A meshless method for an inverse two-phase one-dimensional nonlinear Stefan problem

We extend a meshless method of fundamental solutions recently proposed by the authors for the one-dimensional two-phase inverse linear Stefan problem, to the nonlinear case. In this latter situation the free surface is also considered unknown which is more realistic from the practical point of view. Building on the earlier work, the solution is approximated in each phase by a linear combination of fundamental solutions to the heat equation. The implementation and analysis are more complicated in the present situation since one needs to deal with a nonlinear minimization problem to identify the free surface. Furthermore, the inverse problem is ill-posed since small errors in the input measured data can cause large deviations in the desired solution. Therefore, regularization needs to be incorporated in the objective function which is minimized in order to obtain a stable solution. Numerical results are presented and discussed. © 2014 IMACS.

Optimal embedding for watermarking in discrete data spaces

In this paper, we address the problem of robust information embedding in digital data. Such a process is carried out by introducing modifications to the original data that one would like to keep minimal. It assumes that the data, which includes the embedded information, is corrupted before the extraction is carried out. We propose a principled way to tailor an efficient embedding process for given data and noise statistics. © Springer-Verlag Berlin Heidelberg 2005.

Energy aware data gathering based on adaptive modulation scaling in wireless sensor networks

Energy consumption has been a key concern of data gathering in wireless sensor networks. Previous research works show that modulation scaling is an efficient technique to reduce energy consumption. However, such technique will also impact on both packet delivery latency and packet loss, therefore, may result in adverse effects on the qualities of applications. In this paper, we study the problem of modulation scaling and energy-optimization. A mathematical model is proposed to analyze the impact of modulation scaling on the overall energy consumption, end-to-end mean delivery latency and mean packet loss rate. A centralized optimal management mechanism is developed based on the model, which adaptively adjusts the modulation levels to minimize energy consumption while ensuring the QoS for data gathering. Experimental results show that the management mechanism saves significant energy in all the investigated scenarios. Some valuable results are also observed in the experiments. © 2004 IEEE.

Diabetic retinopathy and blockade of the renin–angiotensin system:new data from the DIRECT study programme

The pathogenesis and medical management of diabetic retinopathy is reviewed. The importance of good control of blood glucose and blood pressure remain key elements in the prevention and treatment of diabetic retinopathy, and a number of specific metabolic pathways have been identified that may be useful additional targets for therapeutic intervention. Trial data, however, aimed specifically to answer the questions of optimum medical management are limited, so the DIRECT study of renin-angiotensin blockade using oral candesartan 32 mg daily is a welcome addition to our knowledge. This arose from the promising improvement of retinopathy outcomes in the EUCLID study of lisinopril in type I diabetes. In DIRECT, 5 years of candesartan treatment in type I diabetes reduced the incidence of retinopathy by two or more steps (EDTRS) in severity by 18% (P = 0.0508) and, in a post hoc analysis, reduced the incidence of retinopathy by three-step progression by 35% (P = 0.034). In type I diabetes patients there was no effect on progression of established retinopathy. In contrast, in type II diabetes, 5 years of candesartan treatment resulted in 34% regression of retinopathy (P ≤0.009). Importantly, an overall significant change towards less-severe retinopathy was noted in both type I and II diabetes (P0.03). Although there is still no absolute proof that these effects were specific to RAS blockade, or just an effect of lower blood pressure, it is reasonable to conclude that candesartan has earned a place in the medical management of diabetic retinopathy, to prevent the problem in type I diabetes and to treat the early stages in type II diabetes. © 2010 Macmillan Publishers Limited All rights reserved.

Workflow Modelling in Grid System for Satellite Data Processing

This paper focuses on a problem of Grid system decomposition by developing its object model. Unified Modelling Language (UML) is used as a formalization tool. This approach is motivated by the complexity of the system being analysed and the need for simulation model design.

Digraphs Definition for an Array Maintenance Problem

In this paper we present a data structure which improves the average complexity of the operations of updating and a certain type of retrieving information on an array. The data structure is devised from a particular family of digraphs verifying conditions so that they represent solutions for this problem.

Using the Agglomerative Method of Hierarchical Clustering as a Data Mining Tool in Capital Market

The purpose of this paper is to explain the notion of clustering and a concrete clustering method- agglomerative hierarchical clustering algorithm. It shows how a data mining method like clustering can be applied to the analysis of stocks, traded on the Bulgarian Stock Exchange in order to identify similar temporal behavior of the traded stocks. This problem is solved with the aid of a data mining tool that is called XLMiner™ for Microsoft Excel Office.

