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CEBE IAB Meeting, Sept 16-18, 2013 in Tallinn

by Mart Min mart.min@ttu.ee. CEBE IAB Meeting, Sept 16-18, 2013 in Tallinn Research on Signal Processing Cooperation with ELIKO Competence Center in electronics and ICT.

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CEBE IAB Meeting, Sept 16-18, 2013 in Tallinn

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  1. byMart Min mart.min@ttu.ee CEBE IAB Meeting, Sept 16-18, 2013 in Tallinn Research on Signal Processing Cooperation with ELIKO Competence Center in electronics and ICT Thomas Johann Seebeck Department of Electronics Tallinn University of Technology

  2. Signal Processing: what kind of? for what? • Digital and analog processing (synthesis and analysis) for: • 1. Synthesis and generation of excitation signals with predetermined bandwidth, waveform, spectral content and shape, for obtaining the most effective excitation for systems/substances to be measured, studied, tested. • 2. Analysis of the response signals (the results of excitation) in: (a) - frequency domain; (b) - joint time-frequency domain; (c) - time domain, to obtain the maximal amount of information for identification of dynamic and predominantly time varying systems, circuits, materials, structures. • Remarks: • Our main identification method is impedance spectroscopy of both technical and living systems (also impedance spectro-tomography). • Impedance – electrical (mostly), but also acoustical, optical, and mechanical. The terms bioimpedance and (electro-)chemicalimpedance mean also electrical impedance, but of biological or chemical matter.

  3. Identification of dynamic systems is the goal

  4. Focus:finding the best excitation waveforms for the fast and wideband time dependent spectral analysis: intensity (Re & ImorM & φ) versus frequency ωand time t

  5. Requirements to the Impedance Spectroscopy Fast measurement and signal processing in a wide frequency range; Simple architecture and electronic circuitry (simplicity, dependability); Low power (extremely low in some applications) and low voltage operation; Excitation waveform: a) easy to generate; b) easy to tune; c) covers the needed frequency range; d)generated energy must be concentrated into the BW of interest; e) effective energy packaging (low crest factor - less than 1.5); f) simple processing of the response signal. Signal processing for performing deconvolution: a) simple algorithms, b) fast processing of the response signals, c) getting frequency domain but time dependent results – performing the joint time-frequency analysis.

  6. Problems to be solved by using of chirps Impedance appears to be non-stationary - their spectra are time dependent. Examples: (a) cardiovascular system (beating heart, pulsating blood); (b) pulmonary system (breathing); (c) running bio-particles in a microfluidic device. Excitation must be: 1) as short as possible to avoid significant changes during the spectrum analysis; 2) as long as possible to enlarge the excitation energy (max signal-to-noise ratio). Which waveform is the best one? A unique property of chirp waveforms – scalability – enables to reach compromise between contradictory requirements (1) and (2) The questions to be answered: a.A chirp wave excitation contains typically hundreds and thousands of cycles. What could be the lowest number of cycles applicable if the fast changes take place? b. Are there any simpler rectangular waveforms (binary or ternary) to replace the sine wave based chirps in practical spectroscopy?

  7. Scalable chirp signals: two chirplets 2 Texc = 250 μs Texc = 1000 μs 4.48mV/Hz1/2 2.24mV/Hz1/2 1mV / Hz1/2 BW = 100 kHz B. Scalability in time domain: duration Texc changes, BW= const = 100 kHz 12cycles 48cycles Bandwidth BW=100 kHz= const Energy E250μs= 125V2∙μs Energy E1000μs= 500V2∙μs Voltage Spectral Density @250μs=2.24mV/Hz1/2 Voltage Spectral Density @ 1000μs=4.48mV/Hz1/2 Changes in the pulse duration Texc reflect in spectral density

