JOINT PROGRAM

8^th WSEAS International Conference on
NEURAL NETWORKS
(NN '07)

Vancouver, Canada, June 18-19, 2007

Monday, June 18 2007

PLENARY LECTURE 1

Partitioning Capabilities of Multi-Layer Perceptrons

Assistant Professor Che-Chern Lin

Department of Industrial Technology Education

National Kaohsiung Normal University

Taiwan

Abstract: Recently, multi-layer-structured neural networks have been widely used in many areas. The back-propagation algorithm is one of popular training algorithms where a multi-layered network structure is designed to map inputs to outputs using well-trained weights. To minimize classification errors, it probably takes a lot of computational time in updating weights. Multi-layer perceptrons (MLPs) are different multi-layer-structured neural networks for classifications. This lecture theoretically discusses the partitioning capabilities of MLPs. We first explain how a MLP forms its decision region and how it performs mappings from inputs to desired outputs. A general discussion on the partitioning capabilities of a single-layer perceptron, two-layer perceptron, and three-layer perceptron is also given.  The implementation feasibilities on more complicated decision regions using MLPs are finally covered in this lecture.

PLENARY LECTURE 2

An Extension of the Crisp Ontology for Uncertain Information Modeling – Fuzzy Ontology Map

Associate Professor James Liu

Department of Computing

Hong Kong Polytechnic University

Abstract: In the current World Wide Web (WWW), users find it difficult to locate relevant information using search engines. This may be due to the fact that the current World Wide Web lacks semantic markup. One of the possible solutions for this problem is Semantic Web. In the latest Semantic Web technology, descriptive markup languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), were proposed to model the web content in a machine-readable way which assists information gathering and automatic searching by software agents. Since these ontology markup languages deal with ‘hard’ semantics with the description and manipulation of crisp data, they are not capable of representing uncertain information while using current ontology representation.

This talk presents an extension of the current ontology representation which supports uncertain information modeling. The extension is called Fuzzy Ontology Map (FOM) which is based on the integration of fuzzy theory and graph theory. The FOM is a connection matrix which collects the membership values between classes in the ontology graph. Thus, a fuzzy ontology could be created by using the FOM and the ontology document (RDF/OWL). It is possible to use an FOCM for better knowledge management and information searching. We’ll focus on Web applications to develop systems that can deal with imprecise or vague information. Practical examples will be given and possible extension of the methodology incorporating the use of high level Petri nets will be provided for future work consideration.

PLENARY LECTURE 3

Applications of Meta-heuristics for Combinatorial Optimization Problems

Associate Professor Reza Tavakkoli-Moghaddam

Department of Industrial Engineering, Faculty of Engineering

University of Tehran, Tehran, Iran

Department of Mechanical Engineering

The University of British Columbia

Vancouver, Canada

Abstract: This plenary lecture deals with various applications of meta-heuristics to solve a number of the combinatorial optimization problems (COPs). It is divided into two main sections: (1) meta-heuristics and (2) optimization problems. In the original definition, meta-heuristics are stochastic/approximate solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of the solution space of the combinatorial optimization problems. With the development of complexity theory in the early 70's, it became clear that, since most of COPs were indeed NP-hard problems, there was little hope of ever finding efficient exact solution procedures for them. This realization emphasized the role of heuristics and meta-heuristics for solving such hard problems that were encountered in real-life applications and that needed to be tackled, whether or not they were NP-hard. Each meta-heuristic method balances the exploration and exploitation of the solution space using a specific strategy. The strategy is mainly inspired by the natural phenomena such as evolution, group cooperation, group competition, short/long-term memory, body immune system, self-replication, learning, and DNA/molecular computing or by other sciences such as annealing process, and quantum computations. Each strategy trades off between the effort and time spent to explore the new regions of the solution space and to exploit the explored regions. The solution representation and operator design are most significant aspects of implementing each meta-heuristic method. An important issue is that these two aspects are variety from a problem to another. In general, how to design operators is extremely depended on the structure of the solution representation and neighborhood definition. Thus, it is possible to consider the several ways for implementing a meta-heuristic method for a given problem. The closer the problems to the real-world situations, the harder the implementation of the meta-heuristics will be.

    The focus of this talk is on considering the above-mentioned aspects of the foregoing meta-heuristics by a number of various examples. These typical examples are: the dynamic cell formation problem, flexible flow lines scheduling problem, aggregate production planning problem, resource-constraint project planning problem, and vehicle routing problem. The considered examples are taken from the newly published work by the author.

PLENARY LECTURE 4

Industrial Applications using Artificial Intelligence and Statistical Techniques

Professor Anna Gabriela Perez de Rivas

Universidad de Los Andes

Facultad de Ciencias Economicas y Sociales

Escuela de Estadistica

Merida - Venezuela

Abstract: In this plenary it will be presented some industrial applications using artificial intelligence techniques as: Expert Systems, Neural Networks, Fuzzy Logic, Neo-Fuzzy Systems and Genetic Algorithms that have been used together with Statistical Techniques as: Data Analysis, Cluster Analysis, Time-series, Data Imputation, among others.

