Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.

Linear dynamical systems are basic state space models literally dealing with underlying system dynamics on the basis of linear state space equations. When the model is employed for time-series data analysis, the system identification, which detects the dimension of hidden state variables, is one of the most important tasks. Recently, it has been found that the model has singularities in the parameter space, which implies that analysis for adverse effects of the singularities is necessary for precise identification. However, the singularities in the models have not been thoroughly studied. There is a previous work, which dealt with the simplest case; the hidden state and the observation variables are both one dimensional. The present paper extends the setting to general dimensions and more rigorously reveals the structure of singularities. The results provide the asymptotic forms of the generalization error and the marginal likelihood, which are often used as criteria for the system identification.

A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.

Filter caches have been studied as an energy efficient solution. They achieve energy savings via selected access to L1 cache, but severely decrease system performance. Therefore, a filter cache system should adopt components that balance execution delay against energy savings. In this letter, we analyze the legacy filter cache system and propose Data Filter Cache with Partial Tag Cache (DFPC) as a new solution. The proposed DFPC scheme reduces energy consumption of L1 data cache and does not impair system performance at all. Simulation results show that DFPC provides the 46.36% energy savings without any performance loss.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

Tracking capsule endoscope location is one of the promising applications offered by implant body area networks (BANs). When tracking the capsule endoscope location, i.e., continuously localize it, it is effective to take the weighted sum of its past locations to its present location, in other words, to low-pass filter its past locations. Furthermore, creating an exact mathematical model of location transition will improve tracking performance. Therefore, in this paper, we investigate two tracking methods with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem. One of the two tracking methods is finite impulse response (FIR) filter-based tracking, which tracks the capsule endoscope location by averaging its past locations. The other one is particle filter-based tracking in order to deal with a nonlinear transition model on the capsule endoscope. However, the particle filter requires that the particle weight is calculated according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated from the received wireless signal. Therefore, for the purpose of applying the particle filter to capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrate that the two tracking methods can improve the performance as compared with the conventional maximum likelihood (ML) localization. Furthermore, we confirm that the particle filter-based tracking outperforms the conventional FIR filter-based tracking by taking the realistic capsule endoscope transition model into consideration.

We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.

This paper derives the balanced realizations of second-order analog filters directly from the transfer function. Second-order analog filters are categorized into the following three cases: complex conjugate poles, distinct real poles, and multiple real poles. For each case, simple formulas are derived for the synthesis of the balanced realizations of second-order analog filters. As a result, we obtain closed form expressions of the balanced realizations of second-order analog filters.

Conventional adaptive notch filter based on an infinite impulse response (IIR) filter is well known. However, this kind of adaptive notch filter has a problem of stability due to its adaptive IIR filter. In addition, tap coefficients of this notch filter converge to solutions with bias error. In order to solve these problems, an adaptive notch filter using Fourier sine series (ANFF) is proposed. The ANFF is stable because an adaptive IIR filter is not used as an all-pass filter. Further, the proposed adaptive notch filter is robust enough to overcome effects of a disturbance signal, due to a structure of the notch filter based on an exponential filter and line symmetry of auto correlation.

In this letter, we present an efficient video matching-based denoising method. Two main issues are addressed in this paper: the matched points and the denoising algorithm based on an adaptive spatial temporal filter. Unlike previous algorithms, our method adaptively selects reference pixels within spatially and temporally neighboring frames. Our method uses more information about matched pixels on neighboring frames than other methods. Therefore, the proposal enhanced the accuracy of video denoising. Simulation results show that the proposed method produces cleaner and sharper images.

The percentage of pedestrian deaths in traffic accidents is on the rise in Japan. In recent years, there have been calls for measures to be introduced to protect vulnerable road users such as pedestrians and cyclists. In this study, a method to detect and track pedestrians using an in-vehicle camera is presented. We improve the technology of detecting pedestrians by using the highly accurate images obtained with a monocular camera. In the detection step, we employ ECoHOG as the feature descriptor; it accumulates the integrated gradient intensities. In the tracking step, we apply an effective motion model using optical flow and the proposed feature descriptor ECoHOG in a tracking-by-detection framework. These techniques were verified using images captured on real roads.

In signal restoration problems, we expect to improve the restoration performance with a priori information about unknown target signals. In this paper, the parametric Wiener filter with linear constraints for unknown target signals is discussed. Since the parametric Wiener filter is usually defined as the minimizer of the criterion not for the unknown target signal but for the filter, it is difficult to impose constraints for the unknown target signal in the criterion. To overcome this difficulty, we introduce a criterion for the parametric Wiener filter defined for the unknown target signal whose minimizer is equivalent to the solution obtained by the original formulation. On the basis of the newly obtained criterion, we derive a closed-form solution for the parametric Wiener filter with linear constraints.

