The variability in the shape of the Blood Oxygenation Level Dependent (BOLD) response, as measured in Functional Magnetic Resonance Imaging (fMRI), can introduce significant uncertainty and inaccuracies in estimating brain connectivity and detecting brain activity. To address this issue, this paper proposes a fuzzy method that offers enhanced robustness in dealing with the inherent uncertainty associated with the brain's response in fMRI data. The results obtained from simulated data demonstrate that the proposed fuzzy method is capable of effectively handling deviations of the Hemodynamic Response Function from its canonical shape, providing promising potential for improving the accuracy and reliability of fMRI analyses.

Functional magnetic resonance imaging (fMRI) is a key neuroimaging technique. The classic fMRI analysis pipeline is based on the assumption that the hemodynamic response (HR) is the same across brain regions, time, and subjects. Although convenient, there is ample evidence that this assumption does not hold, and that these differences result in inaccuracies in brain activity detection. This article presents a new fMRI processing pipeline that captures the intrinsic intra- and intersubject variability of the HR. At the core of this new pipeline is the definition of a fuzzy hemodynamic response function (HRF). The proposed pipeline includes a new fuzzy general linear model (GLM) able to handle the fuzzy HRF, including a practical realization based on the LR representation of fuzzy numbers. This article also describes how to obtain activation maps from the fuzzy GLM, and a methodology to compute the statistical power of the analysis. The method is evaluated in synthetic and real fMRI data and compared with other state-of-the-art techniques. The experiments based on synthetic data show that the fuzzy GLM approach is more robust under uncertainty regarding the true specific shape of the HR. The experiments based on the real data show an increased volume of the activated brain areas, suggesting that the proposed method is able to prevent false negative errors in the boundaries of target brain regions in which HR should be negligible.

Due to imprecise or incomplete information, ordinary reasoning often only allows us to reach conjectures rather than logical consequences. In previous papers, conjectures from a set of premises were mathematically characterized and classified into logical consequences, hypothesis, and speculations. The goal of this paper is to introduce a numerical value, or weight, providing a measure of up to which extent a conjecture is supported by the premises. Several examples are shown.

RIMEP (Reversible Improved Multi Expression Programming), is a system that has been developed for designing reversible digital circuits. This article discloses a new version of RIMEP called “RIMEP2”. The goal was to evolve reversible circuits in a “fanout free” search space. The major changes that RIMEP has undergone, are made in the structure of the chromosome and in the fitness calculation. Although the changes seem to be minor, the impact is effective. The execution time has been considerably decreased and optimal competitive solutions were found for a set of 30 selected benchmarks, where a quantum cost reduction up to 96.13% was reached with an average of 42.17%.

Reversible logic is an emerging research area and has attracted significant attention in recent years. Developing systematic logic synthesis algorithms for reversible logic is still an area of research. Unlike other areas of application, there are relatively few publications on applications of genetic programming - (evolutionary algorithms in general) - to reversible logic synthesis. In this paper, we are introducing a new method; a variant of IMEP. The case of digital circuits for the even-parity problem is investigated. The type of gate used to evolve such a problem is the Fredkin gate.

Reed-Muller expressions and their various extensions and generalizations for binary and multiple-valued logic functions are an important class of discrete function representations that are often used in practical applications. These expressions can be uniformly viewed as discrete polynomial expressions over finite fields GF(2) and GF(q) or the field of rational numbers in the case of expressions with integer-valued coefficients. The optimization of them in the number of product terms count is performed by selecting either positive or negative literals (polarities) for variables in the functions to be represented. Since there are no ways to select in advance the polarity for variables that will result in most compact expression for a given function, all possible expressions have to be generated and the simplest of them selected. This is a task computationally very demanding, the complexity of which is O(q^n \times C), where C is the time to calculate a particular polarity. Since the reduction of the first factor may lead to missing the most compact expression, the reduction of C is the single option to speed up the procedure. In this paper, we propose an approach to the solution of this problem by exploiting the notion of extended dual polarity, which provides a simple way of ordering polarities to obtain an effective way of finding the optimal one by reducing the time to move between them. The method still implies exhaustive search, but it is an optimized search, which may be expressed in very simple rules resulting in efficient implementation. Experimental results illustrate the effectiveness of the proposed method.

In this paper, we study a single objective extension of support vector machines for multicategory classification. Extending the dual formulation of binary SVMs, the algorithm looks for minimizing the sum of all the pairwise distances among a set of prototypes, each one constrained to one of the convex-hulls enclosing a class of examples. The final discriminant system is built looking for an appropriate reference point in the feature space. The obtained method preserves the form and complexity of the binary case, optimizing just one convex objective function with m variables and 2m+K constraints, where m is the number of examples and K the number of classes. Non-linear extension are straightforward using kernels while soft margin versions can be obtained by using reduced convex hulls. Experimental results in well-known UCI benchmarks are presented, comparing the accuracy and efficiency of the proposed approach with other state-of-the-art methods.

In this paper, we introduce the interior-outer-set model for calculating a fuzzy risk represented by a possibility-probability distribution. The model involving combination calculus is very difficult to follow. In this paper, we transform it into a matrix algorithm. Although the algorithm is still difficult to follow, fortunately, it is easy to make a computer program for realizing. This algorithm consists of MOVING-subalgorithm and INDEX-subalgorithm. The former works out leaving and joining matrices. The latter is a combination algorithm to get index sets. An example is presented showing how a user can calculate a risk of strong earthquake with the algorithm.

Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the genetic algorithm's efficacy. One approach presented for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent from the others. Furthermore, a migration operator produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations of a distributed genetic algorithm by applying genetic algorithms with different configurations, we obtain the so-called heterogeneous distributed genetic algorithms. In this paper, we present a hierarchical model of distributed genetic algorithms in which a higher level distributed genetic algorithm joins different simple distributed genetic algorithms. Furthermore, with the union of the hierarchical structure presented and the idea of the heterogeneous distributed genetic algorithms, we propose a type of heterogeneous hierarchical distributed genetic algorithms, the hierarchical gradual distributed genetic algorithms. Experimental results show that the proposals consistently outperform equivalent sequential genetic algorithms and simple distributed genetic algorithms. ©1999 John Wiley & Sons, Inc.

Off-line testing of large multiprocessor networks or VLSI chips with many outputs requires a large volume of memory for reference data storage. Space compaction combined with time compression of test responses can essentially reduce an overhead required for testing and diagnosis. In this paper, we discuss the problem of optimal design for space compressors (compactors), to minimize the number of observation points for detection of single faulty components in multiprocessor networks. A space compactor is assumed to be followed by a time compressor, to detect a fault not necessarily manifesting itself for a single test pattern. We formulate the rules of design for a space compaction matrix for the topology of the circuit-under-test (CUT) modeled by an arbitrary acyclic graph. Tree arrays and Fourier transform networks are considered as examples. The lower and upper bounds on the number of space compactor outputs are obtained, and optimal space compaction matrices are determined for above mentioned CUT topologies. Simple procedures for design of off-line testing devices with built-in self-testing are presented. Estimations on a complexity of proposed designs are given.>

This correspondence deals with the computation of the Chrestenson spectrum of an n-ary, n-place, p-valued function by means of a systolic system. The design of a systolic system in a multiple-valued environment is discussed in details and some aspects are compared to binary realizations.