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

Research on Signal Processing Research on Signal Processing Cooperation with ELIKO Competence Center Cooperation with ELIKO Competence Center

in electronics and ICTin electronics and ICT

byby Mart Min Mart Min mart.min@ttu.eemart.min@ttu.ee

Thomas Johann Seebeck Department of Electronics Thomas Johann Seebeck Department of Electronics Tallinn University of Technology Tallinn University of Technology

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Signal Processing: what kind of? for what? 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-)chemical impedance mean also electrical impedance, but of biological or chemical matter.

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Identification of dynamic systems is the goalIdentification of dynamic systems is the goal

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. response Vz

1) excitation amplitude is limited ! 2) excitation time is limited !

|Ż (f )|

(f )

Short-time DFT or FFT: directly or via binary transforms (Walsh, Hadamard)

Frequency: f 1 to f n

Timing/synchro: t 1 to t 2 Measurement time, Tm

. Response Vz(t)

Generator of excitation current Iexc(t)

|Z (ω,t)| Φ(ω,t) ReŻ (ω,t) ImŻ (ω,t)

Ż(ω,t)

internal noise, time-variant

nonlinear system

Results as Spectrograms

outer noise

Focus: finding the best excitation waveforms for the fast and wideband time dependent spectral analysis: intensity (Re & Im or M & φ)

versus frequency ω and time t

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Requirements to the Impedance SpectroscopyRequirements to the Impedance Spectroscopy

A. Fast measurement and signal processing in a wide frequency range;B. Simple architecture and electronic circuitry (simplicity, dependability);C. Low power (extremely low in some applications) and low voltage operation;

Excitation waveform: Excitation waveform: aa) easy to generate; ) easy to generate; bb) easy to tune; ) easy to tune; cc)) covers the needed frequency range; covers the needed frequency range; dd)) generated energy must be concentrated into the BW of interest; generated energy must be concentrated into the BW of interest; ee) effective energy packaging (low crest factor - less than 1.5); ) effective energy packaging (low crest factor - less than 1.5); ff )) simple processing of the response signal. simple processing of the response signal.

Signal processing for performing deconvolution:Signal processing for performing deconvolution:aa) simple algorithms, ) simple algorithms, bb) fast processing of the response signals, ) fast processing of the response signals, cc) getting frequency domain but time dependent results – performing ) getting frequency domain but time dependent results – performing the joint time-frequency analysisthe joint time-frequency analysis..

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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?

Problems to be solved by using of chirps Problems to be solved by using of chirps

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B. Scalability in time domain: duration Texc changes, BW = const = 100 kHz 1.0

-1.0

-0.8

-0.5

-0.2

0.0

0.2

0.5

0.8

1m0 100u 200u 300u 400u 500u 600u 700u 800u 900u

Texc = 250 μs Texc = 1000 μs

100m

1u

10u

100u

1m

10m

10M1k 10k 100k 1M

2.24 mV/Hz1/2

4.48 mV/Hz1/2

1 mV / Hz1/2

BW = 100 kHz

Energy E250μs = 125 V2∙μs Energy E1000μs = 500 V2∙μs

Voltage Spectral Density @ 250μs = 2.24 mV/ Hz1/2 Voltage Spectral Density @ 1000μs = 4.48 mV/ Hz1/2

Changes in the pulse duration Texc

reflect in spectral density

Bandwidth BW = 100 kHz = const

48 cycles 12 cycles

Scalable chirp signalsScalable chirp signals:: two chirplets 2 two chirplets 2

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A. Scalability in frequency domain: bandwidth BW changes, Texc = const = 250 μs 1.0

-1.0

-0.8

-0.5

-0.2

0.0

0.2

0.5

0.8

250u0 25u 50u 75u 100u 125u 150u 175u 200u 225u

Texc = 250 μs

t

100m

1u

10u

100u

1m

10m

10M1k 10k 100k 1M

2.24 mV/Hz1/2

1.12 mV/Hz1/2

1 mV / Hz1/2

BW = 100 kHz

BW = 400 kHz

Texc = 1000 μs

Excitation energy Eexc = 0.5V2 ∙250 μs = 125 V2∙μs

Voltage Spectral Density @ 100 kHz = 2.24 mV/Hz1/2

Voltage Spectral Density @ 400 kHz = 1.12 mV/Hz1/2

Changes in the frequency span BW reflect in spectral density

48 cycles 12 cycles

Excitation time Texc = 250 μs = const

Scalable chirp signalsScalable chirp signals:: two chirplets 1 two chirplets 1

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10

100u

1m

10m

100m

1

10M1k 10k 100k 1M

-40 dB/dec

RMS spectral density (relative)

