Array antenna technology for wireless systems is highly integrated for demands such as multi-functionality and high-performance. This paper details recent technologies in Japan in design techniques based on computational electromagnetics, antenna hardware techniques in the millimeter-wave band, array signal processing to add adaptive functions, and measurement methods to support design techniques, for array antennas for future wireless systems. Prospects of these four technologies are also described.

Asymmetric bilinear maps using Type-3 pairings are known to be advantageous in several points (e.g., the speed and the size of a group element) to symmetric bilinear maps using Type-1 pairings. Kremer and Mazaré introduce a symbolic model to analyze protocols based on bilinear maps, and show that the symbolic model is computationally sound. However, their model only covers symmetric bilinear maps. In this paper, we propose a new symbolic model to capture asymmetric bilinear maps. Our model allows us to analyze security of various protocols based on asymmetric bilinear maps (e.g., Joux's tripartite key exchange, and Scott's client-server ID-based key exchange). Also, we show computational soundness of our symbolic model under the decisional bilinear Diffie-Hellman assumption.

A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.

This paper presents FIR digital filters based on stochastic/binary hybrid computation with reduced hardware complexity and high computational accuracy. Recently, some attempts have been made to apply stochastic computation to realization of digital filters. Such realization methods lead to significant reduction of hardware complexity over the conventional filter realizations based on binary computation. However, the stochastic digital filters suffer from lower computational accuracy than the digital filters based on binary computation because of the random error fluctuations that are generated in stochastic bit streams, stochastic multipliers, and stochastic adders. This becomes a serious problem in the case of FIR filter realizations compared with the IIR counterparts because FIR filters usually require larger number of multiplications and additions than IIR filters. To improve the computational accuracy, this paper presents a stochastic/binary hybrid realization, where multipliers are realized using stochastic computation but adders are realized using binary computation. In addition, a coefficient-scaling technique is proposed to further improve the computational accuracy of stochastic FIR filters. Furthermore, the transposed structure is applied to the FIR filter realization, leading to reduction of hardware complexity. Evaluation results demonstrate that our method achieves at most 40dB improvement in minimum stopband attenuation compared with the conventional pure stochastic design.

During the development of a human embryo, the position of eyes moves medially and caudally in the viscerocranium. A statistical model of this process can play an important role in embryology by facilitating qualitative analyses of change. This paper proposes an algorithm to construct a spatiotemporal statistical model for the eyeballs of a human embryo. The proposed modeling algorithm builds a statistical model of the spatial coordinates of the eyeballs independently for each Carnegie stage (CS) by using principal component analysis (PCA). In the process, a q-Gaussian distribution with a model selection scheme based on the Aaike information criterion is used to handle a non-Gaussian distribution with a small sample size. Subsequently, it seamlessly interpolates the statistical models of neighboring CSs, and we present 10 interpolation methods. We also propose an estimation algorithm for the CS using our spatiotemporal statistical model. A set of images of eyeballs in human embryos from the Kyoto Collection was used to train the model and assess its performance. The modeling results suggested that information geometry-based interpolation under the assumption of a q-Gaussian distribution is the best modeling method. The average error in CS estimation was 0.409. We proposed an algorithm to construct a spatiotemporal statistical model of the eyeballs of a human embryo and tested its performance using the Kyoto Collection.

An efficient means of learning tree-structural features from tree-structured data would enable us to construct effective mining methods for tree-structured data. Here, a pattern representing rich tree-structural features common to tree-structured data and a polynomial time algorithm for learning important tree patterns are necessary for mining knowledge from tree-structured data. As such a tree pattern, we introduce a term tree pattern t such that any edge label of t belongs to a finite alphabet Λ, any internal vertex of t has ordered children and t has a new kind of structured variable, called a height-constrained variable. A height-constrained variable has a pair of integers (i, j) as constraints, and it can be replaced with a tree whose trunk length is at least i and whose height is at most j. This replacement is called height-constrained replacement. A sequence of consecutive height-constrained variables is called a variable-chain. In this paper, we present polynomial time algorithms for solving the membership problem and the minimal language (MINL) problem for term tree patternshaving no variable-chain. The membership problem for term tree patternsis to decide whether or not a given tree can be obtained from a given term tree pattern by applying height-constrained replacements to all height-constrained variables in the term tree pattern. The MINL problem for term tree patternsis to find a term tree pattern t such that the language generated by t is minimal among languages, generated by term tree patterns, which contain all given tree-structured data. Finally, we show that the class, i.e., the set of all term tree patternshaving no variable-chain, is polynomial time inductively inferable from positive data if |Λ| ≥ 2.

