Multi-dimensional approach to imaging, monitoring, or measuring systems and processes utilizing capacitance sensors

Abstract

A system and method for imaging, monitoring, or measuring systems and processes utilizing only data provided from capacitance sensors. The present invention combines the multi-frequency method of both ECVT/AECVT and DCPT to image or measure processes and systems more efficiently and accurately than the methods alone. The present system analyzes capacitance and current phase acquired at multiple frequencies to determine a plurality of properties of single and multiphase systems all at once. The combined use of ECVT and DCPT in multiphase flow can also be extended to measure volume fraction and phase distribution of flows involving greater than three phases by using multiple frequencies for capacitance, current phase, or both.

Claims

1. A system for generating a three-dimensional tomograph of a vessel interior or other object and for multi-phase flow decomposition, the system comprising: a capacitance sensor device comprising a plurality of electrodes for placement around the vessel or the object; an electrical circuit in communication with the capacitance sensor device for receiving input data from the capacitance sensor device, the electrical circuit adapted to output capacitance signals and electric phase signals at different frequencies; a hardware processing system in communication with the electrical circuit, the hardware processing system programmed with instructions for executing on the hardware processing system to: use the capacitance and electric phase signals at different frequencies to measure a volume fraction of each phase of the flow and for using each capacitance and electrical phase signal to calculate the velocity for each phase of the flow.

2. The system according to claim 1, wherein the capacitance sensor device is comprised of at least two planes of electrodes to provide sensor sensitivity in the axial and radial directions.

3. The system for generating a three-dimensional tomograph of a vessel interior or other object and for multi-phase flow decomposition according to claim 1, wherein the hardware processing system is programmed with instructions for using the product of a velocity and the volume fraction for each phase of the flow to determine mass flow rate.

4. The system according to claim 3, wherein the hardware processing system is programmed with instructions for providing real-time imaging of multiphase flow within the vessel.

5. The system according to claim 3, wherein the hardware processing system is programmed to use the volume fraction of each phase of the flow to produce a phase distribution image.

6. The system according to claim 3, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to reconstruct a three-dimensional volume-image of moving flows and stationary objects by analyzing the electric phase change of current from the capacitance sensor device.

7. The system according to claim 1, wherein the hardware processing system is programmed with instructions for executing on the processing system to measure velocity of flows that do not undergo changes in effective dielectric constant.

8. The system of claim 1, wherein each phase of the flow refers to the phase of the matter in the flow.

9. The system according to claim 1, wherein a sensitivity matrix is generated for the system by recording changes in phase with respect to changes in electric properties of flow materials.

10. The system according to claim 1, wherein the electrical circuit is further comprised of a synchronous demodulation circuit adapted to track phase changes in the current.

11. The system according to claim 9, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to use a gradient of phase sensitivity matrix to generate a three-dimensional (3D) velocity map based on phase measurements.

12. The system according to claim 1, wherein the capacitance sensor device is adapted to be operated at multiple frequencies simultaneously for probing material in the capacitance sensor device using differences in phases from various activation frequencies.

13. The system of claim 1, wherein the system is adapted to control the sensitivity of the capacitance sensor device by changing the frequency of the voltage distribution applied to at least one electrode.

14. The system of claim 1, wherein the electrical circuit is further comprised of: a current to voltage converter for receiving current output from the capacitance sensor device; a gain amplifier in electrical communication with the current to voltage converter; an analog to digital converter in electrical communication with the gain amplifier; a synchronous demodulation circuit in electrical communication with the analog to digital converter.

15. The system of claim 1, wherein the synchronous demodulation circuit is further comprised of low pass filters to filter out high frequency components of a signal received at an input to the synchronous demodulation circuit.

16. The system of claim 1, wherein the capacitance sensor device is adapted to be moved over a stationary object at a predetermined velocity and wherein the system is adapted to determine phase changes in the current.

17. The system of claim 16, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to reconstruct a three-dimensional volume-image from the phase changes.

18. The system of claim 1, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to convert the phase data to volume fraction occupied by material in the capacitance sensor device.

19. The system of claim 1, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to generate a three-dimensional (3D) velocity map by using the difference between two different phase measurements at two different times with a gradient sensitivity matrix.

20. The system of claim 1, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to reconstruct a three-dimensional volume-image from the capacitance signals at the different frequencies.