These applications include methodologies for designing virtual sensors, fault detection and isolation systems, classifier systems and controller Auto-Tuning systems. Some of these applications have been developed for oil companies.

Also, it will be included some research in neural networks that have been developed using variable structure control- based learning algorithms.

SESSION: Neural Network Algorithms

Chair: Che-Chern Lin, Azlinah Mohamed

Tuesday, June 19 2007

SESSION: Neural Network Applications I

Chair: Azlinah Mohamed, Anna Perez

SESSION: Neural Network Applications II

Chair: Anna Perez, Stergios Papadimitriou

PROGRAM

8^th WSEAS International Conference on
FUZZY SYSTEMS
(FS '07)

Vancouver, Canada, June 18-19, 2007

Monday, June 18 2007

PLENARY LECTURE 1

Partitioning Capabilities of Multi-Layer Perceptrons

Assistant Professor Che-Chern Lin

Department of Industrial Technology Education

National Kaohsiung Normal University

Taiwan

Abstract: Recently, multi-layer-structured neural networks have been widely used in many areas. The back-propagation algorithm is one of popular training algorithms where a multi-layered network structure is designed to map inputs to outputs using well-trained weights. To minimize classification errors, it probably takes a lot of computational time in updating weights. Multi-layer perceptrons (MLPs) are different multi-layer-structured neural networks for classifications. This lecture theoretically discusses the partitioning capabilities of MLPs. We first explain how a MLP forms its decision region and how it performs mappings from inputs to desired outputs. A general discussion on the partitioning capabilities of a single-layer perceptron, two-layer perceptron, and three-layer perceptron is also given.  The implementation feasibilities on more complicated decision regions using MLPs are finally covered in this lecture.

PLENARY LECTURE 2

An Extension of the Crisp Ontology for Uncertain Information Modeling – Fuzzy Ontology Map

Associate Professor James Liu

Department of Computing

Hong Kong Polytechnic University

Abstract: In the current World Wide Web (WWW), users find it difficult to locate relevant information using search engines. This may be due to the fact that the current World Wide Web lacks semantic markup. One of the possible solutions for this problem is Semantic Web. In the latest Semantic Web technology, descriptive markup languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), were proposed to model the web content in a machine-readable way which assists information gathering and automatic searching by software agents. Since these ontology markup languages deal with ‘hard’ semantics with the description and manipulation of crisp data, they are not capable of representing uncertain information while using current ontology representation.

This talk presents an extension of the current ontology representation which supports uncertain information modeling. The extension is called Fuzzy Ontology Map (FOM) which is based on the integration of fuzzy theory and graph theory. The FOM is a connection matrix which collects the membership values between classes in the ontology graph. Thus, a fuzzy ontology could be created by using the FOM and the ontology document (RDF/OWL). It is possible to use an FOCM for better knowledge management and information searching. We’ll focus on Web applications to develop systems that can deal with imprecise or vague information. Practical examples will be given and possible extension of the methodology incorporating the use of high level Petri nets will be provided for future work consideration.

PLENARY LECTURE 3

Applications of Meta-heuristics for Combinatorial Optimization Problems

Associate Professor Reza Tavakkoli-Moghaddam

Department of Industrial Engineering, Faculty of Engineering

University of Tehran, Tehran, Iran

Department of Mechanical Engineering

The University of British Columbia

Vancouver, Canada

Abstract: This plenary lecture deals with various applications of meta-heuristics to solve a number of the combinatorial optimization problems (COPs). It is divided into two main sections: (1) meta-heuristics and (2) optimization problems. In the original definition, meta-heuristics are stochastic/approximate solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of the solution space of the combinatorial optimization problems. With the development of complexity theory in the early 70's, it became clear that, since most of COPs were indeed NP-hard problems, there was little hope of ever finding efficient exact solution procedures for them. This realization emphasized the role of heuristics and meta-heuristics for solving such hard problems that were encountered in real-life applications and that needed to be tackled, whether or not they were NP-hard. Each meta-heuristic method balances the exploration and exploitation of the solution space using a specific strategy. The strategy is mainly inspired by the natural phenomena such as evolution, group cooperation, group competition, short/long-term memory, body immune system, self-replication, learning, and DNA/molecular computing or by other sciences such as annealing process, and quantum computations. Each strategy trades off between the effort and time spent to explore the new regions of the solution space and to exploit the explored regions. The solution representation and operator design are most significant aspects of implementing each meta-heuristic method. An important issue is that these two aspects are variety from a problem to another. In general, how to design operators is extremely depended on the structure of the solution representation and neighborhood definition. Thus, it is possible to consider the several ways for implementing a meta-heuristic method for a given problem. The closer the problems to the real-world situations, the harder the implementation of the meta-heuristics will be.

    The focus of this talk is on considering the above-mentioned aspects of the foregoing meta-heuristics by a number of various examples. These typical examples are: the dynamic cell formation problem, flexible flow lines scheduling problem, aggregate production planning problem, resource-constraint project planning problem, and vehicle routing problem. The considered examples are taken from the newly published work by the author.

