Frequency-domain equalization (FDE) based on minimum mean square error (MMSE) criterion can replace the conventional rake combining to significantly improve the bit error rate (BER) performance in a frequency-selective fading channel. MMSE-FDE requires an accurate estimate of the channel transfer function and the signal-to-noise power ratio (SNR). Direct application of pilot-assisted channel estimation (CE) degrades the BER performance, since the frequency spectrum of the pilot chip sequence is not constant over the spreading bandwidth. In this paper, we propose a pilot-assisted decision feedback frequency-domain MMSE-CE. The BER performance with the proposed pilot-assisted MMSE-CE in a frequency-selective Rayleigh fading channel is evaluated by computer simulation. It is shown that MMSE-CE always gives a good BER performance irrespective of the choice of the pilot chip sequence and shows a high tracking ability against fading. For a spreading factor SF of 16, the Eb/N0 degradation for BER=10-4 with MMSE-CE from the ideal CE case is as small as 0.9 dB (including an Eb/N0 loss of 0.28 dB due to the pilot insertion).

Internet access traffic follows hourly patterns that depend on various factors, such as the periods users stay on-line at the access point (e.g. at home or in the office) or their preferences for applications. The clustering of Internet users may provide important information for traffic engineering and billing. For example, it can be used to set up service differentiation according to hourly behavior, resource optimization based on multi-hour routing and definition of tariffs that promote Internet access in low busy hours. In this work, we propose a methodology for clustering Internet users with similar patterns of Internet utilization, according to their hourly traffic utilization. The methodology resorts to three statistical multivariate analysis techniques: cluster analysis, principal component analysis and discriminant analysis. The methodology is illustrated through measured data from two distinct ISPs, one using a CATV access network and the other an ADSL one, offering distinct traffic contracts. Principal component analysis is used as an exploratory tool. Cluster analysis is used to identify the relevant Internet usage profiles, with the partitioning around medoids and Ward's method being the preferred clustering methods. For the two data sets, these methods lead to the choice of 3 clusters with different hourly traffic utilization profiles. The cluster structure is validated through discriminant analysis. It is also evaluated in terms of several characteristics of the user traffic not used in the cluster analysis, such as the type of applications, the amount of downloaded traffic, the activity duration and the transfer rate, resulting in coherent outcomes.

This paper describes a novel parameter generation algorithm for an HMM-based speech synthesis technique. The conventional algorithm generates a parameter trajectory of static features that maximizes the likelihood of a given HMM for the parameter sequence consisting of the static and dynamic features under an explicit constraint between those two features. The generated trajectory is often excessively smoothed due to the statistical processing. Using the over-smoothed speech parameters usually causes muffled sounds. In order to alleviate the over-smoothing effect, we propose a generation algorithm considering not only the HMM likelihood maximized in the conventional algorithm but also a likelihood for a global variance (GV) of the generated trajectory. The latter likelihood works as a penalty for the over-smoothing, i.e., a reduction of the GV of the generated trajectory. The result of a perceptual evaluation demonstrates that the proposed algorithm causes considerably large improvements in the naturalness of synthetic speech.

This paper proposes the most effective deployment scenario of the passive double-star (PON) system to provide multiple broadband services, such as high speed Internet access and broadcast services. The deployment costs of the two major PON technologies, wavelength division multiplexing (WDM) and 10 Gbps time division multiplexing (TDM), are analyzed using the latest cost trend and the most popular access network architecture. These two technologies are compared using the cost analysis results to identify the cost-effective scenarios of PON system deployment. Based on the comparison, this paper reveals that the WDM network becomes cost effective when the service penetration and the shift ratio becomes high.

Manually collecting contexts of a target word and grouping them based on their meanings yields a set of word senses but the task is quite tedious. Towards automated lexicography, this paper proposes a word-sense discrimination method based on two modern techniques; EM algorithm and principal component analysis (PCA). The spherical Gaussian EM algorithm enhanced with PCA for robust initialization is proposed to cluster word senses of a target word automatically. Three variants of the algorithm, namely PCA, sGEM, and PCA-sGEM, are investigated using a gold standard dataset of two polysemous words. The clustering result is evaluated using the measures of purity and entropy as well as a more recent measure called normalized mutual information (NMI). The experimental result indicates that the proposed algorithms gain promising performance with regard to discriminate word senses and the PCA-sGEM outperforms the other two methods to some extent.