  8. Scalable chirp signals: two chirplets 1 t 2.24 mV/Hz1/2 1mV / Hz1/2 1.12 mV/Hz1/2 BW= 100 kHz BW = 400 kHz Texc = 250 μs A.Scalability in frequency domain: bandwidth BWchanges, Texc = const = 250 μs 48cycles 12cycles Texc = 1000 μs Excitation time Texc= 250 μs= const Excitation energyEexc= 0.5V2 ∙250μs=125V2∙μs Voltage Spectral Density @ 100kHz=2.24mV/Hz1/2 Voltage Spectral Density @ 400kHz=1.12mV/Hz1/2 Changes in the frequency span BW reflect in spectral density

  9. A very short Chirplet - Half-cycle linear RMS spectral density (relative) 10 1 10-1 10-2 10-3 10-4 -40 dB/dec 2.26 mV/Hz1/2 1k 10k 100k 1M f, Hz 100kHz Texc = Tch =10μs, BW = 100 kHz Instant frequency, , rad/s - a linear frequency growth Current phase , rad; Generated chirplet

  10. A very short Chirp - 2x quarter-cycle linear chirplet Normalised level RMS spectral density, normalised Time, μs -80 dB/decc Frequency, MHz (max 100kHz) f=fmax(t /Tch)2 Frequency, Hz Time, μs

  11. 0 18 30 Spectra and power of binary/ternary chirps Binary (0) Ternary (30) 100kHz Pexc– excitation power within (BW)exc=100kHz Binary(0): Pexc= 0.85P Ternary (21.2): Pexc=0.94P – max. possible!

  12. Classical sinc waveform – mathematically the best Relative time

  13. Several sine waves simultaneously – Multisine excitation Fast simultaneous measurement at the specific frequencies of interest! +Simultaneous/parallel measurement and analysis (fast); + Frequencies can be chosenfreely; +/- Signal-to-noise level is acceptable; − complicated synthesis restricts the number of different frequency components. 0 Signal space is limited between +1 and -1 (ΣAi= 1) Max crest factor maxCF = ΣAi/(RMS)Σ = 2.83 Min(RMS)Σ = 0.36 (worst case) Max(RMS)Σ=0.72 (optimised phases)

  14. Crest factors CF of optimised multisine excitation(a sum of n sine wave components, n=3 to 20) CF = ΣAi/RMS for optimally synthesized multisine signals The best known before For a single sine wave CF=√2=1.414 Jaan Ojarand’s algorithm

  15. Optimised multisine waveform Relative time

  16. Binary multifrequency waveform Relative time Less than 10% of total RMS

  17. Synthesized multifrequency binary sequences (4 components – 1, 3, 5, 7f) Equal-level components Growing-level components !

  18. Energy and RMS of different excitation waveforms BMF- binary multifrequency MS- multisine A single sine wave has: energy-50%, RMS - 71% (less than MS!) bipolar sinc sinc 1- binary multifrequency (BMF) 2- optimal multisine (MS) 3- modified sinc (bipolar) 5- sinc (classic)

  19. Collaboration with industry through ELIKO

  20. Impedance spectroscopy devices using MBS:laboratory devices prototyped in ELIKO

  21. The project with Electrolux Italy S.p.aPartners: Food and Fermentation Competence Center and ELIKO

  22. Meat quality assessment CAROMETEC A/S just bought a license to use the impedance spectroscopy method (CEBE patent) for meat quality assessment (13.10.2013). Carometec is a world leader in production of meat quality equipment for the food industry

  23. Real-time in vivo identification of various physiological condition of organs usinga range of needles. The foundations are: the different electricalproperties of human tissues(bioimpedance), advanced measurement technology (CEBE patent) we gave overto Injeq Oy,Finland,and proprietary needle designs (Injeq’s patent)

  24. The research center CEBE is founded for making fundamental science. The scientific results can be and have been transferred into industry and commercialised using Technology Competence Centres asELIKO – electronics and ICT, andFFCC – food and fermentation.Thank you for listening! Summary

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