This paper proposes an all-digital process variability monitor which utilizes a simple buffer ring with a pulse counter. The proposed circuit monitors the process variability according to a count number of a single pulse which propagates on the buffer ring and a fixed logic level after the pulse vanishes. The proposed circuit has been fabricated in 65 nm CMOS process and the measurement results demonstrate that we can monitor the PMOS and NMOS variabilities independently using the proposed monitoring circuit. The proposed monitoring technique is suitable not only for the on-chip process variability monitoring but also for the in-field monitoring of aging effects such as negative/positive bias instability (NBTI/PBTI).

On uniformly convex real Banach spaces, a fixed point theorem in weak topology for successively recurrent system of fuzzy-set-valued nonlinear mapping equations and its application to ring nonlinear network systems are theoretically discussed in detail. An arbitrarily-level likelihood signal estimation is then established.

Reliability issues such as a soft error and NBTI (negative bias temperature instability) have become a matter of concern as integrated circuits continue to shrink. It is getting more and more important to take reliability requirements into account even for consumer products. This paper presents a dynamic continuous signature monitoring (DCSM) technique for high reliable computer systems. The DCSM technique dynamically generates reference signatures as well as runtime ones during executing a program. The DCSM technique stores the generated signatures in a signature table, which is a small storage circuit in a microprocessor, unlike the conventional static continuous signature monitoring techniques and contributes to saving program or data memory space that stores the signatures. Our experiments showed that our DCSM technique protected 1.4-100.0% of executed instructions depending on the size of signature tables.

A small broadband omni-directional printed antenna comprising symmetrically arranged trapezoid elements is investigated for broadband Voltage Standing Wave Ratio (VSWR) and low center frequency characteristics. Two symmetrical trapezoid elements are printed on the bottom side of the substrate and are connected to a small ground plane printed on the same side over two strips. The trapezoid elements and the strips are excited in an electromagnetically coupled manner by the monopole element set between the trapezoid elements. Two resonance characteristics arise because the resonance part changes depending on the frequency, and a broad bandwidth becomes possible. The center frequency can be lowered by changing the shapes of the trapezoid elements. The monopole element length is a very important parameter for impedance matching. The space between the monopole element and the trapezoid elements is an important parameter for the optimization of two resonance characteristics. The proposed antenna is shown to achieve a VSWR bandwidth (≤2) of 28.9%, a low profile, and omni-directional pattern features. The measured and numerical results are in good agreement.

In this paper, we propose a novel coding scheme for the geometry of the triangular mesh model. The geometry coding schemes can be classified into two groups: schemes with perfect reconstruction property that maintains their connectivity, and schemes without it in which the remeshing procedure is performed to change the mesh to semi-regular or regular mesh. The former schemes have good coding performance at higher coding rate, while the latter give excellent coding performance at lower coding rate. We propose a geometry coding scheme that maintains the connectivity and has a perfect reconstruction property. We apply a method that successively structures on 2-D plane the surrounding vertices obtained by expanding vertex sequences neighboring the previous layer. Non-separable component decomposition is applied, in which 2-D structured data are decomposed into four components depending on whether their location was even or odd on the horizontal and vertical axes in the 2-D plane. And a prediction and update are performed for the decomposed components. In the prediction process the predicted value is obtained from the vertices, which were not processed, neighboring the target vertex in the 3-D space. And the zero-tree coding is introduced in order to remove the redundancies between the coefficients at similar positions in different resolution levels. SFQ (Space-Frequency Quantization) is applied, which gives the optimal combination of coefficient pruning for the descendant coefficients of each tree element and a uniform quantization for each coefficient. Experiments applying the proposed method to several polygon meshes of different resolutions show that the proposed method gives a better coding performance at lower bit rate when compared to the conventional schemes.

With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.

Scalability and availability are the key features of parallel database systems. To realize scalability, many dynamic load-balancing methods with data placement and parallel index structures on shared-nothing parallel infrastructure have been proposed. Data migration with range-partitioned placement using a parallel Btree is one solution. The combination of range partitioning and chained declustered replicas provides high availability (HA) while preserving scalability. However, independent treatment of the primary and backup data in each node requires long failover times. We propose a novel method for the compound treatment of chained declustered replicas using a parallel Btree, termed the Fat-Btree. In the proposed method, a single Fat-Btree provides access paths to both the primary and backup data of all processor elements (PEs), which greatly reduces failover time. Moreover, these access paths overlap between two neighboring PEs, which enables dynamic load balancing without physical data migration by dynamically redirecting the access paths. In addition, this compound treatment improves memory space utilization to enable index processing with good scalability. Experiments using PostgreSQL on a 160-node PC cluster demonstrate the effectiveness of the high scalability and availability of our proposed method.