This paper investigates the dynamic spectrum management problem for digital subscriber lines. Two new distributed dynamic spectrum management algorithms, which improve upon the existing iterative water-filling algorithm, are proposed. Unlike the iterative water-filling algorithm, in which crosstalk interference is reduced by using adaptive power backoff, the new algorithms employ full power and mitigate crosstalk interference by shifting one user's spectrum away from the other's. Simulation results show that the new algorithms achieve significant performance gains over the iterative water-filling algorithm in mixed central office/remote terminal (CO/RT) deployment asymmetric digital subscriber line (ADSL) and upstream very-high bit-rate digital subscriber line (VDSL).

Let G be any graph with property P (for example, general graph, directed graph, etc.) and S be nonnegative and non-decreasing integer sequence(s). The prescribed degree sequence problem is a problem to determine whether there is a graph G having S as the prescribed sequence(s) of degrees or outdegrees of the vertices. From 1950's, P has attracted wide attentions, and its many extensions have been considered. Let P be the property satisfying the following (1) and (2):(1) G is a directed graph with two disjoint vertex sets A and B. (2) There are r11 (r22, respectively) directed edges between every pair of vertices in A(B), and r12 directed edges between every pair of vertex in A and vertex in B. Then G is called an (r11, r12, r22)-tournament ("tournament", for short). The problem is called the score sequence pair problem of a "tournament" (realizable, for short). S is called a score sequence pair of a "tournament" if the answer of the problem is "yes." In this paper, we propose the characterizations of a score sequence pair of a "tournament" and an algorithm for determining in linear time whether a pair of two integer sequences is realizable or not.

A typical watermarking scheme consists of an embedding scheme and a detection scheme. In detecting a watermark, there are two kinds of detection errors, a false positive error (FPE) and a false negative error (FNE). A detection scheme is said to be optimum if the FNE probability is minimized for a given FPE probability. In this paper, we present an optimum watermark detection scheme for an additive embedding scheme with a spatial domain. The key idea of the proposed scheme is to use the differences between two brightnesses for detecting a watermark. We prove that under the same FPE probability the FNE probability of the proposed optimum detection scheme is no more than that of the previous optimum detection scheme for the additive embedding scheme with the spatial domain. Then, it is confirmed that for an actual image, the FNE probability of the proposed optimum detection scheme is much lower than that of the previous optimum detection scheme. Moreover, it is confirmed experimentally that the proposed optimum detection scheme can control the FPE probability strictly so that the FPE probability is close to a given probability.

This paper presents a formal design of arithmetic circuits using an arithmetic description language called ARITH. The key idea in ARITH is to describe arithmetic algorithms directly with high-level mathematical objects (i.e., number representation systems and arithmetic operations/formulae). Using ARITH, we can provide formal description of arithmetic algorithms including those using unconventional number systems. In addition, the described arithmetic algorithms can be formally verified by equivalence checking with formula manipulations. The verified ARITH descriptions are easily translated into the equivalent HDL descriptions. In this paper, we also present an application of ARITH to an arithmetic module generator, which supports a variety of hardware algorithms for 2-operand adders, multi-operand adders, multipliers, constant-coefficient multipliers and multiply accumulators. The language processing system of ARITH incorporated in the generator verifies the correctness of ARITH descriptions in a formal method. As a result, we can obtain highly-reliable arithmetic modules whose functions are completely verified at the algorithm level.

Watson-Crick automata were introduced as a new computer model and have been intensively investigated regarding their computational power. In this paper, aiming to establish the relations among language families defined by Watson-Crick automata and the family of context-free languages completely, we obtain the following results. (1) F1WK = FSWK = FWK, (2) FWK = AWK, (3) there exists a language which is not context-free but belongs to NWK, and (4) there exists a context-free language which does not belong to AWK.

A function approximation based on an orthonormal wave function expansion in a complex space is derived. Although a probability density function (PDF) cannot always be expanded in an orthogonal series in a real space because a PDF is a positive real function, the function approximation can approximate an arbitrary PDF with high accuracy. It is applied to an actor-critic method of reinforcement learning to derive an optimal policy expressed by an arbitrary PDF in a continuous-action continuous-state environment. A chaos control problem and a PDF approximation problem are solved using the actor-critic method with the function approximation, and it is shown that the function approximation can approximate a PDF well and that the actor-critic method with the function approximation exhibits high performance.

