This paper proposes a novel metamaterial structure, which equivalently indicates negative permittivity, for the purpose of applying it to a near-field imaging and/or diagnostics of electromagnetic properties by using a surface plasmon in microwave frequency range. The proposed structure consists of a conducting wire lattice with conducting spheres embedded at the mid-point of the wire. It is shown that a spatial dispersion of the wire lattice can be reduced significantly by the sphere. It is also shown that this structure can successfully be applied to an excitation of the surface plasmon in the microwave frequency range by adequately cutting into a thin slab.

This paper presents latency and power modeling of an on-chip bus at the early stage of SoC design. The latency model is to estimate a bus throughput associated with bus configuration and behavioral model before the system-level modeling for a target SoC is established. The power model roughly calculates the power consumption of an on-chip bus including the power consumed by bus wire and bus logics. Thus, the bus architecture is determined by the trade-off between the bus throughput and power estimation obtained from the proposed bus model. We evaluate the target SoCs such as an MPEG player and a portable multimedia player so as to compare the estimated throughput from the proposed bus model to the result performed by a commercial system-level co-simulation framework. As the simulation results, the latency and power consumption of the proposed model shows 14% and 8% differences compared with the result from the validated commercial co-simulation tool.

Kernel logistic regression (KLR) is a powerful and flexible classification algorithm, which possesses an ability to provide the confidence of class prediction. However, its training--typically carried out by (quasi-)Newton methods--is rather time-consuming. In this paper, we propose an alternative probabilistic classification algorithm called Least-Squares Probabilistic Classifier (LSPC). KLR models the class-posterior probability by the log-linear combination of kernel functions and its parameters are learned by (regularized) maximum likelihood. In contrast, LSPC employs the linear combination of kernel functions and its parameters are learned by regularized least-squares fitting of the true class-posterior probability. Thanks to this linear regularized least-squares formulation, the solution of LSPC can be computed analytically just by solving a regularized system of linear equations in a class-wise manner. Thus LSPC is computationally very efficient and numerically stable. Through experiments, we show that the computation time of LSPC is faster than that of KLR by two orders of magnitude, with comparable classification accuracy.

Various contrast enhancement methods such as histogram equalization (HE) and local contrast enhancement (LCE) have been developed to increase the visibility and details of a degraded image. We propose an image contrast enhancement method based on the global and local adjustment of gray levels by combining HE with LCE methods. For the optimal combination of both, we introduce a discrete entropy. Evaluation of our experimental results shows that the proposed method outperforms both the HE and LCE methods.

An upper bound is established for certain exponential sums on the rational points of an elliptic curve over a residue class ring ZN , N=pq for two distinct odd primes p and q. The result is a generalization of an estimate of exponential sums on rational point groups of elliptic curves over finite fields. The bound is applied to showing the pseudorandomness of a large family of binary sequences constructed by using elliptic curves over ZN .

We derive the average achievable rate of an adaptive wireless multicast method with antenna diversity in Nakagami fading channels when the rate is selected by the minimum signal-to-noise ratio (SNR) of the multicast group. Based on the limiting distribution of the minimum SNR, we then derive an approximation to the average achievable rate, which provides accurate values easily in a wide range of channel parameters.

This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.

A compact antenna is proposed for operating at the Federal Communications Commission allocated ultra-wideband (UWB) of 3.1-10.6 GHz. The antenna is made by deforming a film antenna which consists of two glass-shaped and square-shaped radiation elements. The antenna in its planar form is optimized for the UWB operation and is deformed by different manners such as folding, meandering or twisting, without much influence on its input characteristics. The deformations not only miniaturize the antenna but also improve its radiation characteristics. A prototype with a dimension of 2033 mm2 is fabricated and then the antenna is deformed by rolling it into a circular rod with a diameter of 6.4 mm, or meandering it into a square rod with a cross-sectional dimension of 65 mm2. The deformed antennas maintain the operation at the UWB and have better omni-directional radiation patterns than the antenna in its planar form.

For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.

A hot clutter mitigation algorithm based on Subbanding and Space Fast-time Adaptive Processing (Fast-time STAP) for Multi-channel Synthetic Aperture Radar (MSAR) is analyzed, and is compared with the method based on just fast-time STAP. Simulation results demonstrate that the method based on subbanding and fast-time STAP performs better than the method based on just fast-time STAP in hot clutter mitigation for MSAR.

This paper presents a combined feature extraction method to improve the performance of bag-of-features image classification. We apply 10 relevant operations to global/local statistics of visual words. Because the pairwise combination of visual words is large, we apply feature selection methods including fisher discriminant criterion and L1-SVM. The effectiveness of the proposed method is confirmed through the experiment.

In this letter, compact loop resonator type circular polarization (CP) antennas with a square ring and an X-shaped meander loop are presented. Both antennas are fed to a microstrip line with electromagnetic coupling. By adjusting the gap and length of a coupled microstrip line, the magnitude and phase conditions of two orthogonal modes for CP can be determined. The proposed antennas show good axial ratios and also good agreements between experimented and simulated results.

Threshold testing, which is an LSI testing method based on the acceptability of faults, is effective in yield enhancement of LSIs and selective hardening for LSI systems. In this paper, we propose test generation models for threshold test generation. Using the proposed models, we can efficiently identify acceptable faults and generate test patterns for unacceptable faults with a general test generation algorithm, i.e., without a test generation algorithm specialized for threshold testing. Experimental results show that our approach is, in practice, effective.

