ADVANCED MATHEMATICAL TOOLS IN METROLOGY III

Inbunden, Engelska, 1997

Del 45 i serien SERIES ON ADVANCES IN MATHEMATICS FOR APPLIED SCIENCES

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Beskrivning

This book is of interest to researchers in universities, research centres and industries who are involved in measurements and need advanced mathematical tools to solve their problems, and to whoever is working in the development of these mathematical tools. Advances in metrology depend on improvements in scientific and technical knowledge and in instrumentation quality as well in a better use of advanced mathematical tools and in the development of new ones. In this book scientists from both the mathematical and the metrological fields exchange their experiences. Industrial sectors such as instrumentation and software, are likely to benefit from this exchange, since metrology has a high impact on the overall quality of industrial products and applied mathematics is becoming more and more important in industrial processes.

Produktinformation

Utgivningsdatum:1997-08-05
Format:Inbunden
Språk:Engelska
Serie:SERIES ON ADVANCES IN MATHEMATICS FOR APPLIED SCIENCES
Antal sidor:300
Förlag:World Scientific Publishing Co Pte Ltd
ISBN:9789810229184

Utforska kategorier

Innehållsförteckning

Parameter estimation as illposed inverse problems - theory and numerical methods, K. Kunisch; wavelet methods in signal processing, P. Maass; bootstrap technique, P. Ciarlini; the technologically and topologically related surface model - 13 constraints for dimensioning, tolerancing and inspection, A. Clement et al; an approximation method for the linearization of 3D metrology problems, L. Mathieu et al; software problems in calibration service - a case study, D. Richter et al; reference data sets for testing software used in metrology, M.G. Cox et al; quality of experimental data in hydrodynamic research, M. Masia et al; mathematical methods for data analysis in medical applications, J. Honerkamp; dipole estimation for MCG-data, E. Krause; identification of distributed parameters - an overview, J. Sprekels; robust alternatives to least squares, W. Stahel; high-dimensional empirical linear prediction, K. Liu; and other papers. (Part contents).