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.

As semiconductor processing technology advances, complex, high density circuits can be integrated in a chip. However, increasing energy consumption is becoming one of the most important limiting factors. Power estimation at the early stage of design is essential since design changes at later stages may significantly lengthen the design period and increase the costs. For efficient power estimation, we analyze the "key" control signals of a digital circuit and develop power models for several operational modes. The trade-off between accuracy and complexity can be made by choosing the number and the complexity of the power models. When compared with those of logic simulation based estimation, experimental results show that 13 to 15 times faster power estimation with an estimation error of about 5% is possible. We have also developed new logic-level power modeling techniques in which logic gates are levelized and several levels are selected to build power model tables. This table based method shows significant improvement in estimation accuracy and a slight improvement in efficiency when compared to a well-known previous method. The average estimation error has been reduced from 13.3% to 3.8%.

Orthogonal frequency division multiplexing (OFDM) is one of the promising transmission techniques for next generation mobile communication systems. Accurate channel estimation is essential for coherent OFDM signal transmission. So far, many pilot-assisted channel estimation schemes have been proposed. In the case of packet transmission, each received packet can be repeatedly processed by decision feedback to improve the channel estimation accuracy, resulting in a decision directed block iterative channel estimation (DD-BICE). However, decision feedback of erroneously detected data symbols degrades the packet error rate (PER) or bit error rate (BER) performance. In this paper, theoretical analysis is presented for the DD-BICE taking into account the decision feedback errors assuming quadrature phase shift keying (QPSK) data modulation. A 2-dimensional (2D) averaging filter is used for reducing the negative impact of decision feedback errors. The impacts of 2D averaging filter and antenna diversity reception are discussed and the validity of the theoretical analysis is confirmed by computer simulation.

This paper deals with adaptive array beamforming based on stochastic gradient descent independent component analysis (ICA) for suppressing interference with robust capabilities. The approach first uses estimates of the interested source directions to construct the multiple regularized constraints, which form an efficient ICA-based beamformer to achieve fast convergence and more robust capabilities than existing MCMV and ESB beamformers. In conjunction with the regularization parameters of the high-order derivative constraints, the width of the main beam for remaining the desired signal and the depth of nulls for suppressing interferers can be adjusted. Several computer simulation examples are provided for illustration and comparison.

In this paper, a non-data aided minimum mean square error (MMSE) receiver with enhanced multiple access interference (MAI) suppression is proposed for direct-sequence code-division multiple-access (DS-CDMA) systems over a multipath fading channel. The design of the proposed receiver is via the following procedure: First, an adaptive correlator is constructed based on the linearly constrained minimum variance (LCMV) criterion to collect each multipath signal and suppress MAI blindly. A maximum ratio combiner is then utilized to coherently combine the correlator outputs. With a set of judicious chosen weight vectors, effective diversity combining can successfully suppress MAI and the desired signals can be effectively retained. Finally, further performance improvement against the finite data sample effect is achieved using a decision-aided scheme in which the channel response is obtained by the decision data and incorporated with the MMSE method to compute the refined weight vector. Performance analysis based on the output signal-to-interference-plus-noise ratio (SINR) is done to examine the efficacy of the proposed non-data aided MMSE receiver, which can offer the similar results as those of the MMSE receiver with the channel estimation correctly obtained beforehand. Computer simulation results then confirm correctness of the analysis results and demonstrate that the proposed blind receiver can successfully resist MAI as well as the finite data sample effect, and significantly outperform than the conventional blind receivers.

For the optimization design of air circuit breaker (ACB), it is important and necessary to calculate the electro-dynamic repulsion force acting on the movable contact. A method based on 3-D FEM with the equations that describe the relationships among current, magnetic field and repulsion force, which takes the ferromagnet into account, is adopted to calculate the electro-dynamic repulsion force. The method enables one to analyze the factors that affect the electro-dynamic repulsion force, including the number of the movable conductor parallel branches as well as the location of the axis and the shape of the flexible connection. The discussion of the calculation results is also presented in this paper.

We study the problem of optimizing admission control policies in mobile multimedia cellular networks when predictive information regarding movement is available and we evaluate the gains that can be achieved by making such predictive information available to the admission controller. We consider a general class of prediction agents which forecast the number of future handovers and we evaluate the impact on performance of aspects like: whether the prediction refers to incoming and/or outgoing handovers, inaccurate predictions, the anticipation of the prediction and the way that predictions referred to different service classes are aggregated. For the optimization process we propose a novel Reinforcement Learning approach based on the concept of afterstates. The proposed approach, when compared with conventional Reinforcement Learning, yields better solutions and with higher precision. Besides it tackles more efficiently the curse of dimensionality inherent to multimedia scenarios. Numerical results show that the performance gains measured are higher when more specific information is provided about the handover time instants, i.e. when the anticipation time is deterministic instead of stochastic. It is also shown that the utilization of the network is maintained at very high values, even when the highest improvements are observed. We also compare an optimal policy obtained deploying our approach with a previously proposed heuristic prediction scheme, showing that plenty of room for technological innovation exists.