This paper proposes Bayesian context clustering using cross validation for hidden Markov model (HMM) based speech recognition. The Bayesian approach is a statistical technique for estimating reliable predictive distributions by treating model parameters as random variables. The variational Bayesian method, which is widely used as an efficient approximation of the Bayesian approach, has been applied to HMM-based speech recognition, and it shows good performance. Moreover, the Bayesian approach can select an appropriate model structure while taking account of the amount of training data. Since prior distributions which represent prior information about model parameters affect estimation of the posterior distributions and selection of model structure (e.g., decision tree based context clustering), the determination of prior distributions is an important problem. However, it has not been thoroughly investigated in speech recognition, and the determination technique of prior distributions has not performed well. The proposed method can determine reliable prior distributions without any tuning parameters and select an appropriate model structure while taking account of the amount of training data. Continuous phoneme recognition experiments show that the proposed method achieved a higher performance than the conventional methods.

A millimeter-wave termination which is tolerant to the resistance error of the embedded resistive film in a multi-layered LTCC substrate has been developed. The tolerance to the resistance error can be accomplished using two bifurcated strip lines overlapping with the resistive film, whose lengths are different form each other. It has been experimentally demonstrated that the proposed termination configuration is effective to enhance the tolerance to resistance error of the embedded resistive film in the LTCC substrate.

In this paper, we present a specific type of irreducible polynomial which is an irreducible m-term polynomial of degree m. Designing the parallel multiplier over GF(2m) by the quadrinomial obtained from this irreducible polynomial, its critical delay path is smaller than that of conventional multipliers for some degree m.

We investigated the photovoltaic properties of multilayered devices consisting of ITO/oxide/Tetraphenyl porphyrin (H2TPP)/Fullerene (C60)/Bathocuproine (BCP)/Al structures. The VOC markedly increases with the insertion of NiO and MoO3 hole collection layers. However, the "kink" behaviors and temperature dependent properties are observed for the devices with and without MoO3 especially for the thick H2TPP film. We demonstrated the analysis of the photovoltaic properties using the Poole-Frenkel and Schottky models based on the dielectric behaviors of porphyrin and MoO3 layers.

Let C be a set of colors, and let ω(c) be an integer cost assigned to a color c in C. An edge-coloring of a graph G is to color all the edges of G so that any two adjacent edges are colored with different colors in C. The cost ω(f) of an edge-coloring f of G is the sum of costs ω(f(e)) of colors f(e) assigned to all edges e in G. An edge-coloring f of G is optimal if ω(f) is minimum among all edge-colorings of G. In this paper, we show that the problem of finding an optimal edge-coloring of a tree T can be simply reduced in polynomial time to the minimum weight perfect matching problem for a new bipartite graph constructed from T. The reduction immediately yields an efficient simple algorithm to find an optimal edge-coloring of T in time O(n1.5Δlog(nNω)), where n is the number of vertices in T, Δ is the maximum degree of T, and Nω is the maximum absolute cost |ω(c)| of colors c in C. We then show that our result can be extended for multitrees.

We review our recent work on ultra-long-haul wavelength division multiplexed (WDM) transmission with high spectral efficiency (SE) employing tight pre-filtering and multi-symbol detection. We start the discussion with a theoretical evaluation of the SE limit of pre-filtered modulation in optical fiber communication systems. We show that pre-filtering induced symbol correlation generates a modulation with memory and thus, a higher SE limit than that of the original memory-less modulation. We also investigate the merits of utilizing the pre-filtering induced symbol correlation with multi-symbol detection to achieve high SE transmission. We demonstrate transoceanic WDM transmission of a pre-filtered polarization division multiplexed return-to-zero quaternary phased shift keying (PDM-RZ-QPSK) modulation format with multi-symbol detection, achieving 419% SE which is higher than the SE limit of the original memory-less PDM-RZ-QPSK format.

The traditional spectrum auctions require a central auctioneer. Then, the secondary users (SUs) can bid for spectrum in multiple auction or sealed auction way. In this paper, we address the problem of distributed spectrum sharing in the cognitive networks where multiple owners sell their spare bands to multiple SUs. Each SU equips multi-interface/multi-radio, so that SU can buy spare bands from multiple owners. On the other hand, each owner can sell its spare bands to serval SUs. There are two questions to be addressed for such an environment: the first one is how to select bands/the owners for each SU; the second one is how to decide the competitive prices for the multiple owners and multiple SUs. To this end, we propose a two-side multi-band market game theoretic framework to jointly consider the benefits of all SUs and owners. The equilibrium concept in such games is named core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all SUs and owners. We show that the core in our model is always non-empty. When the measurement of price takes discrete value, the core of the game is defined as discrete core. The Dynamic Multi-band Sharing algorithm (DMS) is proposed to converge to the discrete core of the game. With small enough measurement unit of price, the algorithm can achieve the optimal performance compared with centralized one in terms of the system utility.