PLENARY LECTURE 4

Industrial Applications using Artificial Intelligence and Statistical Techniques

Professor Anna Gabriela Perez de Rivas

Universidad de Los Andes

Facultad de Ciencias Economicas y Sociales

Escuela de Estadistica

Merida - Venezuela

Abstract: In this plenary it will be presented some industrial applications using artificial intelligence techniques as: Expert Systems, Neural Networks, Fuzzy Logic, Neo-Fuzzy Systems and Genetic Algorithms that have been used together with Statistical Techniques as: Data Analysis, Cluster Analysis, Time-series, Data Imputation, among others.

These applications include methodologies for designing virtual sensors, fault detection and isolation systems, classifier systems and controller Auto-Tuning systems. Some of these applications have been developed for oil companies.

Also, it will be included some research in neural networks that have been developed using variable structure control- based learning algorithms.

SESSION: Fuzzy Systems & Applications

Chair: Yuan-Horng Lin, Sen Chi Yu

SESSION: Fuzzy Systems & Applications I

Chair: Mohammadreza Rafiei, Miroslav Pokorny

Tuesday, June 19 2007

SESSION: Fuzzy Control & Dynamical Systems

Chair: Mohammadreza Anvari, Shuzlina Abdul Rahman

SESSION: Fuzzy Applications on the Web

Chair: James N.K. Liu, Gholam Ali Montazer

SESSION: Fuzzy Systems & Applications II

Chair: J. J. Medel Juárez, Reza Tavakkoli-Moghaddam

PROGRAM

8^th WSEAS International Conference on
EVOLUTIONARY COMPUTING
(EC '07)

Vancouver, Canada, June 18-19, 2007

Monday, June 18 2007

PLENARY LECTURE 1

Partitioning Capabilities of Multi-Layer Perceptrons

Assistant Professor Che-Chern Lin

Department of Industrial Technology Education

National Kaohsiung Normal University

Taiwan

Abstract: Recently, multi-layer-structured neural networks have been widely used in many areas. The back-propagation algorithm is one of popular training algorithms where a multi-layered network structure is designed to map inputs to outputs using well-trained weights. To minimize classification errors, it probably takes a lot of computational time in updating weights. Multi-layer perceptrons (MLPs) are different multi-layer-structured neural networks for classifications. This lecture theoretically discusses the partitioning capabilities of MLPs. We first explain how a MLP forms its decision region and how it performs mappings from inputs to desired outputs. A general discussion on the partitioning capabilities of a single-layer perceptron, two-layer perceptron, and three-layer perceptron is also given.  The implementation feasibilities on more complicated decision regions using MLPs are finally covered in this lecture.

PLENARY LECTURE 2

An Extension of the Crisp Ontology for Uncertain Information Modeling – Fuzzy Ontology Map

Associate Professor James Liu

Department of Computing

Hong Kong Polytechnic University

Abstract: In the current World Wide Web (WWW), users find it difficult to locate relevant information using search engines. This may be due to the fact that the current World Wide Web lacks semantic markup. One of the possible solutions for this problem is Semantic Web. In the latest Semantic Web technology, descriptive markup languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), were proposed to model the web content in a machine-readable way which assists information gathering and automatic searching by software agents. Since these ontology markup languages deal with ‘hard’ semantics with the description and manipulation of crisp data, they are not capable of representing uncertain information while using current ontology representation.

This talk presents an extension of the current ontology representation which supports uncertain information modeling. The extension is called Fuzzy Ontology Map (FOM) which is based on the integration of fuzzy theory and graph theory. The FOM is a connection matrix which collects the membership values between classes in the ontology graph. Thus, a fuzzy ontology could be created by using the FOM and the ontology document (RDF/OWL). It is possible to use an FOCM for better knowledge management and information searching. We’ll focus on Web applications to develop systems that can deal with imprecise or vague information. Practical examples will be given and possible extension of the methodology incorporating the use of high level Petri nets will be provided for future work consideration.

PLENARY LECTURE 3

Applications of Meta-heuristics for Combinatorial Optimization Problems

Associate Professor Reza Tavakkoli-Moghaddam

Department of Industrial Engineering, Faculty of Engineering

University of Tehran, Tehran, Iran

Department of Mechanical Engineering

The University of British Columbia

Vancouver, Canada

Abstract: This plenary lecture deals with various applications of meta-heuristics to solve a number of the combinatorial optimization problems (COPs). It is divided into two main sections: (1) meta-heuristics and (2) optimization problems. In the original definition, meta-heuristics are stochastic/approximate solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of the solution space of the combinatorial optimization problems. With the development of complexity theory in the early 70's, it became clear that, since most of COPs were indeed NP-hard problems, there was little hope of ever finding efficient exact solution procedures for them. This realization emphasized the role of heuristics and meta-heuristics for solving such hard problems that were encountered in real-life applications and that needed to be tackled, whether or not they were NP-hard. Each meta-heuristic method balances the exploration and exploitation of the solution space using a specific strategy. The strategy is mainly inspired by the natural phenomena such as evolution, group cooperation, group competition, short/long-term memory, body immune system, self-replication, learning, and DNA/molecular computing or by other sciences such as annealing process, and quantum computations. Each strategy trades off between the effort and time spent to explore the new regions of the solution space and to exploit the explored regions. The solution representation and operator design are most significant aspects of implementing each meta-heuristic method. An important issue is that these two aspects are variety from a problem to another. In general, how to design operators is extremely depended on the structure of the solution representation and neighborhood definition. Thus, it is possible to consider the several ways for implementing a meta-heuristic method for a given problem. The closer the problems to the real-world situations, the harder the implementation of the meta-heuristics will be.