A method for flexibly allocating and reallocating optical access network (OAN) resources, including fibers and equipment, using the constraint satisfaction problem (CSP) is described. OAN resource allocation during service delivery provisioning involves various input conditions and allocation sequences, so an OAN resource allocation method has to support various workflow patterns. Furthermore, exception processing, such as reallocating OAN resources once they are allocated, is inevitable, especially during the spread of service using optical fiber and during the deployment of an optical access network. However, it is almost impossible to describe all workflow patterns including exception processes. Improving the efficiency of these exception processes, as well as that of the typical processes, is important for reducing the service delivery time. Describing all these patterns and process flows increases development cost. The CSP can be used to search for solutions without having to fix the process sequence and input conditions beforehand. We have formulated the conditions for OAN resource allocation and reallocation as a CSP. Use of this method makes it possible to handle various allocation workflow patterns including exception processes. Evaluation of the solution search time demonstrated its feasibility.

We propose a robust iterative multiuser receiver for decoding convolutional coded code-division multiple access (CDMA) signals in both Gaussian and non-Gaussian channel noise. The receiver is derived from a modified maximum a-posteriori (MAP) algorithm called the max-log-MAP algorithm for robustness against erroneous channel variance estimation. Furthermore, the effect of destructive outliers arising from impulsive noise is mitigated in the proposed receiver by incorporating the robust Huber penalty function into the multiuser detector. The proposed receiver is shown to perform satisfactorily over Gaussian and non-Gaussian impulsive channels. In every iteration, cumulative improvement in the quality of the a-posteriori probabilities is also demonstrated.

This paper describes a morpheme-based pronunciation model that is especially useful to develop the pronunciation lexicon for Large Vocabulary Continuous Speech Recognition (LVCSR) in Korean. To address pronunciation variation in Korean, we analyze phonological rules based on phonemic contexts together with morphological category and morpheme boundary information. Since the same phoneme sequences can be pronounced in different ways at across morpheme boundary, incorporating morphological environment is required to manipulate pronunciation variation modeling. We implement a rule-based pronunciation variants generator to produce a pronunciation lexicon with context-dependent multiple variants. At the lexical level, we apply an explicit modeling of pronunciation variation to add pronunciation variants at across morphemes as well as within morpheme into the pronunciation lexicon. At the acoustic level, we train the phone models with re-labeled transcriptions through forced alignment using context-dependent pronunciation lexicon. The proposed pronunciation lexicon offers the potential benefit for both training and decoding of a LVCSR system. Subsequently, we perform the speech recognition experiment on read speech task with 34K-morpheme vocabulary. Experiment confirms that improved performance is achieved by pronunciation variation modeling based on morpho-phonological analysis.

Cooperative communication enables single antenna to realize a virtual multiple antenna by sharing their antennas. Therefore, it offers transmit diversity. In this letter, we apply pre-coding scheme to the transmit symbols. Although pre-coding has shortcomings, it is possible to employ Space-Time Block Codes (STBC) with single antenna and achieve high gain.

In this paper, we propose a learning classifier based on maximum entropy (ME) for resolving zero-anaphora in Chinese text. Besides regular grammatical, lexical, positional and semantic features motivated by previous research on anaphora resolution, we develop two innovative Web-based features for extracting additional semantic information from the Web. The values of the two features can be obtained easily by querying the Web using some patterns. Our study shows that our machine learning approach is able to achieve an accuracy comparable to that of state-of-the-art systems. The Web as a knowledge source can be incorporated effectively into the ME learning framework and significantly improves the performance of our approach.

The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.

We developed a comprehensive simulation system for evaluating satellite-based navigation services in highly built-up areas; the system can accommodate Global Positioning System (GPS) multipath effects, as well as line-of-sight (LOS) and dilution of position (DOP) issues. For a more realistic simulation covering multipath and diffracted signal propagations, a 3D-ray tracing method was combined with a satellite orbit model and three-dimensional (3D) geographic information system (GIS) model. An accuracy estimation model based on a 3D position determination algorithm with a theoretical delay-locked loop (DLL) correlation computation could measure the extent to which multipath mitigation improved positioning accuracy in highly built-up areas. This system could even capture the multipath effect from an invisible satellite, one of the greatest factors in accuracy deterioration in highly built-up areas. Further, the simulation results of satellite visibility, DOP, and multipath occurrence were mapped to show the spatial distribution of GPS availability. By using object-oriented programming, our simulation system can be extended to other global navigation satellite systems (GNSSs) simply by adding the orbital information of the corresponding GNSS satellites. We demonstrated the applicability of our simulation system in an experimental simulation for Shinjuku, an area of Tokyo filled with skyscrapers.