Card-based protocols enable us to easily perform cryptographic tasks such as secure multiparty computation using a deck of physical cards. Since the first card-based protocol appeared in 1989, many protocols have been designed. A protocol is usually described with a series of somewhat intuitive and verbal descriptions, such as “turn over this card,” “shuffle these two cards,” “apply a random cut to these five cards,” and so on. On the other hand, a formal computational model of card-based protocols via abstract machine was constructed in 2014. By virtue of the formalization, card-based protocols can be treated more rigorously; for example, it enables one to discuss the lower bounds on the number of cards required for secure computations. In this paper, an overview of the computational model with its applications to designing protocols and a survey of the recent progress in card-based protocols are presented.

Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.

Many researchers studied computationally-secure (verifiable) secret sharing schemes which distribute multiple secrets with a bulletin board. However, the security definition is ambiguous in many of the past articles. In this paper, we first review existing schemes based on formal definitions of indistinguishability of secrets, verifiability of consistency, and cheater-detectability. And then, we propose a new secret sharing scheme which is the first scheme with indistinguishability of secrets, verifiability, and cheater-detectability, and allows to share secrets with arbitrary access structures. Further, our scheme is provably secure under well known computational assumptions.

The key escrow problem and high computational cost are the two major problems that hinder the wider adoption of hierarchical identity-based signature (HIBS) scheme. HIBS schemes with either escrow-free (EF) or online/offline (OO) model have been proved secure in our previous work. However, there is no much EF or OO scheme that has been evaluated experimentally. In this letter, several EF/OO HIBS schemes are considered. We study the algorithmic complexity of the schemes both theoretically and experimentally. Scheme performance and practicability of EF and OO models are discussed.

With being pushed into sub-16nm regime, advanced technology nodes printing in optical micro-lithography relies heavily on aggressive Optical Proximity Correction (OPC) in the foreseeable future. Although acceptable pattern fidelity is utilized under process variations, mask design time and mask manufacturability form crucial parameters whose tackling in the OPC recipe is highly demanded by the industry. In this paper, we propose an intensity based OPC algorithm to find a highly manufacturable mask solution for a target pattern with acceptable pattern fidelity under process variations within a short computation time. This is achieved through utilizing a fast intensity estimation model in which intensity is numerically correlated with local mask density and kernel type to estimate the intensity in a short time and with acceptable estimation accuracy. This estimated intensity is used to guide feature shifting, alignment, and concatenation following linearly interpolated variational intensity error model to achieve high mask manufacturability with preserving acceptable pattern fidelity under process variations. Experimental results show the effectiveness of our proposed algorithm on the public benchmarks.

An information flow problem is a graph-theoretical formalization of the transportation of information over a complicated network. It is known that a linear network code plays an essential role in a certain type of information flow problems, but it is not understood clearly how contributing linear network codes are for other types of information flow problems. One basic problem concerning this aspect is the linear solvability of information flow problems, which is to decide if there is a linear network code that is a solution to the given information flow problem. Lehman et al. characterize the linear solvability of information flow problems in terms of constraints on the sets of source and sink nodes. As an extension of Lehman's investigation, this study introduces a hierarchy constraint of messages, and discusses the computational complexity of the linear solvability of information flow problems with the hierarchy constraints. Nine classes of problems are newly defined, and classified to one of three categories that were discovered by Lehman et al.

This article presents efficient strategies for evacuating from an unknown affected area in a plane. Evacuation is the process of movement away from a threat or hazard such as natural disasters. Consider that one or n(n ≥ 3) agents are lost in an unknown convex region P. The agents know neither the boundary information of P nor their positions. We seek competitive strategies that can evacuate the agent from P as quickly as possible. The performance of the strategy is measured by a competitive ratio of the evacuation path over the shortest path. We give a 13.812-competitive spiral strategy for one agent, and prove that it is optimal among all monotone and periodic strategies by showing a matching lower bound. Also, we give a new competitive strategy EES for n(n ≥ 3) agents and adjust it to be more efficient with the analysis of its performance.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.

