loss associated with the mode conversion is estimated to be around 0.32,dB, and the bandwidth for a reflection smaller than $-15$,dB is 8,GHz, i.e., from 59 to 67,GHz.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

A smoothing method for minimum phone error linear regression (MPELR) is proposed in this paper. We show that the objective function for minimum phone error (MPE) can be combined with a prior mean distribution. When the prior mean distribution is based on maximum likelihood (ML) estimates, the proposed method is the same as the previous smoothing technique for MPELR. Instead of ML estimates, maximum a posteriori (MAP) parameter estimate is used to define the mode of prior mean distribution to improve the performance of MPELR. Experiments on a large vocabulary speech recognition task show that the proposed method can obtain 8.4% relative reduction in word error rate when the amount of data is limited, while retaining the same asymptotic performance as conventional MPELR. When compared with discriminative maximum a posteriori linear regression (DMAPLR), the proposed method shows improvement except for the case of limited adaptation data for supervised adaptation.

In this paper, we propose a message passing decoding algorithm which lowers decoding error rates in the error floor regions for non-binary low-density parity-check (LDPC) codes transmitted over the binary erasure channel (BEC) and the memoryless binary-input output-symmetric (MBIOS) channels. In the case for the BEC, this decoding algorithm is a combination with belief propagation (BP) decoding and maximum a posteriori (MAP) decoding on zigzag cycles, which cause decoding errors in the error floor region. We show that MAP decoding on the zigzag cycles is realized by means of a message passing algorithm. Moreover, we extend this decoding algorithm to the MBIOS channels. Simulation results demonstrate that the decoding error rates in the error floor regions by the proposed decoding algorithm are lower than those by the BP decoder.

Even with the recent development of new types of social networking services such as microblogs, Bulletin Board Systems (BBS) remains popular for local communities and vertical discussions. These BBS sites have high volume of traffic everyday with user discussions on a variety of topics. Therefore it is difficult for BBS visitors to find the posts that they are interested in from the large amount of discussion threads. We attempt to explore several main characteristics of BBS, including organizational flexibility of BBS texts, high data volume and aging characteristic of BBS topics. Based on these characteristics, we propose a novel method of Online Topic Detection (OTD) on BBS, which mainly includes a representative post selection procedure based on Markov chain model and an efficient topic clustering algorithm with candidate topic set generation based on Aging Theory. Experimental results show that our method improves the performance of OTD in BBS environment in both detection accuracy and time efficiency. In addition, analysis on the aging characteristic of discussion topics shows that the generation and aging of topics on BBS is very fast, so it is wise to introduce candidate topic set generation strategy based on Aging Theory into the topic clustering algorithm.

Multivariate Public Key Cryptosystems (MPKC) are candidates for post-quantum cryptography. Rainbow is a digital signature scheme in MPKC, whose signature generation and verification are relatively efficient. However, the security of MPKC depends on the difficulty in solving a system of multivariate polynomials, and the key length of MPKC becomes substantially large compared with that of RSA cryptosystems for the same level of security. The size of the secret and public keys in MPKC has been reduced in previous research. The NC-Rainbow is a signature scheme in MPKC, which was proposed in order to reduce the size of secret key of Rainbow. So far, several attacks against NC-Rainbow have been proposed. In this paper, we summarize attacks against NC-Rainbow, containing attacks against the original Rainbow, and analyze the total security of NC-Rainbow. Based on the cryptanalysis, we estimate the security parameter of NC-Rainbow at the several security level.

In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.