Predicting network throughput is important for network-aware applications. Network throughput depends on a number of factors, and many throughput prediction methods have been proposed. However, many of these methods are suffering from the fact that a distribution of traffic fluctuation is unclear and the scale and the bandwidth of networks are rapidly increasing. Furthermore, virtual machines are used as platforms in many network research and services fields, and they can affect network measurement. A prediction method that uses pairs of differently sized connections has been proposed. This method, which we call connection pair, features a small probe transfer using the TCP that can be used to predict the throughput of a large data transfer. We focus on measurements, analyses, and modeling for precise prediction results. We first clarified that the actual throughput for the connection pair is non-linearly and monotonically changed with noise. Second, we built a previously proposed predictor using the same training data sets as for our proposed method, and it was unsuitable for considering the above characteristics. We propose a throughput prediction method based on the connection pair that uses ν-support vector regression and the polynomial kernel to deal with prediction models represented as a non-linear and continuous monotonic function. The prediction results of our method compared to those of the previous predictor are more accurate. Moreover, under an unstable network state, the drop in accuracy is also smaller than that of the previous predictor.

We present a privacy preserving method based on inserting dummy data into original data on the data structure called Zero-suppressed BDDs (ZDDs). Our task is distributed itemset mining, which is frequent itemset mining from horizontally partitioned databases stored in distributed places called sites. We focus on the fundamental case in which there are two sites and each site has a database managed by its owner. By dividing the process of distributed itemset mining into the set union and the set intersection, we show how to make the operations secure in the sense of undistinguishability of data, which is our criterion for privacy preserving based on the already proposed criterion, p-indistinguishability. Our method conceals the original data in each operation by inserting dummy data, where ZDDs, BDD-based directed acyclic graphs, are adopted to represent sets of itemsets compactly and to implement the set operations in constructing the distributed itemset mining process. As far as we know, this is the first technique which gives a concrete representation of sets of itemsets and an implementation of set operations for privacy preserving in distributed itemset mining. Our experiments show that the proposed method provides undistinguishability of dummy data. Furthermore, we compare our method with Secure Multiparty Computation (SMC), which is one of the well-known techniques of secure computation.

Bag of visual words is a promising approach to object categorization. However, in this framework, ambiguity exists in patch encoding by visual words, due to information loss caused by vector quantization. In this paper, we propose to incorporate patch-level contextual information into bag of visual words for reducing the ambiguity mentioned above. To achieve this goal, we construct a hierarchical codebook in which visual words in the upper hierarchy contain contextual information of visual words in the lower hierarchy. In the proposed method, from each sample point we extract patches of different scales, all of which are described by the SIFT descriptor. Then, we build the hierarchical codebook in which visual words created from coarse scale patches are put in the upper hierarchy, while visual words created from fine scale patches are put in the lower hierarchy. At the same time, by employing the corresponding relationship among these extracted patches, visual words in different hierarchies are associated with each other. After that, we design a method to assign patch pairs, whose patches are extracted from the same sample point, to the constructed codebook. Furthermore, to utilize image information effectively, we implement the proposed method based on two sets of features which are extracted through different sampling strategies and fuse them using a probabilistic approach. Finally, we evaluate the proposed method on dataset Caltech 101 and dataset Caltech 256. Experimental results demonstrate the effectiveness of the proposed method.

A parameterization of perfect sequences over composition algebras over the real number field is presented. According to the proposed parameterization theorem, a perfect sequence can be represented as a sum of trigonometric functions and points on a unit sphere of the algebra. Because of the non-commutativity of the multiplication, there are two definitions of perfect sequences, but the equivalence of the definitions is easily shown using the theorem. A composition sequence of sequences is introduced. Despite the non-associativity, the proposed theorem reveals that the composition sequence from perfect sequences is perfect.

This study presents an adaptive quantization index modulation scheme applicable on a small audio segment, which in turn allows the watermarking technique to withstand time-shifting and cropping attacks. The exploitation of auditory masking further ensures the robustness and imperceptibility of the embedded watermark. Experimental results confirmed the efficacy of this scheme against common signal processing attacks.