21. A system for generating a three-dimensional tomograph of a vessel interior or other object and for multi-phase flow decomposition, the system comprising: a capacitance sensor device comprising a plurality of electrodes for placement around the vessel or the object; an electrical circuit in communication with the capacitance sensor device for receiving input data from the capacitance sensor device, the electrical circuit adapted to output capacitance signals and electric phase signals at different frequencies; a hardware processing system in communication with the electrical circuit, the hardware processing system programmed with instructions for executing on the hardware processing system: to use the capacitance and electric phase signals at different frequencies to reconstruct a three-dimensional volume-image from the capacitance signals at the different frequencies and to use the electric phase signals at different frequencies to measure a volume fraction of each phase of the flow and for using each electrical phase signal to calculate the velocity for each phase of the flow.

22. The system according to claim 21, wherein the capacitance sensor device is comprised of at least two planes of electrodes to provide sensor sensitivity in the axial and radial directions.

23. The system for generating a three-dimensional tomograph of a vessel interior or other object and for multi-phase flow decomposition according to claim 21, wherein the hardware processing system is programmed with instructions for using the product of a velocity and the volume fraction for each phase of the flow to determine mass flow rate.

24. The system according to claim 23, wherein the hardware processing system is programmed with instructions for providing real-time imaging of multiphase flow within the vessel.

25. The system according to claim 23, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to reconstruct a three-dimensional volume-image of moving flows and stationary objects by analyzing the electric phase change of current from the capacitance sensor device.

26. The system according to claim 21, wherein the hardware processing system is programmed with instructions for executing on the processing system to measure velocity of flows that do not undergo changes in effective dielectric constant.

27. The system of claim 21, wherein each phase of the flow refers to the phase of the matter in the flow.

28. The system of claim 21, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to use the volume fraction of each phase of the flow to produce a phase distribution image.

29. A system for generating a three-dimensional tomograph of a vessel interior or other object and for multi-phase flow decomposition, the system comprising: a capacitance sensor device comprising a plurality of electrodes for placement around the vessel or the object; an electrical circuit in communication with the capacitance sensor device for receiving input data from the capacitance sensor device, the electrical circuit adapted to output capacitance signals and electric phase signals at different frequencies; and a hardware processing system in communication with the electrical circuit, the hardware processing system programmed with instructions for executing on the hardware processing system to: use the capacitance and electric phase signals at different frequencies to measure a volume fraction of each phase of the flow and for using the volume fraction of each phase of the flow to produce a phase distribution image.

30. The system of claim 29, wherein the hardware processing system is programmed with instructions for executing on the hardware processing system to reconstruct a three-dimensional volume-image from the capacitance signals at different frequencies and the phase changes at different frequencies.

31. The system of claim 29, wherein the hardware processing system is programmed with instructions for using a product of a velocity and the volume fraction for each phase of the flow to determine mass flow rate.

32. They system of claim 29, wherein the electrical circuit is further comprised of: a current to voltage converter for receiving current output from the capacitance sensor device; a gain amplifier in electrical communication with the current to voltage converter; an analog to digital converter in electrical communication with the gain amplifier; a synchronous demodulation circuit in electrical communication with the analog to digital converter, the synchronous demodulation circuit adapted to track phase changes in the current at different frequencies.

33. The system of claim 29, wherein volume fraction is a quantification of a volume of a first matter in relation to the total volume of all matter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) In addition to the features mentioned above, other aspects of the present invention will be readily apparent from the following descriptions of the drawings and exemplary embodiments, wherein like reference numerals across the several views refer to identical or equivalent features, and wherein:

(2) FIG. 1a illustrates a typical 24 electrode ECVT sensor with 4 layers of plates and exemplary measurements that the sensor may provide.

(3) FIG. 1b illustrates the typical components and signal flow of the present invention.

(4) FIG. 2a illustrates a flexible ECVT sensor embodiment where the diameter of the sensor can change readily through mechanical means.

(5) FIG. 2b illustrates the same sensor as in FIG. 2a from a top view of the sensor.

(6) FIG. 3a illustrates an adaptive sensor embodiment for AECTV where the small electrode plates may be activated together to create a single larger meta-plate in far more numerous configurations than traditional ECVT.

(7) FIG. 3b illustrates a possible meta-plate configuration on an AECVT sensor utilizing 3 adjacent plates.

