Recently, a new lattice basis reduction notion, called diagonal reduction, was proposed for lattice-reduction-aided detection (LRAD) of multiinput multioutput (MIMO) systems. In this paper, we improve the efficiency of the diagonal reduction algorithm by employing the fast Givens transformations. The technique of the fast Givens is applicable to a family of LLL-type lattice reduction methods to...

When large amounts of data are available, choosing the variables for inclusion in model building can be problematic. In this analysis, a subset of variables was required from a larger set. This subset was to be used in a later cluster analysis with the aim of extracting dimensions of human flourishing. A genetic algorithm (GA), written in SAS®, was used to select the subset of variables from a ...

The TSP (traveling salesman problem) is one of the typical NP-hard problems in combinatorial optimization problem. The fast and effective approximate algorithms are needed to solve the large-scale problem in reasonable computing time. The known approximate algorithm can not give a good enough tour for the larger instance in reasonable time. So an algorithm called multilevel reduction algorithm ...

In this paper, the speech signal is enhanced from the noisy speech signal using the proposed Least Mean Square (LMS) adaptive noise reduction algorithm. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is ...

Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classication accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classication accuracy than ...

Directed graphs are commonly drawn by the Sugiyama algorithm, where crossing reduction is a crucial phase. It is done by repeated one-sided 2-level crossing minimizations, which are still NP-hard. We introduce a global crossing reduction, which at any particular time captures all crossings, especially for long edges. Our approach is based on the sifting technique and improves the level-by-level...

A vast majority of algorithms for the condensation of the reference set requires a great number of computations in case of processing a very large set, one that contains several dozens of objects. This fact formed the grounds for the presented attempt to develop a completely new classifier, an algorithm which would not only maintain the quality of classification similar to one obtained with the...

We present an algorithm for computing the residue R = X mod M . The algorithm is based on a sign estimation technique that estimates the sign of a number represented by a carry-sum pair produced by a carry save adder. Given the (n + k)-bit X and the n-bit M , the modular reduction algorithm computes the n-bit residue R in O(k + log n) time, and is particularly useful when the operand size is la...

The EM algorithm has been widely used in many learning or statistical tasks. However, since it requires multiple database scans, applying the EM algorithm to data streams is not straight forward. In this paper we propose an online EM algorithm which can deal with data streams. The algorithm utilizes a component reduction technique which reduces the number of components in a mixture model. A not...

In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction p...