    The focus of this talk is on considering the above-mentioned aspects of the foregoing meta-heuristics by a number of various examples. These typical examples are: the dynamic cell formation problem, flexible flow lines scheduling problem, aggregate production planning problem, resource-constraint project planning problem, and vehicle routing problem. The considered examples are taken from the newly published work by the author.

PLENARY LECTURE 4

Industrial Applications using Artificial Intelligence and Statistical Techniques

Professor Anna Gabriela Perez de Rivas

Universidad de Los Andes

Facultad de Ciencias Economicas y Sociales

Escuela de Estadistica

Merida - Venezuela

Abstract: In this plenary it will be presented some industrial applications using artificial intelligence techniques as: Expert Systems, Neural Networks, Fuzzy Logic, Neo-Fuzzy Systems and Genetic Algorithms that have been used together with Statistical Techniques as: Data Analysis, Cluster Analysis, Time-series, Data Imputation, among others.

These applications include methodologies for designing virtual sensors, fault detection and isolation systems, classifier systems and controller Auto-Tuning systems. Some of these applications have been developed for oil companies.

Also, it will be included some research in neural networks that have been developed using variable structure control- based learning algorithms.

SESSION: Evolutionary Optimization Methods & Applications I

Chair: Qiang Hua, Igor Bernik

Tuesday, June 19 2007

SESSION: Evolutionary Computing Applications I

Chair: Jorge A. Tejedor, Haiyi Zhang

SESSION: Evolutionary Computing Applications II

Chair: Michael Rosenman, Rudolf Freund

PROGRAM

8^th WSEAS International Conference on
ACOUSTICS & MUSIC: THEORY & APPLICATIONS
(AMTA '07)

Vancouver, Canada, June 19-21, 2007

Wednesday, June 20 2007

PLENARY LECTURE 5

3D-Auralization of Music

Assistant Professor Lamberto Tronchin

DIENCA - CIARM

University of Bologna, Italy

Abstract: The definition and measurement of sound spatialisation have been strongly enhanced in last years, as nowadays spatialisation is considered quite important during design of auditoria and virtual audio reproduction of sound quality in dedicated listening rooms for 3D reproduction purposes. Even though international standards like ISO 3382 require measuring some spatial parameters (i.e. LE, LF, IACC), usually only binaural measurements are performed, by means of a dummy head, and rarely 3D impulse responses are measured and utilised for sound reproduction.

In this paper, after an overview on the most common techniques utilised for 3D auralization, an innovative procedure of measuring and reproducing spatial sound characteristics is presented. The application of this new technique in virtual 3D sound reconstruction is presented. Furthermore, the methodology is compared with other techniques of 3D sound reproduction. Moreover, the results of a wide campaign of measurements of spatial parameters among different auditoria all over the world, ranging from Italy to Japan and Australia, and conducted with the novel methodology, are compared with the results of standard binaural and 3D measurements. The possibility to enhance the spatial reproduction of sound quality in real spaces and the comprehensibility of spatial parameters is finally considered and presented in different cases.

PLENARY LECTURE 6

General Problems of Sampling-Reconstruction Procedure of Random Processes with the Limited Number of Samples

Professor Vladimir Kazakov

Department of Telecommunications of the School of
Mechanical and Electrical Engineering

National Polytechnic Institute of Mexico

U. Zacatenco, C.P. 07738

Mexico City, MEXICO

Abstract: Some critical observations are given about Balakrishnan's theorem. We prove that this theorem is only valid for Gaussian processes with the infinite number of samples. The general case of the Sampling-Reconstruction Procedure (SRP) of random processes with an arbitrary number of samples is presented on the basis of the conditional mean rule. This rule provides the minimum reconstruction error. This rule is valid for processes with an arbitrary probability density functions. Using this rule we obtain two principal statistical characteristics of any SRP: the reconstruction function and the error reconstruction function. There is the detail consideration of Gaussian processes SRP. The basic functions are linear functions of samples. There are some results about SRP of multidimensional Gaussian processes. The influence of number of samples, the covariance function, the location of samples on the principal SRP characteristics is demonstrated in many examples. The Markov and non Markov models are considered. The cases of the stationary and non stationary processes are investigated. The SRP of some non Gaussian processes of usual types (with Rayleigh, gamma, etc. distributions) is given. The optimal reconstruction functions are no linear functions of samples here. We investigate the SRP of non Gaussian processes at the output of many non linear non inertial converters when the Gaussian processes are in their inputs. With the same methodology we describe the SRP of Gaussian fields when the samples are located in the radial, spiral, triangular, rectangular, pentagonal and arbitrary manner. There are special cases of SRP of random processes with jumps. We consider the SRP of binary Markov process, the SRP of Markov chains with continuous time and with an arbitrary number of states (this result is not published). Finally we discuss the statistical problem of the SRP description of many above mentioned processes with jitter on the basis of our jitter model as the random variable with the beta distribution (some results are not published).