(8) FIG. 3c illustrates a possible meta-plate configuration on an AECVT sensor utilizing 9 adjacent plates.

(9) FIG. 4a illustrates the exemplary stages of the Multi-Dimensional Approach in a linear configuration.

(10) FIG. 4b illustrates the exemplary stages of the Multi-Dimensional Approach in a feedback configuration.

(11) FIG. 5a illustrates one embodiment of Stage 1 according to the linear configuration of the Multi-Dimensional Approach in FIG. 4a.

(12) FIG. 5b illustrates one embodiment of Stage 2 according to the linear configuration of the Multi-Dimensional Approach in FIG. 4a.

(13) FIG. 5c illustrates one embodiment of Stage 3 according to the linear configuration of the Multi-Dimensional Approach in FIG. 4a.

(14) FIG. 6a illustrates one embodiment of Stage 1 according to the feedback configuration of the Multi-Dimensional Approach in FIG. 4b.

(15) FIG. 6b illustrates one embodiment of Stage 2 according to the feedback configuration of the Multi-Dimensional Approach in FIG. 4b.

(16) FIG. 6c illustrates one embodiment of Stage 3 according to the feedback configuration of the Multi-Dimensional Approach in FIG. 4b.

(17) FIG. 7a illustrates one embodiment of estimating new system parameters as in FIG. 5a as the physical configuration of a smart sensor can be detected by the system to use as an additional dimension in the Multi-Dimensional Approach.

(18) FIG. 7b illustrates one embodiment of estimating new system parameters as in FIG. 5a as the physical configuration of a smart sensor can be formed by the system to adapt to a known parameter to optimize the Multi-Dimensional Approach.

(19) FIG. 8a illustrates one embodiment of estimating new system parameters as in FIG. 5a as frequency transition points can be detected by the system through a frequency sweep.

(20) FIG. 8b illustrates one embodiment of estimating new system parameters as in FIG. 5a as pattern recognition may be used in conjunction with a frequency sweep to estimate additional system parameters.

(21) FIG. 9 illustrates one embodiment of estimating new system parameters as in FIG. 5a as AECVT is used to optimize data capture according to pattern recognition algorithms.

(22) FIG. 10 illustrates one embodiment of a building block circuit for measuring receiver current amplitude and phase at different frequencies.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENT(S)

(23) The following detailed description of the exemplary embodiments refers to the accompanying figures that form a part thereof. The detailed description provides explanations by way of exemplary embodiments. It is to be understood that other embodiments may be used having mechanical and electrical changes that incorporate the scope of the present invention without departing from the spirit of the invention.

(24) FIG. 1a illustrates a typical 24 electrode ECVT sensor with 4 planes of plates. It is appreciated that the sensor shape may be comprised of any size, shape, number, or orientation of plates. The sensor of the present invention may provide information on any of the listed parameters including signal phase, signal amplitude (capacitance), frequency response, or spatial geometry of the sensor. Additionally, any spatial derivative of the geometry (e.g., sensitivity matrix) may be obtained or any time or frequency derivative of signal phase or amplitude.

(25) FIG. 1b illustrates the typical components and signal flow of the present invention, comprising: a capacitance sensor comprised of a plurality of electrodes which emit an alternating electrical field from an excite plate and detection of the resulting signal at a detect plate where the signal has been modified by the medium between the plates; a data acquisition system or measurement circuit which, in one embodiment, sends the excitation signal to the sensor excite plates, receives the signal from the detect plates, processes the received signal with a notch filter at the excitation frequencies, demodulates the signal to obtain amplitude and phase, and outputs the data to a processing system; a processing system which processes the data according to the Multi-Dimensional Approach and outputs the data as an image or other quantification. In an alternative embodiment, the activation circuit is separate from the measurement circuit.

(26) FIG. 2a illustrates one embodiment of the sensor for an ECVT system as a flexible sensor which may change diameter or other physical dimensions through mechanical means such as external compression and decompression forces or materials which respond to electrical or other stimuli. The conductive plates in the figure alternate with insulated space. The outer portions of this embodiment are conductive. FIG. 2b illustrates the flexible sensor embodiment in FIG. 2a from the top view of the sensor. This sensor is adapted to expand to fit around the object it is sensing.

