In a multi-user orthogonal frequency division multiplexing (OFDM) system, efficient resource allocation is required to provide service to more users. In this letter, we propose an improved subcarrier allocation algorithm that can increase the spectral efficiency and the number of total transmission bits even if the number of users is too large. The proposed algorithm is divided into two stages. In the first stage, a group of users who are eligible for services is determined by using the bit error rate (BER), the users' minimum data rate requirement, and channel information. In the second stage, subcarriers are first allocated to the users on the basis of channel state, and then the reallocation is performed so that resource waste is minimized. We show that the proposed algorithm outperforms the conventional one on the basis of outage probability, spectral efficiency, and the number of total transmission bits through a computer simulation.

In this paper, we propose an Adaptive Stochastic Collocation Method for block-based Statistical Static Timing Analysis (SSTA). A novel adaptive method is proposed to perform SSTA with delays of gates and interconnects modeled by quadratic polynomials based on Homogeneous Chaos expansion. In order to approximate the key atomic operator MAX in the full random space during timing analysis, the proposed method adaptively chooses the optimal algorithm from a set of stochastic collocation methods by considering different input conditions. Compared with the existing stochastic collocation methods, including the one using dimension reduction technique and the one using Sparse Grid technique, the proposed method has 10x improvements in the accuracy while using the same order of computation time. The proposed algorithm also shows great improvement in accuracy compared with a moment matching method. Compared with the 10,000 Monte Carlo simulations on ISCAS85 benchmark circuits, the results of the proposed method show less than 1% error in the mean and variance, and nearly 100x speeds up.

This work proposes new features to improve the pathological voice quality classification performance. They are the means, the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal, grade 1, grade 2, and grade 3 voices classified in the GRBAS scale. The jitter, the shimmer, the harmonic-to-noise ratio (HNR), and the variance of the short-time energy are utilized as the conventional features. The performances are measured by the classification and regression tree (CART) method. Specifically, the CART-based method by utilizing both the conventional features and the HOS-based ones shows its effectiveness in the pathological voice quality measurement, with the classification accuracy of 87.8%.

It is essential to ensure a satisfactory QoS (Quality of Service) when offering a speech communication system with a noise reduction algorithm. In this paper, we propose a new obejective test methodology for noise-reduced speech that estimates word intelligibility by using a distortion measure. Experimental results confirmed that the proposed methodology gives an accurate estimate with independence of noise reduction algorithms and noise types.

In order to timely response to a user query at run-time, next generation Semantic Web applications demand a robust mechanism to dynamically select one or more existing ontologies available on the Web and combine them automatically if needed. Although existing ontology retrieval systems return a lengthy list of resultant ontologies, they cannot identify which ones can completely meet the query requirements nor determine a minimum set of resultant ontologies that can jointly satisfy the requirements if no single ontology is available to satisfy them. Therefore, this paper presents an ontology retrieval system, namely combiSQORE, which can return single or combinative ontologies that completely satisfy a submitted query when the available ontology database is adequate to answer such query. In addition, the proposed system ranks the returned results based on their semantic similarities to the given query and their modification (integration) costs. The experimental results show that combiSQORE system yields practical combinative ontologies and useful rankings.

Since participants at both end of the communication channel must share common pictogram interpretation to communicate, the pictogram selection task must consider both participants' pictogram interpretations. Pictogram interpretation, however, can be ambiguous. To assist the selection of pictograms more likely to be interpreted as intended, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given category. The proposed measure defines similarity measurement and probability of interpretation words using pictogram interpretations and frequencies gathered from a web survey. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. Five pictogram categories used for categorizing pictogram interpretations are defined based on the five first-level classifications defined in the Concept Dictionary of the EDR Electronic Dictionary. Retrieval performances among not-categorized interpretations, categorized interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized semantic relevance approaches showed more stable performances than the not-categorized approach.

A novel amplification mechanism of traveling TM wave on an electron beam within a waveguide structure is proposed. Under boundary constraint of the waveguide, a hybrid coupling of longitudinal plasma wave and transverse guided one occurs to result in traveling instability. The instability refers to a backward traveling amplification. The new amplification in the waveguide due to the interactive coupling between the space charge mode and the waveguide one is firstly pointed out. The analysis is extended to the relativistic energy range to get a large gain. The features and properties are discussed for a wide frequency range as well as a high gain-bandwidth product.

