CEBE IAB Meeting, Sept 16-18, 2013 in Tallinn Research on Signal Processing Cooperation with ELIKO Competence Center in electronics and ICT by Mart Min by Mart Min Thomas Johann Seebeck Department of Electronics Tallinn University of Technology 1

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. 2

Identification of dynamic systems is the goal 3

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 4

Requirements 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: 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. the joint time-frequency analysis. 5

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 6

B. Scalability in time domain: duration T exc changes, BW = const = 100 kHz Texc = 250 μs Texc = 1000 μs 2.24 mV/Hz 1/ mV/Hz 1/2 1 mV / Hz 1/2 BW = 100 kHz Energy E 250 μs = 125 V 2 ∙ μs Energy E 1000 μs = 500 V 2 ∙ μs Voltage Spectral 250μs = 2.24 mV / Hz 1/2 Voltage Spectral 1 000μs = mV / Hz 1/2 Changes in the pulse duration T exc reflect in spectral density Bandwidth BW = 100 kHz = const 48 cycles 12 cycles Scalable chirp signals : two chirplets 2 7

A. Scalability in frequency domain: bandwidth BW changes, T exc = const = 250 μs Texc = 250 μs t 2.24 mV/Hz 1/ mV/Hz 1/2 1 mV / Hz 1/2 BW = 100 kHz BW = 400 kHz Texc = 1000 μs Excitation energy Eexc = 0.5V 2 ∙250 μs = 125 V 2 ∙μs Voltage Spectral 1 00 kHz = mV / Hz 1/2 Voltage Spectral 4 00 kHz = mV / Hz 1/2 Changes in the frequency span BW reflect in spectral density 48 cycles 12 cycles Excitation time Texc = 250 μs = const Scalable chirp signals : two chirplets 1 8

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

dB/dec c A very short Chirp - 2x quarter-cycle linear chirplet Frequency, Hz Time, μs Normalised level RMS spectral density, normalised Frequency, MHz (max 100kHz) Time, μs f = f max (t /) 2 f = f max (t / T ch ) 2

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

12 Relative time Classical sinc waveform – mathematically the best

Fast simultaneous measurement at 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. 0 Several sine waves simultaneously – Multisine excitation 13 Signal space is limited between +1 and -1 (ΣAi = 1) Max crest factor max CF = ΣA i / (RMS) Σ = 2.83 Min(RMS) Σ = 0.36 (worst case) Min(RMS) Σ = 0.36 (worst case) Max(RMS) Σ = 0.72 (optimised phases) 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) For a single sine wave CF= √ 2=1.414 CF = ΣA i / RMS for optimally synthesized multisine signals The best known before Jaan Ojarand’s algorithm

15 Relative time Optimised multisine waveform

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

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

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

Collaboration with industry through ELIKO

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

The project with Electrolux Italy S.p.a Partners: 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 ( ). 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) 23

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 as ELIKO – electronics and ICT, and FFCC – food and fermentation. Thank you for listening! Summary Summary 24