We propose the multi-domain adaptive learning that enables us to find a point meeting possibly time-varying specifications simultaneously in multiple domains, e.g. space, time, frequency, etc. The novel concept is based on the idea of feasibility splitting -- dealing with feasibility in each individual domain. We show that the adaptive projected subgradient method (Yamada, 2003) realizes the multi-domain adaptive learning by employing (i) a projected gradient operator with respect to a ‘fixed’ proximity function reflecting the time-invariant specifications and (ii) a subgradient projection with respect to ‘time-varying’ objective functions reflecting the time-varying specifications. The resulting algorithm is suitable for real-time implementation, because it requires no more than metric projections onto closed convex sets each of which accommodates the specification in each domain. A convergence analysis and numerical examples are presented.

In this paper, Cellular Neural Networks using genetic algorithm (GA-CNNs) are designed for CMOS image noise reduction. Cellular Neural Networks (CNNs) could be an efficient way to apply to the image processing technique, since CNNs have high-speed parallel signal processing characteristics. Adaptive CNNs structure is designed for the reduction of Photon Shot Noise (PSN) changed according to the average number of photons, and the design of templates for adaptive CNNs is based on the genetic algorithm using real numbers. These templates are optimized to suppress PSN in corrupted images. The simulation results show that the adaptive GA-CNNs more efficiently reduce PSN than do the other noise reduction methods and can be used as a high-quality and low-cost noise reduction filter for PSN. The proposed method is designed for real-time implementation. Therefore, it can be used as a noise reduction filter for many commercial applications. The simulation results also show the feasibility to design the CNNs template for a variety of problems based on the statistical image model.

In this paper, we address the following problems: Given a sequence A of n real numbers, and four parameters I,J,X and Y with I≤ J and X≤ Y, find the longest (or shortest) subsequence of A such that its length is between I and J and its sum is between X and Y. We present an online and an offline algorithm for the problems, both run in O(nlog n) time, which are optimal.

In this paper, we propose a novel stochastic gradient algorithm for efficient adaptive filtering. The basic idea is to sparsify the initial error vector and maximize the benefits from the sparsification under computational constraints. To this end, we formulate the task of algorithm-design as a constrained optimization problem and derive its (non-trivial) closed-form solution. The computational constraints are formed by focusing on the fact that the energy of the sparsified error vector concentrates at the first few components. The numerical examples demonstrate that the proposed algorithm achieves the convergence as fast as the computationally expensive method based on the optimization without the computational constraints.

Evolvable hardware (EHW) is a new research field about the use of Evolutionary Algorithms (EAs) to construct electronic systems. EHW refers in a narrow sense to use evolutionary mechanisms as the algorithmic drivers for system design, while in a general sense to the capability of the hardware system to develop and to improve itself. Genetic Algorithm (GA) is one of typical EAs. We propose optimal circuit design by using GA with parameterized uniform crossover (GApuc) and with fitness function composed of circuit complexity, power, and signal delay. Parameterized uniform crossover is much more likely to distribute its disruptive trials in an unbiased manner over larger portions of the space, then it has more exploratory power than one and two-point crossover, so we have more chances of finding better solutions. Its effectiveness is shown by experiments. From the results, we can see that the best elite fitness, the average value of fitness of the correct circuits and the number of the correct circuits of GApuc are better than that of GA with one-point crossover or two-point crossover. The best case of optimal circuits generated by GApuc is 10.18% and 6.08% better in evaluating value than that by GA with one-point crossover and two-point crossover, respectively.

The Fourier series expansion method is a useful tool to approach the problems of discontinuities in optical waveguides, and it can apply to analyze the Floquet-modes of photonic crystal waveguides. However, it has known that the Floquet-mode calculation with large truncation order is limited because of the roundoff errors. This paper proposes a novel formulation of the Floquet-modes propagating in two-dimensional photonic crystal waveguides formed by circular cylinders. We introduce a periodic boundary condition as same with the conventional method, and the fields are expressed in the Fourier series expansions. The present formulation also introduces the cylindrical-wave expansions and uses the recursive transition-matrix algorithm, which is used to analyze the scattering from cylinder array. This makes us possible to obtain very high accuracy without the use of large truncation order for Fourier series expansion. The presented formulation is validated by numerical experiments.

