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