The complexity of Finite Impulse Response (FIR) filters is mainly dominated by the number of adders (subtractors) used to implement the coefficient multipliers. It is well known that Common Subexpression Elimination (CSE) method based on Canonic Signed Digit (CSD) representation considerably reduces the number of adders in coefficient multipliers. Recently, a binary-based CSE (BSE) technique was proposed, which produced better reduction of adders compared to the CSD-based CSE. In this paper, we propose a new 4-bit binary representation-based CSE (BCSE-4) method which employs 4-bit Common Subexpressions (CSs) for implementing higher order low-power FIR filters. The proposed BCSE-4 offers better reduction of adders by eliminating the redundant 4-bit CSs that exist in the binary representation of filter coefficients. The reduction of adders is achieved with a small increase in critical path length of filter coefficient multipliers. Design examples show that our BCSE-4 gives an average power consumption reduction of 5.2% and 6.1% over the best known CSE method (BSE, NR-SCSE) respectively, when synthesized with TSMC-0.18 µm technology. We show that our BCSE-4 offers an overall adder reduction of 6.5% compared to BSE without any increase in critical path length of filter coefficient multipliers.

In this paper, an efficient node-level target classification scheme in wireless sensor networks (WSNs) is proposed. It uses acoustic and seismic information, and its performance is verified by the classification accuracy of vehicles in a WSN. Because of the hard limitation in resources, parametric classifiers should be more preferable than non-parametric ones in WSN systems. As a parametric classifier, the Gaussian mixture model (GMM) algorithm not only shows good performances to classify targets in WSNs, but it also requires very few resources suitable to a sensor node. In addition, our sensor fusion method uses a decision tree, generated by the classification and regression tree (CART) algorithm, to improve the accuracy, so that the algorithm drives a considerable increase of the classification rate using less resources. Experimental results using a real dataset of WSN show that the proposed scheme shows a 94.10% classification rate and outperforms the k-nearest neighbors and the support vector machine.

Packet delivery in Proxy Mobile IPv6 (PMIPv6) relies on an anchor node called LMA. All packets sent by a source node reach a receiver node via LMA, even though the two nodes attach to the same MAG. In some scenarios, PMIPv6 results in high delivery latency and processing costs due to this unnecessary detour. To address this issue, several PMIPv6 route optimization schemes have been proposed. However, high signaling costs and excessive delays remain when handover is performed. For this reason, we propose an enhanced PMIPv6 route optimization (EPRO) scheme. In addition, we analyze the performance of the EPRO. Analytical results indicate that the EPRO outperforms previous schemes in terms of signaling overhead and handover latency.

An essential condition for diversity reception is that the fading distributions between individual received signals of an antenna array are uncorrelated. In this paper, a new technique to improve the performance of transmission with the correlated Rayleigh-fading signals is proposed. In conventional array systems, individual receivers start sampling the received signals at the same time with the same sampling rate. On the other hand, in the proposed scheme, the received signals are again sampled with the same rate, however the sampling points are shifted in each receiver. Numerical results through computer simulation show that with correlated received signals, by applying the proposed technique the correlation can be reduced to a sufficient level for diversity reception.

In this paper, a new methodology to estimate the number of competing stations in an IEEE 802.11 network, is proposed. Due to the nonlinear nature of the measurement model, an iterative nonlinear filtering algorithm, called the Scaled Unscented Filter (SUF), is employed. The SUF can provide a superior alternative to nonlinear filtering than the conventional Extended Kalman Filter (EKF), since it avoids errors associated with linearization. This approach demonstrates both high accuracy in addition to prompt reactivity to changes in the network occupancy status. In particular, the proposed algorithm shows superior performance in non saturated conditions when compared to the EKF. Numerical results demonstrate that the proposed algorithm provides a more viable method for estimation of the number of competing stations in an IEEE 802.11 network, than estimators based on the EKF.

A system which employs a stylus as an input device is suitable for creative activities like writing and painting. However, such a system does not always provide the user with a GUI that is easy to operate using the stylus. In addition, system usability is diminished because the stylus is not always integrated into the system in a way that takes into consideration the features of a pen. The purpose of our research is to improve the usability of a system which uses a stylus as an input device. We propose shortcut actions, which are interaction techniques for operation with a stylus that are controlled through a user's hand motions made in the air. We developed the Context Sensitive Stylus as a device to implement the shortcut actions. The Context Sensitive Stylus consists of an accelerometer and a conventional stylus. We also developed application programs to which we applied the shortcut actions; e.g., a drawing tool, a scroll supporting tool, and so on. Results from our evaluation of the shortcut actions indicate that users can concentrate better on their work when using the shortcut actions than when using conventional menu operations.