(27) FIG. 3a illustrates one embodiment of the sensor for an AECVT system as an adaptive sensor which may activate any group of smaller plates simultaneously to take measurements as one larger meta-plate. The configuration in which the meta-plates are formed can affect the measurements being taken and can be re-configured electrically to optimize the data being received from the sensor. The illustration depicts three overlapping meta-plate configurations, each with a small axial offset from the other (three electrodes 01, 02, 03 respectively). The overlapping shared space of each meta-plate allows a greater resolution of the interrogated space than traditional ECVT sensors. Additionally, different configurations of meta-plates may be used to optimize data collection for different applications such as in multiphase flow.

(28) FIG. 3b illustrates an exemplary embodiment of a meta-plate by combining three adjacent plates in a 31 configuration. FIG. 3c illustrates an exemplary embodiment of a meta-plate by combining nine adjacent plates in a 33 configuration.

(29) FIG. 4a illustrates an exemplary embodiment of the stages of the Multi-Dimensional approach as decision, measurement, and analysis, which are completed in a linear configuration. FIG. 4b illustrates an exemplary embodiment of the stages of the Multi-Dimensional Approach as decision, measurement, and analysis which are completed in a feedback configuration.

(30) FIG. 5a illustrates an exemplary embodiment of the first stage as presented in FIG. 4a. This first stage is referred to as the decision stage. In this stage, a number of known system parameters are taken as input into the system along with the desired properties to be measured from the system (e.g., phase distribution, velocity, temperature). These inputs are used to determine the optimal configuration to collect data. The optimal configuration may include decisions about excitation frequency, phase manipulations, capacitance manipulations, or geometrical configuration of the sensor. If the given parameters are not enough to measure the required properties, then data may be collected from the sensor and used to estimate further system parameters which then make it possible to measure the required properties.

(31) FIG. 5b illustrates an exemplary embodiment of the second stage as presented in FIG. 4a. This second stage is referred to as the measurement stage. In this stage, the optimal configuration is received from the first stage as input and the sensor and system are activated accordingly to collect data from the space to be interrogated.

(32) FIG. 5c illustrates an exemplary embodiment of the third stage as presented in FIG. 4a. This third stage is referred to as the analysis stage. In this stage, the data from the second stage is taken as input to generate a system of equations which is solved with the collected data and the known/estimated parameters. Once the equations are solved, then the final output is the desired properties to be measured such as volume fraction, velocity, temperature, or multi-phase imaging. For example, see the following equations:

(33) 1Volume Fraction

(34) In the formulation below, four phases are considered to be measured in volume fraction, imaging, and velocimetry. The multidimensional approach allows for more equation to be formulated for solving more phases in the imaging domain. The example here is for illustration.

(35) $C_{m}^{M 1} =_{1}^{M 1} .Math. S_{C 1}^{M 1} +_{2}^{M 1} .Math. S_{C 2}^{M 1} +_{3}^{M 1} .Math. S_{C 3}^{M 1}$ $_{m}^{M 1} =_{1}^{M 1} .Math. S_{1}^{M 1} +_{2}^{M 1} .Math. S_{2}^{M 1} +_{3}^{M 1} .Math. S_{3}^{M 1}$ $\frac{C^{M 1}}{f_{m}} =_{1}^{M 1} .Math. S_{\frac{C}{f} 1}^{M 1} +_{2}^{M 1} .Math. S_{\frac{C}{f} 2}^{M 1} +_{3}^{M 1} .Math. S_{\frac{C}{f} 3}^{M 1}_{1}^{M 1} +_{2}^{M 1} +_{3}^{M 1} +_{4}^{M 1} = \begin{matrix} 1 \\ .Math. \\ 1 \end{matrix}$
Where C.sub.m.sup.M1 is the measured capacitance vector, M is the number of capacitance data in the measured vector, .sub.1.sup.M1, .sub.2.sup.M1, .sub.3.sup.M1, and .sub.4.sup.M1 is the volume fraction of the phase 1, 2, 3 and 4 respectively. S.sub.C1.sup.M1, S.sub.C2.sup.M1, and S.sub.C3.sup.M1 is the sensitivity of capacitance pairs to a change in volume fraction in phase 1, 2, and 3 respectively. S.sub.1.sup.M1, S.sub.2.sup.M1, and S.sub.3.sup.M1 is the sensitivity of electric phase between plate pairs to a change in volume fraction in phases 1, 2, and 3 respectively

