In this paper, we propose a new class of two dimensional (2D) M-channel (M-ch) non-separable filter banks (FBs) based on cosine modulated filter banks (CMFBs) via a new diagonally modulation scheme. Until now, many researchers have proposed 2D non-separable CMFBs. Nevertheless, efficient direction-selective CMFBs have not been yet. Thanks to our new modulations with diagonal shifts, proposed CMFBs have several frequency supports including direction-selective ones which cannot be realized by conventional ones. In a simulation, we show design examples of proposed CMFBs and their various directional frequency supports.

Apriori-based mining algorithms enumerate frequent patterns efficiently, but the resulting large number of patterns makes it difficult to directly apply subsequent learning tasks. Recently, efficient iterative methods are proposed for mining discriminative patterns for classification and regression. These methods iteratively execute discriminative pattern mining algorithm and update example weights to emphasize on examples which received large errors in the previous iteration. In this paper, we study a family of loss functions that induces sparsity on example weights. Most of the resulting example weights become zeros, so we can eliminate those examples from discriminative pattern mining, leading to a significant decrease in search space and time. In computational experiments we compare and evaluate various loss functions in terms of the amount of sparsity induced and resulting speed-up obtained.

A selfish routing game is a simple model of selfish behaviors in networks. It is called that Braess's paradox occurs in the selfish routing game if an equilibrium flow achieved by players' selfish behaviors is not the optimal minimum latency flow. In order to make the minimum latency flow a Nash equilibrium, a marginal cost tax has been proposed. Braess graphs have also been proposed to discuss Braess's paradox. In a large population of selfish players, conflicts between purposes of each player and the population causes social dilemmas. In game theory, to resolve the social dilemmas, a capitation tax and/or a subsidy has been introduced, and players' dynamical behaviors have been formulated by replicator dynamics. In this paper, we formulate replicator dynamics in the Braess graphs and investigate stability of the minimum latency flow with and without the marginal cost tax. An additional latency caused by the marginal cost tax is also shown. To resolve the problem of the additional latency, we extend the capitation tax and the subsidy to a state-dependent tax and apply it to the stabilization problem of the minimum latency flow.

Communication among isolated networks (clusters) in delay tolerant networks (DTNs) can be supported by a message ferry, which collects bundles from clusters and delivers them to a sink node. When there are lots of distant static clusters, multiple message ferries and sink nodes will be required. In this paper, we aim to make groups, each of which consists of physically close clusters, a sink node, and a message ferry. Our objective is minimizing the overall mean delivery delay of bundles in consideration of both the offered load of clusters and distances between clusters and their sink nodes. Based on existing work, we first model this problem as a nonlinear integer programming. Using a commercial nonlinear solver, we obtain a quasi-optimal grouping. Through numerical evaluations, we show the fundamental characteristics of grouping, the impact of location limitation of base clusters, and the relationship between delivery delay and the number of base clusters.

Many successful methods for recognizing human action are spatio-temporal interest point (STIP) based methods. Given a test video sequence, for a matching-based method using a voting mechanism, each test STIP casts a vote for each action class based on its mutual information with respect to the respective class, which is measured in terms of class likelihood probability. Therefore, two issues should be addressed to improve the accuracy of action recognition. First, effective STIPs in the training set must be selected as references for accurately estimating probability. Second, discriminative STIPs in the test set must be selected for voting. This work uses ε-nearest neighbors as effective STIPs for estimating the class probability and uses a variance filter for selecting discriminative STIPs. Experimental results verify that the proposed method is more accurate than existing action recognition methods.

Many High-Dynamic-Range (HDR) rendering techniques have been developed. Of these, the image color appearance model, iCAM, is a typical HDR image rendering algorithm. HDR rendering methods normally require a tone compression process and include many color space transformations from the RGB signal of an input image to the RGB signal of output devices for the realistic depiction of a captured image. The iCAM06, which is a refined iCAM, also contains a tone compression step and several color space conversions for HDR image reproduction. On the other hand, the tone compression and frequent color space changes in the iCAM06 cause color distortion, such as a hue shift and saturation reduction of the output image. To solve these problems, this paper proposes a separate color correction method that has no effect on the output luminance values by controlling only the saturation and hue of the color attributes. The color saturation of the output image was compensated for using the compensation gain and the hue shift was corrected using the rotation matrix. The separate color correction method reduces the existing color changes in iCAM06. The compensation gain and rotation matrix for the color correction were formulated based on the relationship between the input and output tristimulus values through the tone compression. The experimental results show that the revised iCAM06 with the proposed method has better performance than the default iCAM06.