10

1

10-1

10-2

10-3

10-4

1k 10k 100k 1M f, Hz

2.26 mV/Hz1/2

BW = 100 kHz

Instant frequency , , rad/s - a linear frequency growth chfin Ttfdt

tdt /2

)(

Current phase , rad; chfin Ttfdttt 2/2)( 2

100kHz

Texc = Tch = 10 μs,

A very short Chirplet - Half-cycle linear A very short Chirplet - Half-cycle linear

chfin Ttfdttt 2/2)(sinsin 2Generated chirplet 99

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1.0

-1.0

-0.5

0.0

0.5

200 2 4 6 8 10 12 14 16 18

0.10

0.00

0.02

0.04

0.06

0.08

200 2 4 6 8 10 12 14 16 18

20

-80

-60

-40

-20

0

10M1k 10k 100k 1M

-80 dB/decc

A very short Chirp - 2x quarter-cycle linear chirplet A very short Chirp - 2x quarter-cycle linear chirplet

Frequency, Hz Frequency, Hz

Time, Time, μμs s

Normalised level Normalised level

RMS spectral density, normalised RMS spectral density, normalised

Frequency, MHz (max 100kHz) Frequency, MHz (max 100kHz)

Time, Time, μμs s

3/2sin 3ch2 tktC

ff == ffmaxmax(t /(t /Tch))22

0

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30

Spectra and power of binary/ternary chirpsSpectra and power of binary/ternary chirps

Binary(0): Pexc= 0.85P

Binary (0)

Ternary (30)

Ternary (21.2): Pexc= 0.94P – max. possible!

Pexc – excitation powerwithin (BW)exc=100kHz

100kHz

1111

1212

Relative time

Classical sinc waveform – mathematically the bestClassical sinc waveform – mathematically the best

Fast simultaneous measurementat the specific frequencies of interest!

+ Simultaneous/parallel measurement and analysis (fast);

+ Frequencies can be chosen freely;

+/- Signal-to-noise level is acceptable;

− complicated synthesis restricts the number of different frequency components.

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Several sine waves simultaneously – Several sine waves simultaneously – Multisine excitation Multisine excitation

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Signal space is limited between +1 and -1 (Signal space is limited between +1 and -1 (ΣΣAAii = 1)= 1)

Max crest factor maxMax crest factor max CF = CF = ΣΣAAii // (RMS)(RMS)ΣΣ = 2.83 = 2.83

Min(RMS)Min(RMS)ΣΣ = 0.36 (worst case) = 0.36 (worst case)

Max(RMS)Max(RMS)ΣΣ == 0.72 (optimised phases)0.72 (optimised phases)

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Crest factors CF of optimised multisine excitation(a sum of n sine wave components, n = 3 to 20)

For a single sine wave CF=For a single sine wave CF=√√2=1.4142=1.414

CFCF = = ΣΣAAii // RMS for optimally synthesized multisine signalsRMS for optimally synthesized multisine signals

The best known before The best known before

Jaan Ojarand’sJaan Ojarand’salgorithm algorithm

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Relative time

Optimised multisine waveformOptimised multisine waveform

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Relative time

Less than 10% of total RMS

Binary multifrequency waveformBinary multifrequency waveform

Synthesized multifrequency binary sequences

(4 components – 1, 3, 5, 7f)

Equal-level components

Growing-level components !

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1- binary multifrequency (BMF)2- optimal multisine (MS) 3- modified sinc (bipolar)5- sinc (classic)

BMF- binary multifrequency

MS- multisine

bipolar sincsinc

A single sine wave has: energy- 50%, RMS - 71% (less than MS!)

Energy and RMS of different excitation waveformsEnergy and RMS of different excitation waveforms

Collaboration with industry through ELIKO

Industry export

Business ICT, medical

Basic Research

Centre of excellence

CEBE at the TTU

Governmental and Public Sector

of the EU and Estonia, funds, programs, projects

Product Development

Applied and Industry

Research

Competence Centre

2020

Impedance spectroscopy devices using MBS:Impedance spectroscopy devices using MBS:laboratory devices prototyped in ELIKOlaboratory devices prototyped in ELIKO

The project with Electrolux Italy S.p.a

Partners: Food and Fermentation Competence Center and ELIKO

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Meat quality assessmentMeat 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

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

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The research center CEBE is founded for making The research center CEBE is founded for making fundamental science. fundamental science.

The scientific results can be and have been The scientific results can be and have been transferred into industry and commercialised transferred into industry and commercialised using Technology Competence Centres asusing Technology Competence Centres as

ELIKO – electronics and ICT, andELIKO – electronics and ICT, andFFCC – food and fermentation.FFCC – food and fermentation.

Thank you for listening!Thank you for listening!

SummarySummary

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