As a basic tool for deriving sparse representation of a color image from its atomic-decomposition with a redundant dictionary, the authors have recently proposed a new kind of shrinkage technique, viz. color shrinkage, which utilizes inter-channel color dependence directly in the three primary color space. Among various schemes of color shrinkage, this paper particularly presents the soft color-shrinkage and the hard color-shrinkage, natural extensions of the classic soft-shrinkage and the classic hard-shrinkage respectively, and shows their advantages over the existing shrinkage approaches where the classic shrinkage techniques are applied after a color transformation such as the opponent color transformation. Moreover, this paper presents the applications of our color-shrinkage schemes to color-image processing in the redundant tight-frame transform domain, and shows their superiority over the existing shrinkage approaches.

This paper presents a non-interactive verifiable secret sharing scheme (VSS) tolerating a dishonest majority based on data pre-distributed by a trusted authority. As an application of this VSS scheme we present very efficient unconditionally secure protocols for performing multiplication of shares based on pre-distributed data which generalize two-party computations based on linear pre-distributed bit commitments. The main results of this paper are a non-interactive VSS, a simplified multiplication protocol for shared values based on pre-distributed random products, and non-interactive zero knowledge proofs for arbitrary polynomial relations. The security of the schemes is proved using the UC framework.

It is theoretically impossible to restore the original fingerprint image from a sequence of line images captured by a line sensor. However, in this paper we propose a unique fingerprint-image-generation algorithm, which derives fingerprint images from sequences of line images captured at different swipe speeds by the line sensor. A continuous image representation, called trajectory, is used in modeling distortion of raw fingerprint images. Sequences of line images captured from the same finger are considered as sequences of points, which are sampled on the same trajectory in N-dimensional vector space. The key point here is not to reconstruct the original image, but to generate identical images from the trajectory, which are independent of the swipe speed of the finger. The method for applying the algorithm in a practical application is also presented. Experimental results on a raw fingerprint image database from a line sensor show that the generated fingerprint images are independent of swipe speed, and can achieve remarkable matching performance with a conventional minutiae matcher.

This paper describes a novel channel allocation and DBA (Dynamic Bandwidth Allocation) mechanism for ECDM-PON (Electric Code Division Multiplex -- Passive Optical Network) systems. In the current ECDM-PON systems, each ONU (Optical Network Unit) is limited to 2 or 3 CDM channels. This is because (fixed channel) CDM transmitters are expensive, and tunable CDM transmitters even more expensive. With a small number of CDM channels, the bandwidth utilization ratio is restricted by channel blocking. Our proposed mechanisms can reduce the channel blocking ratio without increasing the number of CDM transmitters or using tunable CDM transmitters. To clarify the advantages of the proposed system performance, we have evaluated the channel non-blocking ratio (Rn) and wasted resource ratio (Rw) when some users request bandwidth more than 100%. Evaluation of the non-blocking ratio, Rn shows that the proposed mechanisms approach the performance of a system with tunable CDM transmitters when the number of ONUs with over 100% traffic load is small. We have also simulated throughput for uniform traffic. In addition to these evaluations, we implemented our proposed mechanism on an FPGA (Field Programming Gate Array) and evaluated the calculation speed to allocate timeslots on CDM channels and a timeline.

This study focuses on speaker adaptation techniques for Computer-Assisted Language Learning (CALL). We first investigate the effects and problems of Maximum Likelihood Linear Regression (MLLR) speaker adaptation when used in pronunciation evaluation. Automatic scoring and error detection experiments are conducted on two publicly available databases of Japanese learners' English pronunciation. As we expected, over-adaptation causes misjudgment of pronunciation accuracy. Following the analysis, we propose a novel method, Regularized Maximum Likelihood Regression (Regularized-MLLR) adaptation, to solve the problem of the adverse effects of MLLR adaptation. This method uses a group of teachers' data to regularize learners' transformation matrices so that erroneous pronunciations will not be erroneously transformed as correct ones. We implement this idea in two ways: one is using the average of the teachers' transformation matrices as a constraint to MLLR, and the other is using linear combinations of the teachers' matrices to represent learners' transformations. Experimental results show that the proposed methods can better utilize MLLR adaptation and avoid over-adaptation.

Various kinds of content replication strategies in pure P2P networks have recently been examined. However, such strategies have not been thoroughly considered in hybrid P2P networks. In a hybrid P2P network, the target content can easily be found because there is a server that controls each peer and its content. Therefore, it is important to decrease futile storage resource consumption, since the data search cost through the network is relatively small. This paper proposes an effective content replication strategy that takes into account storage capacity restrictions. In brief, this method restricts the number of contents replicas possessed by a peer using threshold-based control by relocating or deleting excess replicas. Furthermore, the effectiveness of the proposal is evaluated using computer simulations.