As mobile multimedia services expand, user behavior will become more diverse and the control of service quality from the user's perspective will become more important in service design. The quality of the network is one of the critical factors determining mobile service quality. However, this has mainly been evaluated in objective physical terms, such as delay reduction and bandwidth expansion. It is less common to use a human-centered design viewpoint when improving network performance. In this paper, we discuss ways to improve the quality of web services using time-fillers that actively address the human factors to improve the subjective quality of a mobile network. A field experiment was conducted, using a prototype. The results of the field experiment show that time-fillers can significantly decrease user dissatisfaction with waiting, but that this effect is strongly influenced by user preferences concerning content. Based on these results, we discuss the design requirements for effective use of time-fillers.

This paper presents a novel architecture of an asynchronous FPGA for handshake-component-based design. The handshake-component-based design is suitable for large-scale, complex asynchronous circuit because of its understandability. This paper proposes an area-efficient architecture of an FPGA that is suitable for handshake-component-based asynchronous circuit. Moreover, the Four-Phase Dual-Rail encoding is employed to construct circuits robust to delay variation because the data paths are programmable in FPGA. The FPGA based on the proposed architecture is implemented in a 65 nm process. Its evaluation results show that the proposed FPGA can implement handshake components efficiently.

For source camera identification, we propose a method to reconstruct the sensor pattern noise map from a size-reduced query image by minimizing an objective function derived from the observation model. Our method can be applied to multiple queries, and can thus be further improved. Experiments demonstrate the superiority of the proposed method over conventional interpolation-based magnification algorithms.

A specification for digital cinema systems which deal with movies digitally from production to delivery as well as projection on the screens is recommended by DCI (Digital Cinema Initiative), and the systems based on this specification have already been developed and installed in theaters. The parameters of the systems that play an important role in determining image quality include image resolution, quantization bit depth, color space, gamma characteristics, and data compression methods. This paper comparatively discusses a relation between required bit depth and gamma quantization using both of a human visual system for grayscale images and two color difference models for color images. The required bit depth obtained from a contrast sensitivity function against grayscale images monotonically decreases as the gamma value increases, while it has a minimum value when the gamma is 2.9 to 3.0 from both of the CIE 1976 L* a* b* and CIEDE2000 color difference models. It is also shown that the bit depth derived from the contrast sensitivity function is one bit greater than that derived from the color difference models at the gamma value of 2.6. Moreover, a comparison between the color differences computed with the CIE 1976 L* a* b* and CIEDE2000 leads to a same result from the view point of the required bit depth for digital cinema systems.

A method of calculating the top event probability of a fault tree, where dynamic gates and repeated events are included and the occurrences of basic events follow nonexponential distributions, is proposed. The method is on the basis of the Bayesian network formulation for a DFT proposed by Yuge and Yanagi [1]. The formulation had a difficulty in calculating a sequence probability if components have nonexponential failure distributions. We propose an alternative method to obtain the sequence probability in this paper. First, a method in the case of the Erlang distribution is discussed. Then, Tijms's fitting procedure is applied to deal with a general distribution. The procedure gives a mixture of two Erlang distributions as an approximate distribution for a general distribution given the mean and standard deviation. A numerical example shows that our method works well for complex systems.

This paper presents a fine-grain bit-serial reconfigurable VLSI architecture using multiple-valued switch blocks and binary logic modules. Multiple-valued signaling is utilized to implement a compact switch block. A binary-controlled current-steering technique is introduced, utilizing a programmable three-level differential-pair circuit to implement a high-performance low-power arbitrary two-variable binary function, and increase the noise margins in comparison with the quaternary-controlled differential-pair circuit. A current-source sharing technique between a series-gating differential-pair circuit and a current-mode D-latch is proposed to reduce the current source count and improve the speed. It is demonstrated that the power consumption and the delay of the proposed multiple-valued cell based on the binary-controlled current-steering technique and the current-source-sharing technique are reduced to 63% and 72%, respectively, in comparison with those of a previous multiple-valued cell.

Spectral subtraction (SS) is an additive noise removal method which is derived in an extensive framework. In spectral subtraction, it is assumed that speech and noise spectra follow Gaussian distributions and are independent with each other. Hence, noisy speech also follows a Gaussian distribution. Spectral subtraction formula is obtained by maximizing the likelihood of noisy speech distribution with respect to its variance. However, it is well known that noisy speech observed in real situations often follows a heavy-tailed distribution, not a Gaussian distribution. In this paper, we introduce a q-Gaussian distribution in the non-extensive statistics to represent the distribution of noisy speech and derive a new spectral subtraction method based on it. We found that the q-Gaussian distribution fits the noisy speech distribution better than the Gaussian distribution does. Our speech recognition experiments using the Aurora-2 database showed that the proposed method, q-spectral subtraction (q-SS), outperformed the conventional SS method.