The Letter deals with constant false alarm rate (CFAR) detection of random Gaussian target signals embedded in Gaussian clutter with unknown covariance. The proposed detector is analyzed on the assumption that clutter covariance is not known and a random target signal has low-rank property. The low-dimensional subspace-based approach leads to a robust false alarm rate (RFAR) detector. The detection performance loss and the false alarm stability loss to unknown clutter covariance have been evaluated for example scenario.

A technique that acquires documents in the same category with a given short text is introduced. Regarding the given text as a training document, the system marks up the most similar document, or sufficiently similar documents, from among the document domain (or entire Web). The system then adds the marked documents to the training set to learn the set, and this process is repeated until no more documents are marked. Setting a monotone increasing property to the similarity as it learns enables the system to 1) detect the correct timing so that no more documents remain to be marked and to 2) decide the threshold value that the classifier uses. In addition, under the condition that the normalization process is limited to what term weights are divided by a p-norm of the weights, the linear classifier in which training documents are indexed in a binary manner is the only instance that satisfies the monotone increasing property. The feasibility of the proposed technique was confirmed through an examination of binary similarity and using English and German documents randomly selected from the Web.

We propose a new methodology of DOA (direction of arrival) estimation named SPIRE (Stepwise Phase dIfference REstoration) that is able to estimate sound source directions even if there is more than one source in a reverberant environment. DOA estimation in reverberant environments is difficult because the variance of the direction of an estimated sound source increases in reverberant environments. Therefore, we want the distance between microphones to be long. However, because of the spatial aliasing problem, the distance cannot be longer than half the wavelength of the maximum frequency of a source. DOA estimation performance of SPIRE is not limited by the spatial aliasing problem. The major feature of SPIRE is restoration of the phase difference of a microphone pair (M1) by using the phase difference of another microphone pair (M2) under the condition that the distance between the M1 microphones is longer than the distance between the M2 microphones. This restoration process enables the reduction of the variance of an estimated sound source direction and can alleviates the spatial aliasing problem that occurs with the M1 phase difference using direction estimation of the M2 microphones. The experimental results in a reverberant environment (reverberation time = about 300 ms) indicate that even when there are multiple sources, the proposed method can estimate the source direction more accurately than conventional methods. In addition, DOA estimation performance of SPIRE with the array length 0.2 m is shown to be almost equivalent to that of GCC-PHAT with the array length 0.5 m. SPIRE can executes DOA estimation with a smaller microphone array than GCC-PHAT. From the viewpoint of the hardware size and coherence problem, the array length is required to be as small as possible. This feature of SPIRE is preferable.

In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.

Various types of data from environment are used to provide user-adaptive services. Among them, data of a user's past moving paths are useful to predict a moving user's next location and provide related services. This paper proposes a method to predict a moving user's location through analyzing his/her past moving paths. This method analyzes the user's moving path by using three elements of distance, time and direction of moving paths and Dynamic Time Warping (DTW), and selects the representative path, which is the one most similar to the current moving path in the past paths. The selected path can be used to provide service like space and time estimation.

We propose a feasible V-BLAST detector based on modified Householder QRD (M-H-QRD) over spatially correlated fading channel, which can almost match the performance of the V-BLAST algorithm with much lower complexity and better numerical stability. Compared to the sorted QRD (S-QRD) detector, the proposed detector requires a smaller minimum word-length to reach the same value of error floor for fixed-point (FP) numerical precision despite no significant performance difference for floating-point machine precision. All these advantages make it attractive when implemented using FP arithmetic.

In OFDM receivers, an accurate signal-to-noise ratio (SNR) estimation is desirable for all sorts of operations involved in high-performance baseband demodulation. In this work, conventional SNR estimation techniques are investigated. Next, a blind SNR estimation scheme for the phase-shift keying (PSK) signals based on the coherence function is proposed. The proposed method is non-data-aided (NDA) and hence bandwidth-efficient. Simulations confirm that the proposed method has the best performance from moderate to high SNRs both in AWGN and dispersive channels; however, the proposed method has worse performance when SNRs are low.