PLENARY LECTURE 7

Sub-Molecular Systems Analysis: Patterns, Structure, Phylogeny and Functionality

Professor David K.Y. Chiu

Department of Computing & Information Science

and Biophysics Interdepartmental Group

University of Guelph, Canada

Abstract: Systems analysis when applied to biomolecular data focuses on how different components interact in a complex manner. This talk describes some methods developed to reconstruct the relationship between sub-molecular attributes and other attributes such as molecular structural characteristics, phylogenetic associations and genetic functionality. High-order patterns from sub-molecular data are sets of multiple associated values discovered which can relate to other phenotypical data from diverse sources. This talk will discuss experiments using microbial genomes, multiple alignments of model molecules such as transfer RNA, p53 cancer suppressor, cytochrome c, lysozyme and the SH3 domains. It illustrates how the proposed methods can be useful in detecting attributes in suggesting a descriptive partial model for 2D/3D molecular structure, genome organization, cancer factors, core and binding molecular sites, and gene ontology patterns.

PLENARY LECTURE 8

A Holistic View of Business Intelligence

Professor Zeljko Panian

Faculty of Economics and Business

University of Zagreb

Croatia

Abstract: Enterprises should generate their business intelligence (BI) from data captured from each and every source available. Generally taken, there are two main classes of these sources – internal and external sources.

External sources are related to markets on which the enterprise appears and operates, and data captured from these sources can serve as a means for the market intelligence development. On the other hand, there are many internal sources of data that can be used to generate internal enterprise intelligence. They relate primarily to business processes and management issues.

The holistic view of business intelligence suggests that both types of business intelligence – market intelligence as well as internal enterprise intelligence – should be taken into account. To put it more precisely, five key constituents of business intelligence should be considered when planning the development or upgrade of the business intelligence system:

• Customer intelligence, competitive intelligence and value chain intelligence, as the building blocks of the market intelligence, and

• Management intelligence and business process intelligence, as parts of the internal enterprise intelligence.

Features and interactions of all these five BI constituents will be discussed.

SESSION: Acoustics & Music I

Chair: Richard Willgoss, Lamberto Tronchin

SESSION: Acoustics & Music II

Chair: Lamberto Tronchin, Shabbir Majeed Chaudhry

Thursday, June 21 2007

SESSION: Acoustics & Music III

Chair: Shabbir Majeed Chaudhry, Richard Willgoss

PROGRAM

8^th WSEAS International Conference on
MATHEMATICS AND COMPUTERS IN BIOLOGY AND CHEMISTRY
(MCBC'07)

Vancouver, Canada, June 20-21, 2007

Tuesday, June 19 2007

PLENARY LECTURE 5

3D-Auralization of Music

Assistant Professor Lamberto Tronchin

DIENCA - CIARM

University of Bologna, Italy

Abstract: The definition and measurement of sound spatialisation have been strongly enhanced in last years, as nowadays spatialisation is considered quite important during design of auditoria and virtual audio reproduction of sound quality in dedicated listening rooms for 3D reproduction purposes. Even though international standards like ISO 3382 require measuring some spatial parameters (i.e. LE, LF, IACC), usually only binaural measurements are performed, by means of a dummy head, and rarely 3D impulse responses are measured and utilised for sound reproduction.

In this paper, after an overview on the most common techniques utilised for 3D auralization, an innovative procedure of measuring and reproducing spatial sound characteristics is presented. The application of this new technique in virtual 3D sound reconstruction is presented. Furthermore, the methodology is compared with other techniques of 3D sound reproduction. Moreover, the results of a wide campaign of measurements of spatial parameters among different auditoria all over the world, ranging from Italy to Japan and Australia, and conducted with the novel methodology, are compared with the results of standard binaural and 3D measurements. The possibility to enhance the spatial reproduction of sound quality in real spaces and the comprehensibility of spatial parameters is finally considered and presented in different cases.