Production-frame Model of Representation of Data Domain

One of the most important problems of e-learning system is studied in given paper. This problem is building of data domain model. Data domain model is based on usage of correct organizing knowledge base. In this paper production-frame model is offered, which allows structuring data domain and building flexible and understandable inference system, residing in production system.

Solving a Direct Marketing Problem by Three Types of ARTMAP Neural Networks

An important task for a direct mailing company is to detect potential customers in order to avoid unnecessary and unwanted mailing. This paper describes a non-linear method to predict profiles of potential customers using dARTMAP, ARTMAP-IC, and Fuzzy ARTMAP neural networks. The paper discusses advantages of the proposed approaches over similar techniques based on MLP neural networks.

Eye alignment:a novel approach

This thesis describes the design and development of an eye alignment/tracking system which allows self alignment of the eye’s optical axis with a measurement axis. Eye alignment is an area of research largely over-looked, yet it is a fundamental requirement in the acquisition of clinical data from the eye. New trends in the ophthalmic market, desiring portable hand-held apparatus, and the application of ophthalmic measurements in areas other than vision care have brought eye alignment under new scrutiny. Ophthalmic measurements taken in hand-held devices with out an clinician present requires alignment in an entirely new set of circumstances, requiring a novel solution. In order to solve this problem, the research has drawn upon eye tracking technology to monitor the eye, and a principle of self alignment to perform alignment correction. A handheld device naturally lends itself to the patient performing alignment, thus a technique has been designed to communicate raw eye tracking data to the user in a manner which allows the user to make the necessary corrections. The proposed technique is a novel methodology in which misalignment to the eye’s optical axis can be quantified, corrected and evaluated. The technique uses Purkinje Image tracking to monitor the eye’s movement as well as the orientation of the optical axis. The use of two sets of Purkinje Images allows quantification of the eye’s physical parameters needed for accurate Purkinje Image tracking, negating the need for prior anatomical data. An instrument employing the methodology was subsequently prototyped and validated, allowing a sample group to achieve self alignment of their optical axis with an imaging axis within 16.5-40.8 s, and with a rotational precision of 0.03-0.043°(95% confidence intervals). By encompassing all these factors the technique facilitates self alignment from an unaligned position on the visual axis to an aligned position on the optical axis. The consequence of this is that ophthalmic measurements, specifically pachymetric measurements, can be made in the absence of an optician, allowing the use of ophthalmic instrumentation and measurements in health professions other than vision care.

Data Flow Analysis and the Linear Programming Model

* The research is supported partly by INTAS: 04-77-7173 project, http://www.intas.be

Bottleneck Problem Solution using Biological Models of Attention in High Resolution Tracking Sensors

Every high resolution imaging system suffers from the bottleneck problem. This problem relates to the huge amount of data transmission from the sensor array to a digital signal processing (DSP) and to bottleneck in performance, caused by the requirement to process a large amount of information in parallel. The same problem exists in biological vision systems, where the information, sensed by many millions of receptors should be transmitted and processed in real time. Models, describing the bottleneck problem solutions in biological systems fall in the field of visual attention. This paper presents the bottleneck problem existing in imagers used for real time salient target tracking and proposes a simple solution by employing models of attention, found in biological systems. The bottleneck problem in imaging systems is presented, the existing models of visual attention are discussed and the architecture of the proposed imager is shown.

Conceptual Idea of Identification of Patterns and Problem Solving in the Human’s Memory and the Possibilities to Use it in Artificial Intelligent

Given cybernetic idea is formed on the basis of neurophysiologic, neuropsychological, neurocybernetic data and verisimilar hypotheses, which fill gaps of formers, of the author as well. First of all attention is focused on general principles of a Memory organization in the brain and processes which take part in it that realize such psychical functions as perception and identification of input information about patterns and a problem solving, which is specified by the input and output conditions, as well. Realization of the second function, essentially cogitative, is discussed in the aspects of figurative and lingual thinking on the levels of intuition and understanding. The reasons of advisability and principles of bionic approach to creation of appropriate tools of artificial intelligent are proposed.

The data type of spatial objects

A spatial object consists of data assigned to points in a space. Spatial objects, such as memory states and three dimensional graphical scenes, are diverse and ubiquitous in computing. We develop a general theory of spatial objects by modelling abstract data types of spatial objects as topological algebras of functions. One useful algebra is that of continuous functions, with operations derived from operations on space and data, and equipped with the compact-open topology. Terms are used as abstract syntax for defining spatial objects and conditional equational specifications are used for reasoning. We pose a completeness problem: Given a selection of operations on spatial objects, do the terms approximate all the spatial objects to arbitrary accuracy? We give some general methods for solving the problem and consider their application to spatial objects with real number attributes. © 2011 British Computer Society.

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.