This paper presents a new phase unwrapping algorithm, based on an extended particle filter (EPF) for SAR interferometry. This technique is not limited by the nonlinearity of the model, and is able to accurately unwrap noisy interferograms by applying EPF to simultaneously perform noise suppression and phase unwrapping. Results obtained from synthetic and real data validate the effectiveness of the proposed method.

In this paper, we propose a method for deciding the parameters to satisfy the experiment values, and also checked the effectiveness of this method based on Kramers-Kronig (K.K.) relation. In our proposed method, we are expressed as matrix the Sellmeier's formula, and are solved the simulatenaous equation until the satisfied the experiment value. Numerical results are given for the influence of pulse responses using the medium constants which can be found by proposed method. Also, numerical technique of pulse responses is employed the fast inversion of Laplace transform (FILT).

In this paper, a simple yet efficient texture representation is proposed for texture classification by exploring the joint statistics of local quantized patterns (jsLQP). In order to combine information of different domains, the Gaussian derivative filters are first employed to obtain the multi-scale gradient responses. Then, three feature maps are generated by encoding the local quantized binary and ternary patterns in the image space and the gradient space. Finally, these feature maps are hybridly encoded, and their joint histogram is used as the final texture representation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art LBP based and even learning based methods for texture classification.

In this paper, we propose a rectangular weighting function that can be used in the method of iteratively reweighted least squares (IRWLS) for designing equiripple all-pass IIR filters. The purpose of introducing this weighting function is to improve the convergence performance in the solution of the IRWLS. The height of each rectangle is designed to be equal to the local maximum of each ripple, and the width of each rectangle is designed so that the area of each rectangle becomes equal to the area of each ripple. Here, the ripple is the absolute value of the phase error. We show experimentally that the convergence performance in the solution of the IRWLS can be improved by using the proposed weighting function.

Rule engine technologies have been widely used in the development of enterprise information systems. However, these rule-based systems may suffer the problem of low performance, when there is a large amount of facts data to be matched with the rules. The way of cluster or grid to construct rule engines can flexibly expand system processing capability by increasing cluster scale, and acquire shorter response time. In order to speed up pattern matching in rule engine, a double hash filter approach for alpha network, combined with beta node indexing, is proposed to improve Rete algorithm in this paper. By using fact type node in Rete network, a hash map about ‘fact type - fact type node’ is built in root node, and hash maps about ‘attribute constraint - alpha node’ are constructed in fact type nodes. This kind of double hash mechanism can speed up the filtration of facts in alpha network. Meanwhile, hash tables with the indexes calculated through fact objects, are built in memories of beta nodes, to avoid unnecessary iteration in the join operations of beta nodes. In addition, rule engine based on this improved Rete algorithm is applied in the enterprise information systems. The experimental results show that this method can effectively speed up the pattern matching, and significantly decrease the response time of the application systems.

With the diffusion of web services caused by the appearance of a new architecture known as cloud computing, a large number of websites have been used by attackers as hopping sites to attack other websites and user terminals because many vulnerable websites are constructed and managed by unskilled users. To construct hopping sites, many attackers force victims to download malware by using vulnerabilities in web applications. To protect websites from these malware infection attacks, conventional methods, such as using anti-virus software, filter files from attackers using pattern files generated by analyzing conventional malware files collected by security vendors. In addition, certain anti-virus software uses a behavior blocking approach, which monitors malicious file activities and modifications. These methods can detect malware files that are already known. However, it is difficult to detect malware that is different from known malware. It is also difficult to define malware since legitimate software files can become malicious depending on the situation. We previously proposed an access filtering method based on communication opponents, which are other servers or terminals that connect with our web honeypots, of attacks collected by web honeypots, which collect malware infection attacks to websites by using actual vulnerable web applications. In this blacklist-based method, URLs or IP addresses, which are used in malware infection attacks collected by web honeypots, are listed in a blacklist, and accesses to and from websites are filtered based on the blacklist. To reveal the effects in an actual attack situation on the Internet, we evaluated the detection ratio of anti-virus software, our method, and a composite of both methods. Our evaluation revealed that anti-virus software detected approximately 50% of malware files, our method detected approximately 98% of attacks, and the composite of the two methods could detect approximately 99% of attacks.

This paper describes how to design matching structures to improve the frequency characteristics of one-dimensional finite periodic structures. In particular, it deals with one-dimensional finite superlattices. A downhill simplex method is used to determine some of the structural parameters of the matching structure. Numerical examples show that this method is effective in improving the frequency characteristics of finite superlattices.

This paper proposes a robust bilateral filter which can handle mixed Gaussian and impulsive noise by hybridizing the conventional bilateral filter and the switching median filter. The effectiveness of the proposed method is verified in comparison with other conventional methods by some experiments using the natural digital images.