PLENARY LECTURE 6

General Problems of Sampling-Reconstruction Procedure of Random Processes with the Limited Number of Samples

Professor Vladimir Kazakov

Department of Telecommunications of the School of
Mechanical and Electrical Engineering

National Polytechnic Institute of Mexico

U. Zacatenco, C.P. 07738

Mexico City, MEXICO

Abstract: Some critical observations are given about Balakrishnan's theorem. We prove that this theorem is only valid for Gaussian processes with the infinite number of samples. The general case of the Sampling-Reconstruction Procedure (SRP) of random processes with an arbitrary number of samples is presented on the basis of the conditional mean rule. This rule provides the minimum reconstruction error. This rule is valid for processes with an arbitrary probability density functions. Using this rule we obtain two principal statistical characteristics of any SRP: the reconstruction function and the error reconstruction function. There is the detail consideration of Gaussian processes SRP. The basic functions are linear functions of samples. There are some results about SRP of multidimensional Gaussian processes. The influence of number of samples, the covariance function, the location of samples on the principal SRP characteristics is demonstrated in many examples. The Markov and non Markov models are considered. The cases of the stationary and non stationary processes are investigated. The SRP of some non Gaussian processes of usual types (with Rayleigh, gamma, etc. distributions) is given. The optimal reconstruction functions are no linear functions of samples here. We investigate the SRP of non Gaussian processes at the output of many non linear non inertial converters when the Gaussian processes are in their inputs. With the same methodology we describe the SRP of Gaussian fields when the samples are located in the radial, spiral, triangular, rectangular, pentagonal and arbitrary manner. There are special cases of SRP of random processes with jumps. We consider the SRP of binary Markov process, the SRP of Markov chains with continuous time and with an arbitrary number of states (this result is not published). Finally we discuss the statistical problem of the SRP description of many above mentioned processes with jitter on the basis of our jitter model as the random variable with the beta distribution (some results are not published).

PLENARY LECTURE 7

Sub-Molecular Systems Analysis: Patterns, Structure, Phylogeny and Functionality

Professor David K.Y. Chiu

Department of Computing & Information Science

and Biophysics Interdepartmental Group

University of Guelph, Canada

Abstract: Systems analysis when applied to biomolecular data focuses on how different components interact in a complex manner. This talk describes some methods developed to reconstruct the relationship between sub-molecular attributes and other attributes such as molecular structural characteristics, phylogenetic associations and genetic functionality. High-order patterns from sub-molecular data are sets of multiple associated values discovered which can relate to other phenotypical data from diverse sources. This talk will discuss experiments using microbial genomes, multiple alignments of model molecules such as transfer RNA, p53 cancer suppressor, cytochrome c, lysozyme and the SH3 domains. It illustrates how the proposed methods can be useful in detecting attributes in suggesting a descriptive partial model for 2D/3D molecular structure, genome organization, cancer factors, core and binding molecular sites, and gene ontology patterns.

PLENARY LECTURE 8

A Holistic View of Business Intelligence

Professor Zeljko Panian

Faculty of Economics and Business

University of Zagreb

Croatia

Abstract: Enterprises should generate their business intelligence (BI) from data captured from each and every source available. Generally taken, there are two main classes of these sources – internal and external sources.

External sources are related to markets on which the enterprise appears and operates, and data captured from these sources can serve as a means for the market intelligence development. On the other hand, there are many internal sources of data that can be used to generate internal enterprise intelligence. They relate primarily to business processes and management issues.

The holistic view of business intelligence suggests that both types of business intelligence – market intelligence as well as internal enterprise intelligence – should be taken into account. To put it more precisely, five key constituents of business intelligence should be considered when planning the development or upgrade of the business intelligence system:

• Customer intelligence, competitive intelligence and value chain intelligence, as the building blocks of the market intelligence, and

• Management intelligence and business process intelligence, as parts of the internal enterprise intelligence.

Features and interactions of all these five BI constituents will be discussed.

SESSION: Numerical Analysis and Image Processing

Chair: Walter Krämer, Zhang Jie

Thursday, June 21 2007

SESSION: Modelling and Mathematics in Biomedicine

Chair: David Chiu, Girija Jayaraman

PROGRAM

8^th WSEAS International Conference on
MATHEMATICS AND COMPUTERS IN BUSINESS AND ECONOMICS
(MCBE'07)

Vancouver, Canada, June 20-21, 2007

Tuesday, June 19 2007

SESSION: Decision Making in Economics

Chair: Chang-Kyo Suh, Mojca Bernik

Wednesday, June 20 2007

PLENARY LECTURE 5

3D-Auralization of Music

Assistant Professor Lamberto Tronchin

DIENCA - CIARM

University of Bologna, Italy

Abstract: The definition and measurement of sound spatialisation have been strongly enhanced in last years, as nowadays spatialisation is considered quite important during design of auditoria and virtual audio reproduction of sound quality in dedicated listening rooms for 3D reproduction purposes. Even though international standards like ISO 3382 require measuring some spatial parameters (i.e. LE, LF, IACC), usually only binaural measurements are performed, by means of a dummy head, and rarely 3D impulse responses are measured and utilised for sound reproduction.

In this paper, after an overview on the most common techniques utilised for 3D auralization, an innovative procedure of measuring and reproducing spatial sound characteristics is presented. The application of this new technique in virtual 3D sound reconstruction is presented. Furthermore, the methodology is compared with other techniques of 3D sound reproduction. Moreover, the results of a wide campaign of measurements of spatial parameters among different auditoria all over the world, ranging from Italy to Japan and Australia, and conducted with the novel methodology, are compared with the results of standard binaural and 3D measurements. The possibility to enhance the spatial reproduction of sound quality in real spaces and the comprehensibility of spatial parameters is finally considered and presented in different cases.