We present an alternative approach to what we call the "standard optimization", which minimizes a cost function by searching a parameter space. Instead, our approach "projects" in the joint observation space onto the manifold defined by the "consistency constraint", which demands that any minimal subset of observations produce the same result. This approach avoids many difficulties encountered in the standard optimization. As typical examples, we apply it to line fitting and multiview triangulation. The latter produces a new algorithm far more efficient than existing methods. We also discuss the optimality of our approach.

Delay Tolerant Networks (DTNs) are a class of emerging networks that experience frequent and long-duration partitions. Delay is inevitable in DTNs, so ensuring the validity and reliability of the message transmission and making better use of buffer space are more important than concentrating on how to decrease the delay. In this paper, we present a novel routing protocol named Location and Direction Aware Priority Routing (LDPR) for DTNs, which utilizes the location and moving direction of nodes to deliver a message from source to destination. A node can get its location and moving direction information by receiving beacon packets periodically from anchor nodes and referring to received signal strength indicator (RSSI) for the beacon. LDPR contains two schemes named transmission scheme and drop scheme, which take advantage of the nodes' information of the location and moving direction to transmit the message and store the message into buffer space, respectively. Each message, in addition, is branded a certain priority according to the message's attributes (e.g. importance, validity, security and so on). The message priority decides the transmission order when delivering the message and the dropping sequence when the buffer is full. Simulation results show that the proposed LDPR protocol outperforms epidemic routing (EPI) protocol, prioritized epidemic routing (PREP) protocol, and DTN hierarchical routing (DHR) protocol in terms of packet delivery ratio, normalized routing overhead and average end-to-end delay. It is worth noting that LDPR doesn't need infinite buffer size to ensure the packet delivery ratio as in EPI. In particular, even though the buffer size is only 50, the packet delivery ratio of LDPR can still reach 93.9%, which can satisfy general communication demand. We expect LDPR to be of greater value than other existing solutions in highly disconnected and mobile networks.

In this Letter, the maximum likelihood (ML) estimator for the parameters of a real sinusoid in additive white Gaussian noise using irregularly-spaced samples is derived. The ML frequency estimate is first determined by a one-dimensional search, from which optimum amplitude and phase estimates are then computed. It is shown that the estimation performance of the ML method can attain Cramér-Rao lower bound when the signal-to-noise ratio is sufficiently large.

Fuzzy cognitive maps (FCMs) are used to support decision-making, and the decision processes are performed by inference of FCMs. The inference greatly depends on activation functions such as sigmoid function, hyperbolic tangent function, step function, and threshold linear function. However, the sigmoid functions widely used for decision-making processes have been designed by experts. Therefore, we propose a method for designing sigmoid functions through Lyapunov stability analysis. We show the usefulness of the proposed method through the experimental results in inference of FCMs using the designed sigmoid functions.

We address the problem of perceived age estimation from face images, and propose a new semi-supervised approach involving two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster-representative samples for covering as many clusters as possible. This simple sampling strategy allows us to boost the performance of a manifold-based semi-supervised learning method only with a relatively small number of labeled samples. The second contribution is to take the heterogeneous characteristics of human age perception into account. It is rare to misjudge the age of a 5-year-old child as 15 years old, but the age of a 35-year-old person is often misjudged as 45 years old. Thus, magnitude of the error is different depending on subjects' age. We carried out a large-scale questionnaire survey for quantifying human age perception characteristics, and propose to utilize the quantified characteristics in the framework of weighted regression. Consequently, our proposed method is expressed in the form of weighted least-squares with a manifold regularizer, which is scalable to massive datasets. Through real-world age estimation experiments, we demonstrate the usefulness of the proposed method.

The rapid growth of IT technology has enabled ship navigation and automation systems to gain better functionality and safety. However, they generally have their own proprietary structures and networks, which makes interfacing with and remote access to them difficult. In this paper, we propose a total ship service framework that includes a ship area network to integrate separate system networks with heterogeneity and dynamicity, and a ship-shore communication infrastructure to support a remote monitoring and maintenance service using satellite communications. Finally, we present some ship service systems to demonstrate the applicability of the proposed framework.

Estimating the number of signals (NIS) is an important goal in array signal processing, such as direction-of-arrival (DOA) estimation. A common approach for solving this problem is to use an eigenvalue of the array covariance matrix and information criterion, such as the Akaike information criterion (AIC) and minimum description length (MDL). However they suffer serious degradation, when the incoming signals are coherent. To estimate the NIS of the coherent signals impinging on a uniform linear array (ULA), a method for estimating the number of signals without eigendecomposition (MENSE) is proposed. The accuracy of the NIS estimation performance of MENSE is superior to the other algorithms equipped with preprocessing such as the spatial smoothing preprocessing (SSP) and forward/backward spatial smoothing techniques (FBSS) to decorrelate the coherency of signals. Instead of using SSP or FBSS preprocessing, MENSE uses the Hankel correlation matrices. The Hankel correlation matrices can not only decorrelate the coherency of signals but also suppress the influence of noise. However, in severe conditions like low signal-to-noise ratio (SNR) or a closely spaced signals impinging on a ULA, the NIS estimation metric of MENSE has some bias which causes estimation error. In this paper, we pay attention to the multiplicity defined by the ratio of the geometric mean to the arithmetic mean. Accordingly, we propose a new estimation metric that has less bias than that in MENSE. The Computer simulation results show that the proposed method is superior to MENSE in the above severe conditions.