In this paper, we present an efficient downlink power control scheme, for wireless networks, based on two key ideas: (i) global-local fixed-point-approximation technique (GLOFPAT) and (ii) bottleneck removal criterion (BRC). The proposed scheme copes with all scenarios including infeasible case where no power allocation can provide all multiple accessing users with target quality of service (QoS). For feasible case, the GLOFPAT efficiently computes a desired power allocation which corresponds to the allocation achieved by conventional algorithms. For infeasible case, the GLOFPAT offers valuable information to detect bottleneck users, to be removed based on the BRC, which deteriorate overall QoS. The GLOFPAT is a mathematically-sound distributed algorithm approximating desired power allocation as a unique fixed-point of an isotone mapping. The unique fixed-point of the global mapping is iteratively computed by fixed-point-approximations of multiple distributed local mappings, which can be computed in parallel by base stations respectively. For proper detection of bottleneck users, complete analysis of the GLOFPAT is presented with aid of the Tarski's fixed-point theorem. Extensive simulations demonstrate that the proposed scheme converges faster than the conventional algorithm and successfully increases the number of happy users receiving target QoS.

For obtaining a higher level of generalization capability in supervised learning, model parameters should be optimized, i.e., they should be determined in such a way that the generalization error is minimized. However, since the generalization error is inaccessible in practice, model parameters are usually determined in such a way that an estimate of the generalization error is minimized. A standard procedure for model parameter optimization is to first prepare a finite set of candidates of model parameter values, estimate the generalization error for each candidate, and then choose the best one from the candidates. If the number of candidates is increased in this procedure, the optimization quality may be improved. However, this in turn increases the computational cost. In this paper, we give methods for analytically finding the optimal model parameter value from a set of infinitely many candidates. This maximally enhances the optimization quality while the computational cost is kept reasonable.

In this letter, we study the problem of designing an efficient power and bit allocation scheme in the context of a hierarchical image transmission system based on an embedded multi-carrier modulation (EMCM) scheme over digital subscriber line. Authors describe a novel algorithm that performs power minimization under bit rate constraint and QoS requirement. It is based on the Hughes-Hartogs algorithm, and successively allocates the bits of the high, then low priority data streams. Simulations that assess the performance of the proposed algorithm are also provided and discussed; they demonstrate the interest of the proposed scheme.

This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. Robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induce large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The combination of KFD space and posterior probability of image sequence is the main contribution of the proposed method. The performance is evaluated by using two face databases. Effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.

In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In particular, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCA-based technique over the conventional PCA technique.

This paper presents a video coding method that improves error resilient functionality of H.264 with good coding efficiency. The method is based on PD (polyphase downsampling) multiple description coding. The only changes to H.264 are inserting PD before the DCT process and having new data partitioning NAL units. A coded slice is sent on 3 data partitioning NAL units. A header NAL unit contains motion vectors and block modes. Each of the other two NAL units contains a description generated by PD multiple description coding. The experimental results on all 9 of the test sequences of JVT SVC show that the proposed method gives 0.5 to 5 dB enhancement over the existing H.264 FMO checker board mode with motion vector based error-concealment.

According to the Gestalt principals, this paper presents a recognition method of grouping areas in trademark images modeling features for measuring the attraction degree between couples of image components. This investigation would be used for content-based image retrieval from the view of mirroring human perception for images. Depending on variability in human perception for trademark images, the proposed method finds grouping areas by calculating Mahalanobis distance with the features to every combination of two components in images. The features are extracted from every combination of two components in images, and the features represent proximity, shape similarity, and closure between two components. In addition, changing combination of the features, plural grouping patterns are output. Besides, this paper shows the efficiency and limits of the proposed method from experimental results. In the experiments, 104 participants have perceived grouping patterns to 74 trademark images and the human perceptions have been compared with outputs by the proposed method for the 74 images.

Content-based publish/subscribe systems provide a useful alternative to traditional address-based communication due to their ability to decouple communication between participants. It has remained a challenge to design a scalable overlay supporting the complexity of content-based networks, while satisfying the desirable properties large distributed systems should have. This paper presents the design of Mirinae, a new structured peer-to-peer overlay mesh based on the interests of peers. Given an event, Mirinae provides a flexible and efficient dissemination tree minimizing the participation of non-matching nodes. We also present a novel ID space transformation mechanism for balancing routing load of peers even with highly skewed data, which is typical of the real world. Our evaluation demonstrates that Mirinae is able to achieve its goals of scalability, efficiency, and near-uniform load balancing. Mirinae can be used as a substrate for content-search and range query in other important distributed applications.

In this paper, we treat a design problem for IIR digital filters described by rational transfer function in discrete space. First, we form the filter design problem using the modified least-squares (MLS) criterion and express it as the quadratic form with respect to the numerator and denominator coefficients. Next, we show the relaxation method using the Lagrange multiplier method in order to search for the good solution efficiently. Additionally we can check the filter stability when designing the denominator coefficients. Finally, we show the effectiveness of the proposed method using a numerical example.