10th International Conference, Burgos, Spain, September 23-26, 2009. Proceedings

This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, held in Burgos, Sapin, in September 2009. The 100 revised full papers presented were carefully reviewed and selected from over 200 submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing; data mining and information management; neuro-informatics, bio-informatics, and bio-inspired models; agents and hybrid systems; soft computing techniques in data mining; recent advances on swarm-based computing; intelligent computational techniques in medical image processing; advances on ensemble learning and information fursion; financial and business engineering (modeling and applications); MIR day 2009 - Burgos; and nature inspired models for industrial applications

Advances in pervasive computing

Purpose &ndash; The purpose of this paper is to provide an overview of advances in pervasive computing.Design/methodology/approach &ndash; The paper provides a critical analysis of the literature.Findings &ndash; Tools expected to support these advances are: resource location framework, data management (e.g. replica control) framework, communication paradigms, and smart interaction mechanisms. Also, infrastructures needed to support pervasive computing applications and an information appliance should be easy for anyone to use and the interaction with the device should be intuitive.Originality/value &ndash; The paper shows how everyday devices with embedded processing and connectivity could interconnect as a pervasive network of intelligent devices that cooperatively and autonomously collect, process and transport information, in order to adapt to the associated context and activity<br /

Exploration of NDE Properties of AMB Supported Rotors for Structural Damage Detection

Recent advancements in actuator technology, power electronics, sensors, and signal processing have created a rapid development of smart machine technologies for rotating machinery. Ranging from machine condition monitoring and diagnostics to full active control of machine behavior, the integration of electrical and computer systems has produced significant advances in machine performance and reliability. Magnetic bearings are a typical mechatronics product. The hardware is composed of mechanical components combined with electronic elements such as sensors and power amplifiers and an information processing part, usually in the form of a microprocessor. In addition, an increasingly important part is software, which specifies the coordination of bearing forces to sensed rotor motion and consequently dictates the dynamic properties of the complete system. The inherent ability for sensing, information processing, and actuation gives the magnetic bearing the potential to become a key element in smart and intelligent machines

Exploration of NDE Properties of AMB Supported Rotors for Structural Damage Detection

Recent advancements in actuator technology, power electronics, sensors, and signal processing have created a rapid development of smart machine technologies for rotating machinery. Ranging from machine condition monitoring and diagnostics to full active control of machine behavior, the integration of electrical and computer systems has produced significant advances in machine performance and reliability. Magnetic bearings are a typical mechatronics product. The hardware is composed of mechanical components combined with electronic elements such as sensors and power amplifiers and an information processing part, usually in the form of a microprocessor. In addition, an increasingly important part is software, which specifies the coordination of bearing forces to sensed rotor motion and consequently dictates the dynamic properties of the complete system. The inherent ability for sensing, information processing, and actuation gives the magnetic bearing the potential to become a key element in smart and intelligent machines

The Future of Computing: \u3cem\u3eCyberspace\u3c/em\u3e

This paper traces trends in the technological advances of computer and communication systems and examines the promises of the Information Society: global information sharing; and, intelligent decision-support. The technological developments that will lead to the realization of Cyberspace, an information rich environment in which virtual reality capabilities couple directly to the human senses, is explored in terms of five essential components: information processing requirements; communication networks; computing devices (i.e., platforms); hardware and software user-interfaces; and, the meaningful representation of information. Attention is drawn to the critical role played by information representation in a Cyberspace environment. The author argues that the communication infrastructure must become more than a message passing facility. It must have some understanding of the information it is transmitting. If this fundamental requirement is met then Cyberspace will present human society with an unprecedented potential for leveraging the capabilities of the individual members of society for their own benefit and the collective benefit of mankind

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

A Study on Neural Network Architectures

With the growing emphasis on autonomy, intelligence and an increased amount of information required by businesses, traditional processing technology can only cope through faster hardware with more complex customized software.  The traditional computation techniques of programming were not capable enough to solve “hard” problems like pattern recognition, prediction, compression, optimization, classification and machine learning. In order to solve such problems, an interest towards developing intelligent computation systems became stronger. To develop such intelligent systems, innumerable advances have been made by the researchers. An artificial neural network is a data processing system consisting of a huge number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. These artificial neurons are pigeonholed on the basis of architecture, training or learning method and activation function. The neural network architecture is the arrangement of neurons to form layers and connections scheme formed in between and within the layers. Neural network architectures are broadly classified into feed-forward and feedback architectures that further contain single and multiple layers. The feed-forward networks provide a unidirectional signal flow whereas in the feedback networks the signals can flow in both the directions. These neural network architectures are trained through various learning algorithms for producing most efficient solutions to computation problems. In this paper, we present neural network architectures that play a crucial role in modelling the intelligent systems. Keywords: Artificial Neural Network, feed-forward networks, feedback networks