A phase locking value (PLV) in electrocorticography is an essential indicator for analysis of cognitive activities and detection of severe diseases such as seizure of epilepsy. The PLV computation requires a simultaneous pursuit of high-throughput and low-cost implementation in hardware acceleration. The PLV computation consists of bandpass filtering, Hilbert transform, and mean phase coherence (MPC) calculation. The MPC calculation includes trigonometric functions and divisions, and these calculations require a lot of computational amounts. This paper proposes an MPC calculation method that removes high-cost operations from the original MPC with mathematically identical derivations while the conventional methods sacrifice either computational accuracy or throughput. This paper also proposes a hardware implementation of MPC calculator whose latency is 21 cycles and pipeline interval is five cycles. Compared with the conventional implementation with the same standard cell library, the proposed implementation marks 2.8 times better hardware implementation efficiency that is defined as throughput per gate counts.

A cograph (complement reducible graph) is a graph which can be generated by disjoint union and complement operations on graphs, starting with a single vertex graph. Cographs arise in many areas of computer science and are studied extensively. With the goal of developing an effective data mining method for graph structured data, in this paper we introduce a graph pattern expression, called a cograph pattern, which is a special type of cograph having structured variables. Firstly, we show that a problem whether or not a given cograph pattern g matches a given cograph G is NP-complete. From this result, we consider the polynomial time learnability of cograph pattern languages defined by cograph patterns having variables labeled with mutually different labels, called linear cograph patterns. Secondly, we present a polynomial time matching algorithm for linear cograph patterns. Next, we give a polynomial time algorithm for obtaining a minimally generalized linear cograph pattern which explains given positive data. Finally, we show that the class of linear cograph pattern languages is polynomial time inductively inferable from positive data.

We study a reconfiguration problem for Steiner trees in an unweighted graph, which determines whether there exists a sequence of Steiner trees that transforms a given Steiner tree into another one by exchanging a single edge at a time. In this paper, we show that the problem is PSPACE-complete even for split graphs, while solvable in linear time for interval graphs and for cographs.

Given a graph G=(V,E) where V and E are a vertex and an edge set, respectively, specified with a subset VNT of vertices called a non-terminal set, the spanning tree with non-terminal set VNT is a connected and acyclic spanning subgraph of G that contains all the vertices of V where each vertex in a non-terminal set is not a leaf. In the case where each edge has the weight of a nonnegative integer, the problem of finding a minimum spanning tree with a non-terminal set VNT of G was known to be NP-hard. However, the complexity of finding a spanning tree on general graphs where each edge has the weight of one was unknown. In this paper, we consider this problem and first show that it is NP-hard even if each edge has the weight of one on general graphs. We also show that if G is a cograph then finding a spanning tree with a non-terminal set VNT of G is linearly solvable when each edge has the weight of one.

This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.

A non-regular tree T with a prescribed branching sequence (s1,s2,...,sn) is a rooted and ordered tree such that its internal nodes are numbered from 1 to n in preorder and every internal node i in T has si children. Recently, Wu et al. (2010) introduced a concise representation called RD-sequences to represent all non-regular trees and proposed a loopless algorithm for generating all non-regular trees in a Gray-code order. In this paper, based on such a Gray-code order, we present efficient ranking and unranking algorithms of non-regular trees with n internal nodes. Moreover, we show that the ranking algorithm and the unranking algorithm can be run in O(n2) time and O(n2+nSn-1) time, respectively, provided a preprocessing takes O(n2Sn-1) time and space in advance, where .

In this paper, we introduce a concise representation, called right-distance sequences (or RD-sequences for short), to describe all t-ary trees with n internal nodes. A result reveals that there exists a close relationship between the representation and the well-formed sequences suggested by Zaks [Lexicographic generation of ordered trees, Theoretical Computer Science 10 (1980) 63-82]. Using a coding tree and its concomitant tables, a systematical way can help us to investigate the structural representation of t-ary trees. Consequently, we develop efficient algorithms for determining the rank of a given t-ary tree in lexicographic order (i.e., a ranking algorithm), and for converting a positive integer to its corresponding RD-sequence (i.e., an unranking algorithm). Both the ranking and unranking algorithms can be run in O(tn) time and without computing all the entries of the coefficient table.

Manually collecting contexts of a target word and grouping them based on their meanings yields a set of word senses but the task is quite tedious. Towards automated lexicography, this paper proposes a word-sense discrimination method based on two modern techniques; EM algorithm and principal component analysis (PCA). The spherical Gaussian EM algorithm enhanced with PCA for robust initialization is proposed to cluster word senses of a target word automatically. Three variants of the algorithm, namely PCA, sGEM, and PCA-sGEM, are investigated using a gold standard dataset of two polysemous words. The clustering result is evaluated using the measures of purity and entropy as well as a more recent measure called normalized mutual information (NMI). The experimental result indicates that the proposed algorithms gain promising performance with regard to discriminate word senses and the PCA-sGEM outperforms the other two methods to some extent.

In literature, many methods have been presented for enumerating binary trees (full binary trees) and regular k-ary trees, while no one for enumerating arbitrary trees or arbitrary k-ary trees. It is proposed in 1997 using a context-free grammar GBT (GFBT) to code binary trees (full binary trees) for enumerating them. In this paper, we use another grammar GT (GTk) to code arbitrary trees (arbitrary k-ary trees) for enumerating them. The properties of words of Ln(GT) (Ln(GTk)) are discussed in depth, including necessary and sufficient conditions for a word, prefix and suffix of Ln(GT) (Ln(GTk)), and efficient algorithms are given and analyzed for the enumeration of words of Ln(GT) (Ln(GTk)).