PLENARY LECTURE 6

General Problems of Sampling-Reconstruction Procedure of Random Processes with the Limited Number of Samples

Professor Vladimir Kazakov

Department of Telecommunications of the School of
Mechanical and Electrical Engineering

National Polytechnic Institute of Mexico

U. Zacatenco, C.P. 07738

Mexico City, MEXICO

Abstract: Some critical observations are given about Balakrishnan's theorem. We prove that this theorem is only valid for Gaussian processes with the infinite number of samples. The general case of the Sampling-Reconstruction Procedure (SRP) of random processes with an arbitrary number of samples is presented on the basis of the conditional mean rule. This rule provides the minimum reconstruction error. This rule is valid for processes with an arbitrary probability density functions. Using this rule we obtain two principal statistical characteristics of any SRP: the reconstruction function and the error reconstruction function. There is the detail consideration of Gaussian processes SRP. The basic functions are linear functions of samples. There are some results about SRP of multidimensional Gaussian processes. The influence of number of samples, the covariance function, the location of samples on the principal SRP characteristics is demonstrated in many examples. The Markov and non Markov models are considered. The cases of the stationary and non stationary processes are investigated. The SRP of some non Gaussian processes of usual types (with Rayleigh, gamma, etc. distributions) is given. The optimal reconstruction functions are no linear functions of samples here. We investigate the SRP of non Gaussian processes at the output of many non linear non inertial converters when the Gaussian processes are in their inputs. With the same methodology we describe the SRP of Gaussian fields when the samples are located in the radial, spiral, triangular, rectangular, pentagonal and arbitrary manner. There are special cases of SRP of random processes with jumps. We consider the SRP of binary Markov process, the SRP of Markov chains with continuous time and with an arbitrary number of states (this result is not published). Finally we discuss the statistical problem of the SRP description of many above mentioned processes with jitter on the basis of our jitter model as the random variable with the beta distribution (some results are not published).

PLENARY LECTURE 7

Sub-Molecular Systems Analysis: Patterns, Structure, Phylogeny and Functionality

Professor David K.Y. Chiu

Department of Computing & Information Science

and Biophysics Interdepartmental Group

University of Guelph, Canada

Abstract: Systems analysis when applied to biomolecular data focuses on how different components interact in a complex manner. This talk describes some methods developed to reconstruct the relationship between sub-molecular attributes and other attributes such as molecular structural characteristics, phylogenetic associations and genetic functionality. High-order patterns from sub-molecular data are sets of multiple associated values discovered which can relate to other phenotypical data from diverse sources. This talk will discuss experiments using microbial genomes, multiple alignments of model molecules such as transfer RNA, p53 cancer suppressor, cytochrome c, lysozyme and the SH3 domains. It illustrates how the proposed methods can be useful in detecting attributes in suggesting a descriptive partial model for 2D/3D molecular structure, genome organization, cancer factors, core and binding molecular sites, and gene ontology patterns.

PLENARY LECTURE 8

A Holistic View of Business Intelligence

Professor Zeljko Panian

Faculty of Economics and Business

University of Zagreb

Croatia

Abstract: Enterprises should generate their business intelligence (BI) from data captured from each and every source available. Generally taken, there are two main classes of these sources – internal and external sources.

External sources are related to markets on which the enterprise appears and operates, and data captured from these sources can serve as a means for the market intelligence development. On the other hand, there are many internal sources of data that can be used to generate internal enterprise intelligence. They relate primarily to business processes and management issues.

The holistic view of business intelligence suggests that both types of business intelligence – market intelligence as well as internal enterprise intelligence – should be taken into account. To put it more precisely, five key constituents of business intelligence should be considered when planning the development or upgrade of the business intelligence system:

• Customer intelligence, competitive intelligence and value chain intelligence, as the building blocks of the market intelligence, and

• Management intelligence and business process intelligence, as parts of the internal enterprise intelligence.

Features and interactions of all these five BI constituents will be discussed.

Thursday, June 21 2007

SESSION: Modelling and Mathematics in Economics

Chair: Zeljko Panian, Ahmad A. Moreb

SESSION: Applied Economics

Chair: Zeljko Panian, Patricia Milligan

PROGRAM

8^th WSEAS International Conference on
AUTOMATION and INFORMATION
(ICAI’07)

Vancouver, Canada, June 20-21, 2007

Wednesday, June 20 2007

PLENARY LECTURE 5

3D-Auralization of Music

Assistant Professor Lamberto Tronchin

DIENCA - CIARM

University of Bologna, Italy

Abstract: The definition and measurement of sound spatialisation have been strongly enhanced in last years, as nowadays spatialisation is considered quite important during design of auditoria and virtual audio reproduction of sound quality in dedicated listening rooms for 3D reproduction purposes. Even though international standards like ISO 3382 require measuring some spatial parameters (i.e. LE, LF, IACC), usually only binaural measurements are performed, by means of a dummy head, and rarely 3D impulse responses are measured and utilised for sound reproduction.

