The Medium Access Control (MAC) protocol that uses non-overlapping multiple channels, called the multi-channel MAC protocol, was proposed in order to increase the capacity of ad hoc networks. Since the number of packet interfaces on each node is less than the number of channels in ad hoc networks in general, the node needs to select a suitable channel for data transmission. This means that the multi-channel MAC protocol must be provided with a good channel selection algorithm. In this paper, we design a channel selection algorithm called Conditionally Randomized Channel Selection (CRCS) based on Extended Receiver Directed Transmission (xRDT) protocol that only uses one packet interface. Briefly, CRCS uses the acitve channel for data transmission until the amount of data packets reaches a threshold, at which point it selects one of the available channels other than the active channel. Although CRCS is a very simple channel selection algorithm, by using network simulator we find that CRCS is effective to increase the capacity of ad hoc networks and to keep the load balance of all channels compared to the other channel selection algorithms.

Hypercube Qn is a well-known graph structure having three different kinds of equivalent definitions that are: 1. binary n bit sequences with the adjacency condition, 2. Q1=K2, Qn=Qn-1 K2, where means the Cartesian product, 3. the Cayley graph on Z2n with the generator set {100, 0100, , 001}. We give a necessary and sufficient condition for a set of binary sequences to be a generator set for the hypercube. Then, we give relations between some generator sets and relational products. These results show the wide variety of representability of hypercubes which would be used for many applications.

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.

This paper presents a novel dynamic subchannel allocation scheme that can improve the cell capacity by coordinating the intercell interference (ICI) in a cellular orthogonal frequency division multiple access (OFDMA) system. The proposed scheme mitigates the ICI by adopting the virtual cell concept and improves the frequency reuse factor through subchannel reuse among different virtual cells. In particular, each virtual cell is assigned a primary and a secondary subchannel group, and each sector base station (BSs) allocates the subchannel resulting in the least ICI in probability out of the candidate subchannels to the mobile stations, dynamically searching from its primary group and then secondary group. In addition, an optional use of pico-cell overlay at the intersection of the virtual cells is also proposed to enhance the fairness of the proposed scheme with the BS-MS distance. Through computer simulation, it is shown that the proposed scheme has the advantages of improved cell capacity and fairness compared to the conventional schemes.

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair in which the source language is unsegmented and the target language segmentation is known. In the first step, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the proposed method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available monolingually built segmentation tools.

We propose a blind dereverberation method based on spectral subtraction using a multi-channel least mean squares (MCLMS) algorithm for distant-talking speech recognition. In a distant-talking environment, the channel impulse response is longer than the short-term spectral analysis window. By treating the late reverberation as additive noise, a noise reduction technique based on spectral subtraction was proposed to estimate the power spectrum of the clean speech using power spectra of the distorted speech and the unknown impulse responses. To estimate the power spectra of the impulse responses, a variable step-size unconstrained MCLMS (VSS-UMCLMS) algorithm for identifying the impulse responses in a time domain is extended to a frequency domain. To reduce the effect of the estimation error of the channel impulse response, we normalize the early reverberation by cepstral mean normalization (CMN) instead of spectral subtraction using the estimated impulse response. Furthermore, our proposed method is combined with conventional delay-and-sum beamforming. We conducted recognition experiments on a distorted speech signal simulated by convolving multi-channel impulse responses with clean speech. The proposed method achieved a relative error reduction rate of 22.4% in relation to conventional CMN. By combining the proposed method with beamforming, a relative error reduction rate of 24.5% in relation to the conventional CMN with beamforming was achieved using only an isolated word (with duration of about 0.6 s) to estimate the spectrum of the impulse response.

We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.

We propose an efficient selective block encoding scheme with motion information feedback in distributed video coding (DVC). The proposed scheme estimates the spatial and temporal matching costs for each block in the side information (SI) and for the blocks with high matching costs, the motion information is provided to the encoder side to selectively encode the motion-compensated frame difference signal. Experimental results show that the proposed scheme outperforms the recently developed DVC algorithms.