Yubazaki et al. have proposed "single input rule modules connected type fuzzy reasoning method" (SIRMs method, for short) whose final output is obtained by summarizing the product of the importance degrees and the inference results from single input fuzzy rule module. Another type of single input type fuzzy reasoning method proposed by Hayashi et al. (we call it "Single Input Connected fuzzy reasoning method" (SIC method, for short) in this paper) uses rule modules to each input item as well as SIRMs method. We expect that inference results of SIRMs method and SIC method have monotonicity if the antecedent parts and consequent parts of fuzzy rules in SIRMs rule modules have monotonicity. However, this paper points out that even if fuzzy rules in SIRMs rule modules have monotonicity, the inference results do not necessarily have monotonicity. Moreover, it clarifies the conditions for the monotonicity of inference results by SIRMs method and SIC method.

We present an effective method of collision recovery for orthogonal frequency division multiplexing (OFDM)-based communications. For the OFDM system, the modulated message data can be demodulated using the partial time-domain OFDM signal. Therefore, the partial time-domain signal can be adopted to reconstruct the whole OFDM time-domain signal with estimated channel information. This property can be utilized to recover packets from the collisions. Since most collisions are cases in which a long packet collides with a short packet, the collided part is assumed to be short. The simulated results show that the method can recover the two collided packets with a certain probability and can be developed to solve the problem of hidden terminals. This method will dramatically benefit the protocol design of wireless networks, including ad hoc and sensor networks.

The large PAPR of orthogonal frequency division multiplexing (OFDM) transmission is one of the serious problems for mobile communications that require severe power saving. Iterative clipping and filtering is an effective method for the PAPR reduction of OFDM signals. This paper evaluates PAPR reduction effect with a graded band-limiting filter in the iterative clipping and filtering method. The evaluation result by computer simulation shows that the excellent peak reduction effect can be obtained in the fewer iteration numbers by using a roll-off filter instead of the conventional rectangular filter, and the iteration number with the roll-off filter achieving the same PAPR is fewer by twice. The result confirms that the clipping and filtering method by using a graded band-limiting filter can achieve low peak OFDM transmission with less computational complexity.

This paper presents an inversion algorithm for dynamic Bayesian networks towards robust speech recognition, namely DBNI, which is a generalization of hidden Markov model inversion (HMMI). As a dual procedure of expectation maximization (EM)-based model reestimation, DBNI finds the 'uncontaminated' speech by moving the input noisy speech to the Gaussian means under the maximum likelihood (ML) sense given the DBN models trained on clean speech. This algorithm can provide both the expressive advantage from DBN and the noise-removal feature from model inversion. Experiments on the Aurora 2.0 database show that the hidden feature model (a typical DBN for speech recognition) with the DBNI algorithm achieves superior performance in terms of word error rate reduction.

The objective of this paper is to develop the theoretical foundation to the pilot-assisted channel estimation using delay-time domain windowing for the coherent detection of OFDM signals. The pilot-assisted channel estimation using delay-time domain windowing is jointly used with polynomial interpolation, decision feedback and Wiener filter. A closed-form BER expression is derived. The impacts of the delay-time domain window width, multipath channel decay factor, the maximum Doppler frequency are discussed. The theoretical analysis is confirmed by computer simulation.

This paper investigates the use of the affine transformation matrix when employing principal component analysis (PCA) to compress the data of 3D animation models. Satisfactory results were achieved for the common 3D models by using PCA because it can simplify several related variables to a few independent main factors, in addition to making the animation identical to the original by using linear combinations. The selection of the principal component factor (also known as the base) is still a subject for further research. Selecting a large number of bases could improve the precision of the animation and reduce distortion for a large data volume. Hence, a formula is required for base selection. This study develops an automatic PCA selection method, which includes the selection of suitable bases and a PCA separately on the three axes to select the number of suitable bases for each axis. PCA is more suitable for animation models for apparent stationary movement. If the original animation model is integrated with transformation movements such as translation, rotation, and scaling (RTS), the resulting animation model will have a greater distortion in the case of the same base vector with regard to apparent stationary movement. This paper is the first to extract the model movement characteristics using the affine transformation matrix and then to compress 3D animation using PCA. The affine transformation matrix can record the changes in the geometric transformation by using 44 matrices. The transformed model can eliminate the influences of geometric transformations with the animation model normalized to a limited space. Subsequently, by using PCA, the most suitable base vector (variance) can be selected more precisely.