In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from that of the previous work in that different weights are assigned to each feature of the SMV a novel process. According to the experimental results, the proposed approach is effective for speech/music classification using the SVM.

In 2007, Ding et al. proposed an attractive scheme, which is called the -Invertible Cycles (IC) scheme. IC is one of the most efficient multivariate public-key cryptosystems (MPKC); these schemes would be suitable for using under limited computational resources. In 2008, an efficient attack against IC using Grobner basis algorithms was proposed by Fouque et al. However, they only estimated the complexity of their attack based on their experimental results. On the other hand, Patarin had proposed an efficient attack against some multivariate public-key cryptosystems. We call this attack Patarin's attack. The complexity of Patarin's attack can be estimated by finding relations corresponding to each scheme. In this paper, we propose an another practical attack against the IC encryption/signature scheme. We estimate the complexity of our attack (not experimentally) by adapting Patarin's attack. The attack can be also applied to the IC- scheme. Moreover, we show some experimental results of a practical attack against the IC/IC- schemes. This is the first implementation of both our proposed attack and an attack based on Grobner basis algorithm for the even case, that is, a parameter is even.

This letter proposes the Inference Algorithm through Effective Slot Allocation (ESA-IA). In ESA-IA, the tags which match the prefix of the reader's request-respond in the corresponding slot; the group of tags with an even number of 1's responds in slot 0, while the group with an odd number of 1's responds in slot 1. The proposed algorithm infers '00' and '11' if there are two collided bits in slot 0, while inferring '01' and '10' if there are two collided bits in slot 1. The ESA-IA decreases the time consumption for tag identification by reducing the overall number of queries.

Traditional TCP has a good congestion control strategy that adapts its sending rate in accordance with network congestion. In addition, a fast recovery algorithm can help TCP achieve better throughput by responding to temporary network congestion well. However, if multiple packet losses occur, the time to enter congestion avoidance phase would be delayed due to the long recovery time. Moreover, during the recovery phase, TCP freezes congestion window size until all lost packets are recovered, and this can make recovery time much longer resulting in performance degradation. To mitigate such recovery overhead, we propose Momentary recovery algorithm that recovers packet loss without an extra recovery phase. As other TCP and variants, our algorithm also halves the congestion window size when packet drop is detected, but it performs congestion avoidance phase immediately as if loss recovery is completed. For lost packets, TCP sender transmits them along with normal packets as long as congestion window permits rather than performs fast retransmission. In this manner, we can eliminate recovery overhead efficiently and reach steady state momentarily after network congestion. Finally, we provide a simulation based study on TCP recovery behaviors and confirm that our Momentary recovery algorithm always shows better performance compared with NewReno, SACK, and FACK.

In recent VLSI systems, signal propagation delays are requested to achieve the specifications with very high accuracy. In order to meet the specifications, the routing of a net often needs to be detoured in order to increase the routing delay. A routing method should utilize a routing area with obstacles as much as possible in order to realize the specifications of nets simultaneously. In this paper, a fast longer path algorithm that generates a path of a net in routing grid so that the length is increased as much as possible is proposed. In the proposed algorithm, an upper bound for the length in which the structure of a routing area is taken into account is used. Experiments show that our algorithm utilizes a routing area with obstacles efficiently.

In this paper we give a simple algorithm to compute a canonical code for fullerene graphs. Our algorithm runs in O(n) time, while the best known algorithm runs in O(n2) time. Our algorithm is simple. One can generalize the algorithm to compute a canonical code for the skeleton of a convex polyhedron with n vertices. The algorithm runs in O(n2) time.

In this paper an algebraic trellis vector quantization (ATVQ) that introduces algebraic codebooks into trellis coded vector quantization (TCVQ) structure is presented. Low encoding complexity and minimum memory storage requirements are achieved using the proposed approach. It exploits advantages of both the TCVQ and the algebraic codebooks to know the delayed decision, the codebook widening, the low computational complexity and the no storage of codebook. This novel vector quantization scheme is used to encode the wideband speech line spectral frequencies (LSF) parameters. Experimental results on wideband speech have shown that ATVQ yields the same performance as the traditional split vector quantization (SVQ) and the TCVQ in terms of spectral distortion (SD). It can achieve a transparent quality at 47 bits/frame with a considerable reduction of memory storage and computation complexity when compared to SVQ and TCVQ.

