Visual Analyser: a Sophisticated Virtual Measurements Laboratory

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Visual Analyser: a Sophisticated Virtual
Measurements Laboratory for Students
A. ACCATTATIS, M. SALMERI,
A. MENCATTINI, G. RABOTTINO, R. LOJACONO
University of Rome “Tor Vergata”
16th IMEKO TC4 Symposium
Exploring New Frontiers of Instrumentation and Methods for Electrical and Electronic Measurements
Sept. 22-24, 2008, Florence, Italy
Summary
 What is the software Visual Analyser: a virtual
instrumentation set running under Windows;
 Purposes of Visual Analyser :didactics, research;
 Visual Analyser as typical Digital Signal
Processing application;
 Metrological Characterization of Visual Analyser
(under construction), uncertainty;
Laboratory
Idea: deeply modify the software Visual Analyser to obtain:
A Didactic laboratory for students;
Low cost Measurement instruments;
“Low cost” hardware = PC
“Low cost” software = Visual Analyser.
Personal Computer and DSP
Using a personal computer as “no cost“ hardware…
…plus the software Visual Analyser…
= PC as a DSP based hardware on which apply the major
results of theDigital Signal Processing science;
= source code, possibility to quickly adapt the software.
DSP
Using a PC as a standard DSP platform
Up to the year 1990 real time elaboration of
signal implemented making use of dedicated
Microprocessors (DSP)
From the year 1990 on the computational power of a PC reached a DSP;
For this reason now it is possible to write program like Visual Analyser.
Metrics
200.000 lines of C++ code; Windows, Linux + wine;
Windows multithreading, as commercial instruments, making possible
to run simultaneously all the simulated instruments;
Object Oriented;
No predefined library;
IEEE 80 bit Floating point;
meaningless “Rounding error” .
Purposes
Low cost virtual measurement laboratory for students;
Research activities involving signal acquisition, elaboration, synthesis;
Demonstration during lessons of many important concepts;
Uncertainty calculus.
Instruments
Spectrum analyzer
Nyquist conversion real time
Oscilloscope
Frequency compensation
Wave form generator
24 bit support
Frequency meter
Specific hardware supported
Volt Meter AC
Cross and auto correlation
Filtering
THD
Data log with “trigger” events
Cepstrum
Main window
Frequency meter
Waveform generator
Phase
Multithreaded Architecture
Sample acquisition
User Interface
Ram (buffer)
Freq.
Functions
D/A left
D/A right
Capture
Uncertainty
Calculus based on standard literature, the metrological characterization
depends mainly from the acquisition board;
IEEE extended floating point, 80 bit 64 bit mantissa, rounding
error highly reduced;
Numerical analysis: no cancellation, no ill conditioned algorithms;
Lack of documentation of soundcard, we are implementig an automated
procedure Based on Monte Carlo analysis to obtain metrological
characterization of Visual Analyser + soundcard.
References
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Accattatis, Master Thesis, “Sviluppo di uno strumento virtuale real-time per La generazione analisi ed
acquisizione dei segnali”.
S. Caldara, S. Nuccio, C. Spataro, “Measurement uncertainty estimation of a virtual instrument”, Proc.
of Instrumentation and Measurement Technology Conference (IMTC 2000), Baltimore, MD, USA.
H. Haitjema, B. Van Dorp, M. Morel, P. H. J. Schellekens, “Uncertainty estimation by the concept of
virtual instruments”, Proceedings of SPIE, the International Society for Optical Engineering, 2001.
D. A. Lampasi, L. Podestà, “A Practical Approach to Evaluate the Measurement Uncertainty of Virtual
Instruments”, Proc. of Instrumentation and Measurement Technology Conference, Como, Italy, May
2004.
E. Ghiani, N. Locci, C. Muscas, “Auto-Evaluation of the Uncertainty in Virtual Instruments”, IEEE
Transactions on Instrumentation and Measurement, vol. 53, n. 3, June 2004.
M. J. Korczynski, A. Hetman, “A Calculation of Uncertainties in Virtual Instrument”, Proc. of
Instrumentation and Measurement Technology Conference, Ottawa, Canada, May 2005.
G. Betta, C. Liguori, A. Pietrosanto “Propagation of uncertainty in a discrete Fourier transform
algorithm”, Elsevier Measurement 27 (2000) 231-239.
R.I. Becker, N. Morrison, “The errors in FFT estimation of the Fourier transform”, IEEE Transaction
Signal Process. 44 (8) (1996) 2073-2077.
A.V. Oppenheim, R. W. Schafer, “Discrete-time signal processing”, Prentice Hall signal processing
series.
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