The long-standing problem of predicting circuit performance variations without a huge number of statistical investigations is demonstrated to be solvable by a surface-potential-based MOSFET model. Direct connection of model parameters to physical device parameters reflecting process variations and the reduced number of model parameters are the enabling key model properties. It has been proven that the surface-potential-based model HiSIM2 is capable of reproducing measured I-V and its derivatives' variations with those of device/process related model parameters. When used to predict 51-stage ring oscillator frequency variation including both inter- and intra-chip variation, it reproduces measurements with shortened simulation time.

A new state estimation algorithm is presented for a class of LTI systems that have an input disturbance in polynomial form and a sinusoidal sensor disturbance in the measurement output. Adaptation rules are developed for identifying the unknown magnitude, phase and frequency of the sensor disturbance from the system output measurement. For the application of the identification result to the state estimation problem, the sinusoidal signal with arbitrary initial phase has been considered in this paper. In order to test the performance of the proposed algorithm, comparative computer simulations have been carried out with a robust state observer. Simulation results show the effectiveness of the proposed method.

This paper presents a low complexity partially folded architecture of transposed FIR filter and cubic B-spline interpolator for ATSC terrestrial broadcasting systems. By using the multiplexer, the proposed FIR filter and interpolator can provide high clock frequency and low hardware complexity. A binary representation method was used for designing the high order FIR filter. Also, in order to compensate the truncation error of FIR filter outputs, a fixed-point range detection method was used. The proposed partially folded architecture was designed and implemented with 90-nm CMOS technology that had a supply voltage of 1.1 V. The implementation results show that the proposed architectures have 12% and 16% less hardware complexity than the other kinds of architecture. Also, both the filter and the interpolator operate at a clock frequency of 200 MHz and 385 MHz, respectively.

This study shows a fast simulation method for turbo codes over an additive white class A noise (AWAN) channel. The reduction of the estimation time is achieved by applying importance sampling (IS) which is one of the variance reduction simulation methods. In order to adapt the AWAN channel, we propose a design method of a simulation probability density function (PDF) utilized in IS. The proposed simulation PDF is related to the Bhattacharyya bound to evaluate wider area of the signal space than the conventional method. Since the mean translation method, which is a conventional design method of the simulation PDF used in IS, is optimized for an additive white Gaussian noise channel, the evaluation time of the error performance of turbo codes over the AWAN channel can not be reduced. To evaluate BER of 10-8, the simulation time of the proposed method can be reduced to 1/104 under the condition of the same accuracy of the traditional Monte Carlo simulation method.

We describe the fabrication processes and electrical characteristics of two types of NbN junctions. One is a self-shunted NbN/NbNx/AlN/NbN Josephson junction, which is expected to improve the density of integrated circuits; the other is an underdamped NbN/AlNx/NbN tunnel junction with radical-nitride AlNx barriers, which has highly controllable junction characteristics. In the former, the junction characteristics were changed from underdamped to overdamped by varying the thickness of the NbNx layer. Overdamped junctions with a 6-nm-thick NbNx film exhibited a characteristic voltage of Vc = 0.8 mV and a critical current density of Jc = 22 A/cm2 at 4.2 K. In the junctions with radical-nitride AlNx barriers, Jc could be controlled in the range 0.01-3 kA/cm2 by varying the process conditions, and good uniformity of the junction characteristics was obtained.

Providing mobile services that ensure user satisfaction is one of the most crucial issues for telecommunication carriers and service providers. Traditionally, user satisfaction with the service, i.e., the Quality of Experience (QoE), has been assessed in a laboratory by using elaborate network systems and customized terminals. However, reliable QoE for a target mobile service in the context of actual use cannot be obtained by laboratory experiment, since QoE can be affected by a variety of factors such as environmental conditions and the mental state of the user. This paper proposes a novel Web script-based field evaluation method designed to ascertain user satisfaction with mobile services. The proposed method has the following practical advantages. Since experimental conditions, especially communication conditions, can be simulated with a Web script, such as FlashLite, a subjective assessment can be conducted anywhere using the mobile terminal of the participant. Thus, QoE for a target mobile service in the field can easily be obtained.