(36) $S_{\frac{C}{f} 1}^{M 1}, S_{\frac{C}{f} 2}^{M 1}, and S_{\frac{C}{f} 3}^{M 1}$
is the sensitivity of differential frequency response of capacitance between plate pairs to a change in volume fraction in phases 1, 2, and 3 respectively.
Without loss of generality, higher order derivative equations of capacitance or phase can be formulated to solve for more phases in the imaging domain. For example, second order derivative equations of the capacitance and phase for a four phase system can be formulated as:

(37) $\frac{^{2} C^{M 1}}{^{2} f_{m}} =_{1}^{M 1} .Math. S_{\frac{^{2} C}{^{2} f} 1}^{M 1} +_{2}^{M 1} .Math. S_{\frac{^{2} C}{^{2} f} 2}^{M 1} +_{3}^{M 1} .Math. S_{\frac{^{2} C}{^{2} f} 3}^{M 1}$ $\frac{^{2}^{M 1}}{^{2} f_{m}} =_{1}^{M 1} .Math. S_{\frac{^{2}}{^{2} f} 1}^{M 1} +_{2}^{M 1} .Math. S_{\frac{^{2}}{^{2} f} 2}^{M 1} +_{3}^{M 1} .Math. S_{\frac{^{2}}{^{2} f} 3}^{M 1}$
2Imaging Equations
For each phase in step 1 where the volume fraction vector has been calculated, imaging can be performed based on volume fraction step, for example, phase 1 capacitance equation, phase equation or differential frequency equation can be used to reconstruct an image of each phase. Below are examples of the capacitance, phase, and differential frequency equations through which the signal portion attributed to that phase is calculated.

(38) $C_{1}^{M 1} =_{1}^{M 1} .Math. S_{C 1}^{M 1}$ $_{1}^{M 1} =_{1}^{M 1} .Math. S_{1}^{M 1}$ $\frac{C^{M 1}}{f_{1}} =_{1}^{M 1} .Math. S_{\frac{C}{f} 1}^{M 1}$
To formulate an image based on one of those equations, one follows the typical reconstruction techniques developed and used in literature. For example, the Phase 1 can be reconstructed from the capacitance measurements following the equations below:
C.sub.1.sup.M1=.sub.1.sup.M1,S.sub.C1.sup.M1=S.sub.C1.sup.MN.sub.1.sup.1N
Following from the above,
S.sub.C1.sup.NM(.sub.1.sup.M1,S.sub.C1.sup.M1)=.sub.1.sup.1N
Where S.sub.C1.sup.MN is the pixel wise sensitivity matrix, and .sub.1.sup.1N is the image vector. On the other hand S.sub.C1.sup.M1 is the volume fraction capacitance weight of phase 1 and .sub.1.sup.M1 is the volume fraction vector of Phase 1.

(39) From this point on, reconstruction algorithms can be used similar to ECVT or AECVT reconstruction.

(40) 3Velocimetry:

(41) From step 2, a velocimetry map can be calculated for each phase based on a selected dimension. For example, using capacitance related to phase 1 velocimetry equations for phase 1:
=g.sub.x1v.sub.x1+g.sub.y1v.sub.y1+g.sub.z1v.sub.z1
v.sub.x1=g.sub.x1.sup.T.sub.1
v.sub.y1=g.sub.y1.sup.T.sub.1
v.sub.z1=g.sub.z1.sup.T.sub.1
Where .sub.1 denotes the time rate change of capacitance signal of phase 1. v.sub.x1, v.sub.y1, and v.sub.z1 are the x, y, and z components of the velocity profile of phase 1, respectively. g.sub.x1, g.sub.y1, and g.sub.z1 are the dot product between the image and the sensitivity gradient velocimetry for image vectors of the x, y, and z components of the velocity profile for phase 1.

(42) FIG. 6a illustrates an exemplary embodiment of the first stage as presented in FIG. 4b. Similar to FIG. 5a, this figure depicts the flow of the decision stage with known parameters and properties to be measured as input. However, in addition to these inputs, this stage also takes feedback from stage 3 such as further determined system parameters to better refine the decision stage in optimizing data collection configurations.

(43) FIG. 6b illustrates an exemplary embodiment of the second stage as presented in FIG. 4b. Similar to FIG. 5b, this figure depicts the flow of the measurement stage as taking the optimal configuration from stage 1 as input and collecting data accordingly.