FDTD (Finite-Difference Time-Domain) method has been widely used for the analysis of photonic devices consisting of sub-wavelength structures. In recent years, increasing efforts have been made to implement the FDTD on GPGPUs (General-Purpose Graphic Processing Units), to shorten simulation time. On the other hand, it is widely recognized that most of the middle- and low-end GPGPUs have difference of computational performance, between single-precision and double-precision type arithmetics. Therefore the type selection of single/double precision for electromagnetic field variables in FDTD becomes a key issue from the viewpoint of the total simulation performance. In this study we investigated the difference of results between the use of single-precision and double-precision. As a most fundamental sub-wavelength photonic structure, we focused on an alternating multilayer (one dimensional periodic structure). Obtained results indicate that significant difference appears for the amplitudes of higher order spatial harmonic waves.

Some diffusive and recurrence properties of Lorentz Lattice Gas Cellular Automata (LLGCA) have been expensively studied in terms of the densities of some of the left/right static/flipping mirrors/rotators. In this paper, for any combination S of these well known scatters, we study the computational complexity of the following problem which we call PERIODICITY on the S-model: given a finite configuration that distributes only those scatters in S, whether a particle visits the starting position periodically or not. Previously, the flipping mirror model and the occupied flipping rotator model have been shown unbounded, i.e. the process is always diffusive [17]. On the other hand, PERIODICITY is shown PSPACE-complete in the unoccupied flipping rotator model [21]. In this paper, we show that PERIODICITY is PSPACE-compete in any S-model that is neither occupied, unbounded, nor static. Particularly, we prove that PERIODICITY in any unoccupied and bounded model containing flipping mirror is PSPACE-complete.

It is known that, using just a deck of cards, an arbitrary number of parties with private inputs can securely compute the output of any function of their inputs. In 2009, Mizuki and Sone constructed a six-card COPY protocol, a four-card XOR protocol, and a six-card AND protocol, based on a commonly used encoding scheme in which each input bit is encoded using two cards. However, up until now, there are no known results to construct a set of COPY, XOR, and AND protocols based on a two-card-per-bit encoding scheme, which all can be implemented using only four cards. In this paper, we show that it is possible to construct four-card COPY, XOR, and AND protocols using polarizing plates as cards and a corresponding two-card-per-bit encoding scheme. Our protocols use a minimum number of cards in the setting of two-card-per-bit encoding schemes since four cards are always required to encode the inputs. Moreover, we show that it is possible to construct two-card COPY, two-card XOR, and three-card AND protocols based on a one-card-per-bit encoding scheme using a common reference polarizer which is a polarizing material accessible to all parties.

In this paper, we present a faster (wall-clock time) sorting method for numerical data subjected to fully homomorphic encryption (FHE). Owing to circuit-based construction and the FHE security property, most existing sorting methods cannot be applied to encrypted data without significantly compromising efficiency. The proposed algorithm utilizes the cryptographic single-instruction multiple-data (SIMD) operation, which is supported by most existing FHE algorithms, to reduce the computational overhead. We conducted a careful analysis of the number of required recryption operations, which are the computationally dominant operations in FHE. Accordingly, we verified that the proposed SIMD-based sorting algorithm completes the given task more quickly than existing sorting methods if the number of data items and (or) the maximum bit length of each data item exceed specific thresholds.

This paper proposes a fast mode decision technique for intra prediction of High Efficiency Video Coding (HEVC) based on a reliability metric for motion vectors (RMMV). Since such a decision problem can be regarded as a kind of pattern classification, an efficient classifier is required for the reduction of computation complexity. This paper employs the RMMV as a classifier because the RMMV can efficiently categorize image blocks into flat(uniform), active, and edge blocks, and can estimate the direction of an edge block as well. A local search for angular modes is introduced to further speed up the decision process. An experiment shows the advantage of our technique over other techniques.

We consider a problem as follows: Given unit weights arriving in an online manner with the total cardinality unknown, upon each arrival we decide where to place it on the unit circle in $mathbb{R}^{2}$. The objective is to set the center of mass of the placed weights as close to the origin as possible. We apply competitive analysis defining the competitive difference as a performance measure. We first present an optimal strategy for placing unit weights which achieves a competitive difference of $rac{1}{5}$. We next consider a variant in which the destination of each weight must be chosen from a set of positions that equally divide the unit circle. We give a simple strategy whose competitive difference is 0.35. Moreover, in the offline setting, several conditions for the center of mass to lie at the origin are derived.