Codes constructed as subsets of the projective geometry of a vector space over a finite field have recently been shown to have applications as unconditionally secure authentication codes and random network error correcting codes. If the dimension of each codeword is restricted to a fixed integer, the code forms a subset of a finite-field Grassmannian, or equivalently, a subset of the vertices o...

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combin...

The problem of predicting oil prices is worthy of attention. As oil represents the backbone of the world economy, the goal of this paper is to design a model, which is more accurate. We modeled the prediction process comprising of three steps: feature selection, data partitioning and analyzing the prediction models. Six prediction models namely: Multi-Layered Perceptron (MLP), Sequential Minima...

Rank metric codes can either be used as such for error correction in data storage equipments, or be lifted into constant-dimension codes (CDCs) and thus be used for error correction in random network coding. This paper investigates the decoder error probability (DEP) of rank metric codes and CDCs. We first study the DEP of rank metric codes using a bounded rank distance decoder. We derive asymp...

Automatic Target Recognition (ATR) has become a widely researched problem due to its applicability in domains such as surveillance systems. It can be implemented on various types of images captured by different sensors. In this paper a novel framework for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) imagery using Ensemble Classifier is presented. A combination of Princip...

We consider the problem of efficient integration of an n-variate polynomial with respect to the Gaussian measure in Rn and related problems of complex integration and optimization of a polynomial on the unit sphere. We identify a class of n-variate polynomials f for which the integral of any positive integer power fp over the whole space is well-approximated by a properly scaled integral over a...

The purpose of this work is to identify a linear time-invariant dynamic model of wastewater treatment plants with multilevel pseudo random signals as an excitation input. The plants naturally aim to remove suspended substances, organic material and phosphate. An activated sludge process becomes the best technology available to control the discharge of pollutants. For this purpose, state-space m...

In this paper we make an extensive study of different methods for building ensembles of classifiers. We examine variants of ensemble methods that are based on perturbing features. We illustrate the power of using these variants by applying them to a number of different problems. We find that the best performing ensemble is obtained by combining an approach based on random subspace with a cluste...

The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that only a few labels are active in any given datapoint as compared to the total number of labels. However, only a small number of existing work take direct advantage of this inherent extreme sparsity in the label space. By ...

Generation of consistent clusters is always an interesting research issue in the field of knowledge and data engineering. In real applications, different similarity measures and different clustering techniques may be adopted in different clustering spaces. In such a case, it is very difficult or even impossible to define an appropriate similarity measure and clustering criteria in the union spa...