Presentation on theme: "A. ACCATTATIS, M. SALMERI, A. MENCATTINI, G. RABOTTINO, R. LOJACONO Visual Analyser: a Sophisticated Virtual Measurements Laboratory for Students University."— Presentation transcript:
A. ACCATTATIS, M. SALMERI, A. MENCATTINI, G. RABOTTINO, 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
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.