This paper proposes a micro-code based Programmable Memory BIST (PMBIST) architecture that can support various kinds of test algorithms. The proposed Non-linear PMBIST (NPMBIST) guarantees high flexibility and high fault coverage using not only March algorithms but also non-linear algorithms such as Walking and Galloping. This NPMBIST has an optimized hardware overhead, since algorithms can be implemented with the minimum bits by the optimized instructions. Finally, various and complex algorithms can be run thanks to its support of multi-loop.

We study the online file allocation problem on ring networks. In this paper, we present a 7-competitive randomized algorithm against an adaptive online adversary on uniform cactus graphs. The algorithm is deterministic if the file size is 1. Moreover, we obtain lower bounds of 4.25 and 3.833 for a deterministic algorithm and a randomized algorithm against an adaptive online adversary, respectively, on ring networks.

This paper presents algorithms for searching text regions with specifying annotated information in tag-annotated text by using Region Algebra. The original algebra and its efficient algorithms are extended to handle both nested regions and crossed regions. The extensions are necessary for text search by using rich linguistic annotations. We first assign a depth number to every nested tag region to order these regions and write efficient algorithms using the depth number for the containment operations which can treat nested tag regions. Next, we introduce variables for attribute values of tags into the algebra to treat annotations in which attributes indicate another tag regions, and propose an efficient method of treating re-entrancy by incrementally determining values for variables. Our algorithms have been implemented in a text search engine for MEDLINE, which is a large textbase of abstracts in medical science. Experiments in tag-annotated MEDLINE abstracts demonstrate the effectiveness of specifying annotations and the efficiency of our algorithms. The system is made publicly accessible at http://www-tsujii.is.s.u-tokyo.ac.jp/medie/.

This paper proposes new techniques to simulate a MIMO propagation channel using the ray-tracing method for the purpose of decreasing the computational complexity. These techniques simulate a MIMO propagation channel by substituting the propagation path between a particular combination of transmitter and receiver antennas for all combinations of transmitter and receiver antennas. The estimation accuracy calculated using the proposed techniques is evaluated based on comparison to the results calculated using imaging algorithms. The results show that the proposed techniques simulate a MIMO propagation channel with low computational complexity, and a high level of estimation accuracy is achieved using the proposed Vector-Rotation Approximation technique compared to that for the imaging algorithm.

Factorization of Hadamard matrices can provide fast algorithm and facilitate efficient hardware realization. In this letter, constructions of factorizable multilevel Hadamard matrices, which can be considered as special case of unitary matrices, are inverstigated. In particular, a class of ternary Hadamard matrices, together with its application, is presented.

This paper presents an extended Relief-F algorithm for nominal attribute estimation, for application to small-document classification. Relief algorithms are general and successful instance-based feature-filtering algorithms for data classification and regression. Many improved Relief algorithms have been introduced as solutions to problems of redundancy and irrelevant noisy features and to the limitations of the algorithms for multiclass datasets. However, these algorithms have only rarely been applied to text classification, because the numerous features in multiclass datasets lead to great time complexity. Therefore, in considering their application to text feature filtering and classification, we presented an extended Relief-F algorithm for numerical attribute estimation (E-Relief-F) in 2007. However, we found limitations and some problems with it. Therefore, in this paper, we introduce additional problems with Relief algorithms for text feature filtering, including the negative influence of computation similarities and weights caused by a small number of features in an instance, the absence of nearest hits and misses for some instances, and great time complexity. We then suggest a new extended Relief-F algorithm for nominal attribute estimation (E-Relief-Fd) to solve these problems, and we apply it to small text-document classification. We used the algorithm in experiments to estimate feature quality for various datasets, its application to classification, and its performance in comparison with existing Relief algorithms. The experimental results show that the new E-Relief-Fd algorithm offers better performance than previous Relief algorithms, including E-Relief-F.

The algebra of communicating shared resources (ACSR) is a timed process algebra which extends classical process algebras with the notion of a resource. In analyzing ACSR models, the existing techniques such as bisimulation checking and Hennessy-Milner Logic (HML) model checking are very important in theory of ACSR, but they are difficult to use for large complex system models in practice. In this paper, we suggest a framework to verify ACSR models against their requirements described in an expressive timed temporal logic. We demonstrate the usefulness of our approach with a real world case study.