The interference rejection combining (IRC) receiver, which can suppress inter-cell interference, is effective in improving the cell-edge user throughput. The IRC receiver is typically based on the minimum mean square error (MMSE) criteria, and requires a covariance matrix including the interference signals, in addition to a channel matrix from the serving cell. Therefore, in order to clarify the gain from the IRC receiver, the actual estimation error of these matrices should be taken into account. In a system performance evaluation, the link performance modeling of the IRC receiver, i.e., the output signal-to-interference-plus-noise power ratio (SINR) after IRC reception including the estimation errors, is very important in evaluating the actual performance of the IRC receiver in system level simulations. This is because these errors affect the suppression of the interference signals for the IRC receiver. Therefore, this paper investigates and proposes IRC receiver modeling schemes for the covariance matrix and channel estimation errors. As the modeling scheme for the covariance matrix, we propose a scheme that averages the conventional approximation using the complex Wishart distribution in the frequency domain to address issues that arise in a frequency selective fading channel. Furthermore, we propose a modeling scheme for the channel estimation error according to the ideal channel response of all cells and a channel estimation filter to address channel fading fluctuations. The results of simulations assuming the LTE/LTE-Advanced downlink with two transmitter and receiver antenna branches show that the proposed modeling scheme for the covariance matrix estimation error accurately approximates the performance of a realistic IRC receiver, which estimates the covariance matrix and channel matrix of the serving cell based on the demodulation reference signal (DM-RS), even in a frequency selective fading channel. The results also show that the proposed modeling scheme for the channel estimation error is a robust scheme in terms of the r.m.s. delay spread of a channel model compared to the scheme using the mean square error (MSE) statistic of the estimated channel coefficients based on a channel estimation filter.

In this paper, we propose a method for designing genetically optimized Linguistic Models (LM) with the aid of fuzzy granulation. The fundamental idea of LM introduced by Pedrycz is followed and their design framework based on Genetic Algorithm (GA) is enhanced. A LM is designed by the use of information granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules represented as a fuzzy set. However, it is difficult to optimize the number of linguistic contexts, the number of clusters generated by each context, and the weighting exponent. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on GA. Experiments on the coagulant dosing process in a water purification plant reveal that the proposed method shows better performance than the previous works and LM itself.

Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.

In time-varying frequency selective channels, to obtain high-rate joint time-frequency diversity, linear dispersion coded orthogonal frequency division multiplexing (LDC-OFDM), has recently been proposed. Compared with OFDM systems, single-carrier systems may retain the advantages of lower PAPR and lower sensitivity to carrier frequency offset (CFO) effects, which motivates this paper to investigate how to achieve joint frequency and time diversity for high-rate single-carrier block transmission systems. Two systems are proposed: linear dispersion coded cyclic-prefix single-carrier modulation (LDC-CP-SCM) and linear dispersion coded zero-padded single-carrier modulation (LDC-ZP-SCM) across either multiple CP-SCM or ZP-SCM blocks, respectively. LDC-SCM may use a layered two-stage LDC decoding with lower complexity. This paper analyzes the diversity properties of LDC-CP-SCM, and provides a sufficient condition for LDC-CP-SCM to maximize all available joint frequency and time diversity gain and coding gain. This paper shows that LDC-ZP-SCM may be effectively equipped with low-complexity minimum mean-squared error (MMSE) equalizers. A lower complexity scheme, linear transformation coded SCM (LTC-SCM), is also proposed with good diversity performance.

The present paper introduces a new approach to the construction of a sequence set with a zero-correlation zone (ZCZ), which is referred to as a ZCZ sequence set. The proposed sequence construction generates a ZCZ sequence set from a ZCZ sequence set. The proposed method can generate an almost optimal ZCZ sequence set, the member size of which approaches the theoretical bound, when an almost optimal ZCZ sequence is used for the sequence construction. The proposed sequence set consists of NO subsets, where a ZCZ sequence set Z(LO, NO, ZO is used in sequence construction. The correlation function of the sequences of a pair of different subsets, referred to as the inter-subset correlation function, has a ZCZ with a width that is about times that of the correlation function of sequences of the same subset (intra-subset correlation function) for integers Λ ≥ 1, T, and m ≥ 0. Wide inter-subset zero-correlation enables improved performance during application of the proposed sequence set.

In this paper, we propose a decentralized strategy to find out the linear precoding matrices for a two-hop multiuser relay communication system. From a game-theoretic perspective, we model the source allocation process as a strategic noncooperative game for fixing relay precoding matrix and the multiuser interference treating as additive colored noise. Alternately, from the global optimization perspective, we prove that the optimum relay precoding matrix follows the transceiver Winner filter structure for giving a set of source transmit matrices. Closed-form solutions are finally obtained by using our proposed joint iterative SMSE algorithm and numerical results are provided to give insights on the proposed algorithms.

In block transmissions, inter-block interference (IBI) due to delayed waves exceeding a cyclic prefix severely limits the performance. To suppress IBI in downlink MC-CDMA systems, this paper proposes a novel channel shortening method using a time-domain equalizer. The proposed method minimizes a cost function related to equalizer output autocorrelations without the transmission of training symbols. We prove that the method can shorten a channel and suppress IBI completely. Simulation results show that the proposed method can significantly suppress IBI using relatively less number of received blocks than a conventional method when the number of users is moderate.