Due to the increased preservation efforts, large amounts of past Web data have been stored in Web archives and other archival repositories. Utilizing this data can offer certain benefits to users, for example, it can facilitate page understanding. In this paper, we propose a system for interactive exploration of page histories. We demonstrate an application called Page History Explorer (PHE) for summarizing and visualizing histories of Web pages. PHE portrays the overview of page evolution, characterizes its typical content over time and lets users observe page histories from different viewpoints. In addition, it enables flexible comparison of histories of different pages.

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.

XQuery has become the standard for querying XML. Just like SQL, XQuery allows nested expressions. To optimize XQuery processing, a lot of research has been done on normalization, i.e., transforming nested expressions to equivalent unnested ones. Previous normalization rules are classified into two categories—source-level/ and algebra-level/—depending on whether a construct is specified in the XQuery syntax or as equivalent algebraic expressions. From an implementation point of view, the former is preferable to the latter since it can be implemented in a variety of XQuery engines with different algebras. However, existing source-level rules have several problems: They do not handle quantified expressions, incur duplicated query results, and use many temporary files. In this paper, we propose new source-level normalization rules that solve these problems. Through analysis and experiments, we show that our normalization rules can reduce query execution time from hours to a few seconds and can be adapted to a variety of XQuery engines.

Due to the limitations of language-processing tools for the Thai language, pattern-based information extraction from Thai documents requires supplementary techniques. Based on sliding-window rule application and extraction filtering, we present a framework for extracting semantic information from medical-symptom phrases with unknown boundaries in Thai unstructured-text information entries. A supervised rule learning algorithm is employed for automatic construction of information extraction rules from hand-tagged training symptom phrases. Two filtering components are introduced: one uses a classification model to predict rule application across a symptom-phrase boundary based on instantiation features of rule internal wildcards, the other uses weighted classification confidence to resolve conflicts arising from overlapping extractions. In our experimental study, we focus our attention on two basic types of symptom phrasal descriptions: one is concerned with abnormal characteristics of some observable entities and the other with human-body locations at which primitive symptoms appear. The experimental results show that the filtering components improve precision while preserving recall satisfactorily.

Recently, to track and relate news documents from several sources, association rule mining has been applied due to its performance and scalability. This paper presents an empirical investigation on how term representation basis, term weighting, and association measure affects the quality of relations discovered among news documents. Twenty four combinations initiated by two term representation bases, four term weightings, and three association measures are explored with their results compared to human judgment of three-level relations: completely related, somehow related, and unrelated relations. The performance evaluation is conducted by comparing the top-k results of each combination to those of the others using so-called rank-order mismatch (ROM). The experimental results indicate that a combination of bigram (BG), term frequency with inverse document frequency (TFIDF) and confidence (CONF), as well as a combination of BG, TFIDF and conviction (CONV), achieves the best performance to find the related documents by placing them in upper ranks with 0.41% ROM on top-50 mined relations. However, a combination of unigram (UG), TFIDF and lift (LIFT) performs the best by locating irrelevant relations in lower ranks (top-1100) with 9.63% ROM. A detailed analysis on the number of the three-level relations with regard to their rankings is also performed in order to examine the characteristic of the resultant relations. Finally, a discussion and an error analysis are given.

In this paper, we introduce a new sequential pattern, the Interactive User Sequence Pattern (IUSP). This pattern is useful for grouping highly interrelated users in one-way communications such as e-mail, SMS, etc., especially when the communications include many spam users. Also, we propose an efficient algorithm for discovering IUSPs from massive one-way communication logs containing only the following information: senders, receivers, and dates and times. Even though there is a difficulty in that our new sequential pattern violates the Apriori property, the proposed algorithm shows excellent processing performance and low storage cost in experiments on a real dataset.