(44) FIG. 6c illustrates an exemplary embodiment of the third stage as presented in FIG. 4b. Similar to FIG. 5c, this figure depicts the flow of the analysis stage through generating and solving equations. However, in addition to the generation and solving of equations from data collected in stage 2, the flow here includes a step for pattern recognition and data analysis for the purposes of estimating additional parameters which can be fed back into stage 1 as input to optimize the data collection configuration even further.

(45) FIG. 7a illustrates an exemplary embodiment of the component of stage 1 in FIGS. 5a and 6a which deals with estimating new system parameters from measurements obtained from the sensor. In this embodiment, the new system parameter is the geometry of the smart sensor which can change and adapt its physical configuration. By detecting the shape through geometry sensing plates, the new parameter of the sensor geometry is known and can be fed back to the step which determines which dimensions to employ in measurement for optimal data collection.

(46) FIG. 7b illustrates an exemplary embodiment of the component of stage 1 in FIGS. 5a and 6a which deals with estimating new system parameters from measurements obtained from the sensor. In this embodiment, known parameters indicate that a particular sensor geometry should be employed in data collection and mechanical mechanisms embedded in the sensor are activated to conform the sensor shape to this new geometrical configuration. This new configuration is a new parameter that can be fed back into the step which determines which dimensions to employ in measurement for optimal data collection.

(47) FIG. 8a illustrates an exemplary embodiment of the component of stage 1 in FIGS. 5a and 6a which deals with estimating new system parameters from measurements obtained from the sensor. In this embodiment, the new system parameters are frequency transition points at which other parameters change when excited at frequencies above and below. A frequency sweep can be carried out on any parameter. These transition points are a new parameter that can be fed back into the step which determines which dimensions to employ in measurement for optimal data collection.

(48) FIG. 8b illustrates an exemplary embodiment of the component of stage 1 in FIGS. 5a and 6a which deals with estimating new system parameters from measurements obtained from the sensor. In this embodiment, similar to FIG. 8a, a frequency sweep is employed to collect data for determining frequency transition points. In addition to the steps in FIG. 8a, a pattern recognition or data analysis step is employed to classify the system based on the specific frequency transition points identified. This new classification is a new parameter that can be fed back into the step which determines which dimensions to employ in measurement for optimal data collection.

(49) FIG. 9 illustrates an exemplary embodiment of the component of stage 1 in FIGS. 5a and 6a which deals with estimating new system parameters from measurements obtained from the sensor. In this embodiment, the adaptive technique for ECVT or AECVT is employed in which data is collected from an AECVT sensor and pattern recognition or data analysis helps to determine a new optimal plate activation configuration. The new optimal plate configuration is a new parameter that can be fed back into the step which determines which dimensions to employ in measurement for optimal data collection.

(50) FIG. 10 illustrates an exemplary embodiment with single excitation and receiver channels to measure capacitance and phase of ECVT or adaptive sensor segments for single capacitance measurements. This building block can be used with other circuit components to form a full system to measure multiple capacitance values of an ECVT, AECVT, or DCPT sensor system. This building block features:

(51) 1) In-phase and quadrature parallel detectors providing two orthogonal demodulations of the received signal (104). A 90 degree phase shifter (102) provides the reference signal for the quadrature detector.

(52) 2) the amplitude and phase of the detected signal as the root mean square and arctangent of the in-phase and quadrature components, respectively (106).

(53) 3) detected signal phase to represent dielectric and lossy material properties in the multi-dimensional configuration. Amplitude and phase of the detected signal in multi-dimensional configuration are compared to amplitude and phase of a calibrated signal to decouple the material in the imaging domain from its lossy and dielectric properties. The phase is used also in the DCPT mode. Both amplitude and phase are measured at different frequencies to increase available measurement dimensions for the Multi-dimensional approach.

(54) While certain embodiments of the present invention are described in detail above, the scope of the invention is not to be considered limited by such disclosure, and modifications are possible without departing from the spirit of the invention as evidenced by the following claims:

Multi-dimensional approach to imaging, monitoring, or measuring systems and processes utilizing capacitance sensors

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G01N27/228

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G01N27/226

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A61B5/05

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G06T11/003

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G01N27/22

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Abstract

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