In this paper, after an overview on the most common techniques utilised for 3D auralization, an innovative procedure of measuring and reproducing spatial sound characteristics is presented. The application of this new technique in virtual 3D sound reconstruction is presented. Furthermore, the methodology is compared with other techniques of 3D sound reproduction. Moreover, the results of a wide campaign of measurements of spatial parameters among different auditoria all over the world, ranging from Italy to Japan and Australia, and conducted with the novel methodology, are compared with the results of standard binaural and 3D measurements. The possibility to enhance the spatial reproduction of sound quality in real spaces and the comprehensibility of spatial parameters is finally considered and presented in different cases.

PLENARY LECTURE 6

General Problems of Sampling-Reconstruction Procedure of Random Processes with the Limited Number of Samples

Professor Vladimir Kazakov

Department of Telecommunications of the School of
Mechanical and Electrical Engineering

National Polytechnic Institute of Mexico

U. Zacatenco, C.P. 07738

Mexico City, MEXICO

Abstract: Some critical observations are given about Balakrishnan's theorem. We prove that this theorem is only valid for Gaussian processes with the infinite number of samples. The general case of the Sampling-Reconstruction Procedure (SRP) of random processes with an arbitrary number of samples is presented on the basis of the conditional mean rule. This rule provides the minimum reconstruction error. This rule is valid for processes with an arbitrary probability density functions. Using this rule we obtain two principal statistical characteristics of any SRP: the reconstruction function and the error reconstruction function. There is the detail consideration of Gaussian processes SRP. The basic functions are linear functions of samples. There are some results about SRP of multidimensional Gaussian processes. The influence of number of samples, the covariance function, the location of samples on the principal SRP characteristics is demonstrated in many examples. The Markov and non Markov models are considered. The cases of the stationary and non stationary processes are investigated. The SRP of some non Gaussian processes of usual types (with Rayleigh, gamma, etc. distributions) is given. The optimal reconstruction functions are no linear functions of samples here. We investigate the SRP of non Gaussian processes at the output of many non linear non inertial converters when the Gaussian processes are in their inputs. With the same methodology we describe the SRP of Gaussian fields when the samples are located in the radial, spiral, triangular, rectangular, pentagonal and arbitrary manner. There are special cases of SRP of random processes with jumps. We consider the SRP of binary Markov process, the SRP of Markov chains with continuous time and with an arbitrary number of states (this result is not published). Finally we discuss the statistical problem of the SRP description of many above mentioned processes with jitter on the basis of our jitter model as the random variable with the beta distribution (some results are not published).

PLENARY LECTURE 7

Sub-Molecular Systems Analysis: Patterns, Structure, Phylogeny and Functionality

Professor David K.Y. Chiu

Department of Computing & Information Science

and Biophysics Interdepartmental Group

University of Guelph, Canada

Abstract: Systems analysis when applied to biomolecular data focuses on how different components interact in a complex manner. This talk describes some methods developed to reconstruct the relationship between sub-molecular attributes and other attributes such as molecular structural characteristics, phylogenetic associations and genetic functionality. High-order patterns from sub-molecular data are sets of multiple associated values discovered which can relate to other phenotypical data from diverse sources. This talk will discuss experiments using microbial genomes, multiple alignments of model molecules such as transfer RNA, p53 cancer suppressor, cytochrome c, lysozyme and the SH3 domains. It illustrates how the proposed methods can be useful in detecting attributes in suggesting a descriptive partial model for 2D/3D molecular structure, genome organization, cancer factors, core and binding molecular sites, and gene ontology patterns.

PLENARY LECTURE 8

A Holistic View of Business Intelligence

Professor Zeljko Panian

Faculty of Economics and Business

University of Zagreb

Croatia

Abstract: Enterprises should generate their business intelligence (BI) from data captured from each and every source available. Generally taken, there are two main classes of these sources – internal and external sources.

External sources are related to markets on which the enterprise appears and operates, and data captured from these sources can serve as a means for the market intelligence development. On the other hand, there are many internal sources of data that can be used to generate internal enterprise intelligence. They relate primarily to business processes and management issues.

The holistic view of business intelligence suggests that both types of business intelligence – market intelligence as well as internal enterprise intelligence – should be taken into account. To put it more precisely, five key constituents of business intelligence should be considered when planning the development or upgrade of the business intelligence system:

• Customer intelligence, competitive intelligence and value chain intelligence, as the building blocks of the market intelligence, and

• Management intelligence and business process intelligence, as parts of the internal enterprise intelligence.

Features and interactions of all these five BI constituents will be discussed.

SESSION: Information Technology and Computers

Chair: Hankyu Lim, Sunint Saini

Thursday, June 21 2007

SESSION: Systems Modelling and Automation I

Chair: Vladimir Kazakov, Sylvia Encheva

SESSION: Systems Modelling and Automation II

Chair: Javier Moreno, Kwoting Fang