Visual Analyser: a Sophisticated Virtual Measurements Laboratory for Students

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Nov 24, 2013 (3 years and 8 months ago)

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A. ACCATTATI S, M. SALMERI,

A. MENCATTI NI,
G. RABOTTI NO,
R
.

LOJACONO


Visual Analyser: a Sophisticated Virtual

Measurements Laboratory for Students

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

Low cost Measurement instruments;

A Didactic laboratory for students;

“Low cost” hardware = PC

“Low cost” software = Visual Analyser.



Idea: deeply modify the software Visual Analyser to obtain:

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

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.

Up to the year 1990 real time elaboration of
signal implemented making use of dedicated

Microprocessors (DSP)

Metrics

200.000 lines of C++ code; Windows, Linux + wine;

Object Oriented;

Windows multithreading, as commercial instruments, making possible
to run simultaneously all the simulated instruments;

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

Oscilloscope

Wave form generator


Frequency meter


Volt Meter AC


Filtering


Data log with “trigger” events


Nyquist conversion real time


Frequency compensation



24 bit support


Specific hardware supported


Cross and auto correlation


THD


Cepstrum


Main

window

Frequency

meter

Waveform

generator

Phase

Multithreaded

Architecture

Ram (buffer)

D/A right

Functions

D/A
left

Freq
.

Capture

Sample
acquisition

User

Interface

Uncertainty

Calculus based on standard literature, the metrological characterization

depends mainly from the acquisition board;

Numerical analysis: no cancellation, no ill conditioned algorithms;

IEEE extended floating point, 80 bit 64 bit mantissa, rounding
error highly reduced;

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

1.
Accattatis, Master Thesis, “Sviluppo di uno strumento virtuale real
-
time per La

generazione analisi ed
acquisizione dei segnali”.

2.
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.

3.
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.

4.
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.

5.
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.

6.
M. J.
Korczynski
, A. Hetman, “A Calculation of Uncertainties in Virtual Instrument”,
Proc. of
Instrumentation and Measurement Technology Conference
, Ottawa, Canada, May 2005.

7.
G.
Betta
, C.
Liguori
, A.
Pietrosanto

“Propagation of uncertainty in a discrete Fourier transform
algorithm”,
Elsevier Measurement 27

(2000) 231
-
239.

8.
R.I. Becker, N. Morrison, “The errors in FFT estimation of the Fourier transform”, IEEE Transaction
Signal Process.
44 (8) (1996) 2073
-
2077.

9.
A.V.

Oppenheim
, R. W.
Schafer
, “
Discrete
-
time

signal

processing”,
Prentice

Hall
signal

processing
series
.