In this paper, we propose a normalization method dividing the gradient vector by the sum of the diagonal and two adjoining elements of the matrix expressing the correlation between the components of the discrete Fourier transform (DFT) of the reference signal used for the identification of unknown system. The proposed method can thereby improve the estimation speed of coefficients of adaptive filter.

In this letter a circular polarization microstrip antenna with switchable polarization is proposed. The switchable polarization sense characteristic is realized via a shunt connected varactor tuning diode. The appropriate capacitance of the diode at the reverse bias voltage can alter two circular polarizations, as the tuning diode, which is located near the rectangular slot in the circular patch, is utilized in a perturbation element. The switchable polarization is analyzed using the equivalent circuit model representing the resonances of each orthogonal mode. Simulation, calculated, and measured results agree well.

The Multiple-Input Multiple-Output (MIMO) Zero-Forcing (ZF) receiver requires accurate Channel State Information (CSI), which is impacted by channel estimation error, to perform properly. Moreover, interference occurs due to the change of channel coefficients between the channel estimation events in fading channels. Thus, in practice, both channel estimation error and interference greatly influence Bit Error Rate (BER) performance. In this letter, we derive an Signal-to-Interference-and-Noise Ratio (SINR) expression considering both channel estimation error and interference and develop approximate closed-form BER expressions of M-PSK and M-QAM for the MIMO ZF receiver in Nakagami-m fading channels. We then analyze the effects of channel estimation error, interference, and the numbers of transmit and receive antennas.

This paper describes a new method for semi-supervised discriminative language modeling, which is designed to improve the robustness of a discriminative language model (LM) obtained from manually transcribed (labeled) data. The discriminative LM is implemented as a log-linear model, which employs a set of linguistic features derived from word or phoneme sequences. The proposed semi-supervised discriminative modeling is formulated as a multi-objective optimization programming problem (MOP), which consists of two objective functions defined on both labeled lattices and automatic speech recognition (ASR) lattices as unlabeled data. The objectives are coherently designed based on the expected risks that reflect information about word errors for the training data. The model is trained in a discriminative manner and acquired as a solution to the MOP problem. In transcribing Japanese broadcast programs, the proposed method reduced relatively a word error rate by 6.3% compared with that achieved by a conventional trigram LM.

This paper proposes an On-Ground Polarization-Forming (GPF) technique to realize a novel polarization-tracking-free satellite communication system whose communication satellite uses linear polarizations. In this system, mobile terminals use circular polarization to realize polarization-tracking-free and simplified terminal configuration. To output circular polarization from the satellite's horizontal and vertical polarization antennas, those output signals transmitted from the satellite are controlled by the base station using the GPF technique. We fabricate a GPF transmitter to evaluate its polarization forming performance. Measured results show that the proposed technique achieves very high cross-polarization discrimination, more than 27 dB.

We propose a reduced complexity maximum likelihood sequence detection (MLSD) equalizer for wireless communications using bidirectional decision feedback equalizers (DFEs). We apply reduced-length two-level estimates produced by a bidirectional DFE. Therefore, the computationally expensive MLSD algorithm is applied sparingly for two-level signals with the effective channel length shorter than the original channel, regardless of the original constellation size of the symbols. Simulation results show that the proposed algorithm outperforms existing combination schemes based on bidirectional DFEs, especially for large constellations.

In this paper, we examine a new P2P traffic localization approach that exploits peer selection adaptation (i.e., preferring peers who are likely to provide better performance), called Netpherd. Netpherd enables peers to communicate with local domain peers by manipulating networking performance across network domains (i.e., adding an artificial delay to inter-domain traffic). Our feasibility study shows that Netpherd reduces the inter-domain traffic by influencing peer selection adaptation. Netpherd also improves download performance of the peers who know many local domain peers. We discuss one guideline to improve Netpherd based on the feasibility study and verify the guideline with evaluation results.

Microwave imaging techniques, in particular synthetic aperture radar (SAR), are able to obtain useful images even in adverse weather or darkness, which makes them suitable for target position or feature estimation. However, typical SAR imagery is not informative for the operator, because it is synthesized using complex radio signals with greater than 1.0 m wavelength. To deal with the target identification issue for imaging radar, various automatic target recognition (ATR) techniques have been developed. One of the most promising ATR approaches is based on neural network classification. However, in the case of SAR images heavily contaminated by random or speckle noises, the classification accuracy is severely degraded because it only compares the outputs of neurons in the final layer. To overcome this problem, this paper proposes a self organized map (SOM) based ATR method, where the binary SAR image is classified using the unified distance matrix (U-matrix) metric given by the SOM. Our numerical analyses and experiments on 5 types of civilian airplanes, demonstrate that the proposed method remarkably enhances the classification accuracy, particular in lower S/N situations, and holds a significant robustness to the angular variations of the observation.