This paper proposes a novel high-temperature superconducting dual-band bandpass filter (HTS-DBPF), that employs a broadside coupling structure, in which quarter-wavelength resonators are formed on opposite sides of each substrate. This structure provides a dual-band operation of the BPF and flexibility, in the sense of having a wide range in selecting two center passband frequencies of the HTS-DBPF. This paper employs the ratio of the lower and higher center passband frequencies, α, as a criterion for evaluating the flexibility. The obtained α ranges are from 1 to 4.7, which are the widest for DBPFs for mobile communications applications, to the best knowledge of the authors. This paper presents a 2.4-/2.9-GHz band HTS-DBPF, as an experimental example, using a YBCO film deposited on an MgO substrate. The measured frequency responses of the HTS-DBPF agree with the electromagnetic simulated results. Measurement and simulation results confirm that the proposed filter architecture is effective in configuring a DBPF that can set each center passband frequency widely.

Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.

Semantic concept in an utterance is obtained by a fuzzy matching methods to solve problems such as words' variation induced by automatic speech recognition (ASR), or missing field of key information by users in the process of spoken language understanding (SLU). A two-stage method is proposed: first, we adopt conditional random field (CRF) for building probabilistic models to segment and label entity names from an input sentence. Second, fuzzy matching based on similarity function is conducted between the named entities labeled by a CRF model and the reference characters of a dictionary. The experiments compare the performances in terms of accuracy and processing speed. Dice similarity and cosine similarity based on TF score can achieve better accuracy performance among four similarity measures, which equal to and greater than 93% in F1-measure. Especially the latter one improved by 8.8% and 9% respectively compared to q-gram and improved edit-distance, which are two conventional methods for string fuzzy matching.

A mixed storage-type design using flip-flops and latches (FF/latch-based design) has advantages on such as area and power compared to single storage-type design (only flip-flops or latches). Considering FF/latch-based design at high-level synthesis is necessary, because resource binding process significantly affects the quality of resulting circuits. One of the fundamental aspects in FF/latch-based design is that different resource binding solutions could lead to the different numbers of latch-replacable registers. Therefore, as a first step, this paper addresses a datapath design problem in which resource binding and selecting storage-types of registers are simultaneously optimized for datapath area minimization (i.e., latch replacement maximization). An efficient algorithm based on the compatibility path decomposition and an integer linear programming-based exact approach are presented. Experiments confirm the effectiveness of the proposed approaches.

To provide robust wireless data transmission over fading channels, various schemes which involve the use of relays have been proposed. In some of those schemes, the relay chooses not to forward the received message if its reliability is deemed as too low. Some researchers refer to such schemes as selective decode-and-forward. Our work in this paper falls into such a category. More specifically speaking, the relay in our system is a censorial relay (a relay that performs censorial task). It evaluates the reliability, in terms of log likelihood ratio (LLR), of a received data bit (from the source). If its LLR magnitude is below some preset threshold, then it is censored (i.e. not sent to the destination). When the channel is Rayleigh faded, closed-form bit error rate (BER) expressions for the proposed system are derived for several scenarios. Those scenarios are differentiated by the availability of an energy detector (ED) and the various degrees of knowledge regarding the channel state information (CSI). Aided by those closed-form BER expressions, the system parameters can be efficiently optimized to achieve the minimum BER. Simulation results are observed to closely match theoretical values, as computed by the afore-mentioned closed-form BER expressions. As compared to some existing relay-assisted systems in which censoring is incorporated, the performance of our system is better in terms of BER when the same amount of CSI is exploited.

We have proposed a method of identifying superspreaders by flow sampling and a method of filtering legitimate hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters of φ, the measurement period length, m*, the identification threshold of the flow count m within φ, and H*, the identification probability for hosts with m=m*, remained unsolved. These three parameters seriously impact the ability to identify the spread of infection. Our contributions in this work are two-fold: (1) we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine, and (2) the proposed method can optimize the identification accuracy of worm-infected hosts by maximally using a limited amount of memory resource of monitors.

In order to execute coherent Doppler processing in a high range-rate scenario, whether it is for detection, estimation or imaging, range walk embedded in target return should be compensated first. In case of a bistatic radar geometry where a transmitter, a receiver and a target can be all moving, the extent of range walk depends on their relative positions and velocities. This paper presents a coherent Doppler processing algorithm to achieve target detection and Doppler frequency estimation of a target under a bistatic radar geometry. This algorithm is based on the assumption that a target has constant Doppler frequency during a coherent processing interval (CPI). Thus, we first show under what condition the assumption could be valid. We next develop an algorithm, along with its implementation procedures where the region of range walk, called a window, is manipulated. Finally, the performance of a proposed algorithm is examined through simulations.