Advanced applications in wide-area impedance sensing

Advanced Applications in Wide-Area Impedance Sensing

by

Elizabeth C. George

S.B., Massachusetts Institute of Technology (2012)

Submitted to the Department of Electrical Engineering and Computer Science

in partial fulfillment of the requirements for the degree of

Master of Engineering in Electrical Engineering and Computer Science

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MASSACHUSETTS INSTITUTE OF TECHNOLOGY

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February 2014

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Massachusetts Institute of Technology 2014. All rights reserved.

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Depar~ent of

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Electrical Eng eering and Computer Science

January 31, 2014

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Dr. Steven B. Leeb

Professor of E.E.C.S. & M.E., MacVicar Faculty Fellow

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Thesis Supervisor

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Al-Thaddeus Avestruz

Doctoral Candidate

Thesis Supervisor

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Accepted by...

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Prof. Albert R. Meyer

Chairman, Master of Engineering Thesis Committee

....

Advanced Applications in Wide-Area Impedance Sensing

by

Elizabeth C. George

Submitted to the Department of Electrical Engineering and Computer Science on January 31, 2014, in partial fulfillment of the

requirements for the degree of

Master of Engineering in Electrical Engineering and Computer Science

Abstract

In this thesis a wide-area impedance sensor used in hyperspectral imaging for a wide variety of ap-plications is presented. Building on previous work, this sensor is decoupled from fluorescent lamps and thus is used to passively detect independent sources such as fluorescent lamps and laptop con-trol signals. The active sensing electrodes with common-mode feedback are used with a spectrum analyzer and synchronous demodulator for source and material detection respectively. A setup for active source multiplexing is described and used in image reconstruction and material detection. Algorithms for image reconstruction from such ill-posed inverse problems are discussed and improve-ments are made. Hyperspectral techniques for extracting the occupancy values of various materials are shown. Finally, future experiments using this sensor for gesture sensing and as a purely digital demodulator for passive imaging are described.

Thesis Supervisor: Dr. Steven B. Leeb

Title: Professor of E.E.C.S. & M.E., MacVicar Faculty Fellow Thesis Supervisor: Al-Thaddeus Avestruz

Acknowledgements

I would like to thank everyone whose support made this thesis possible. I am extremely grateful

to my thesis supervisors, Professor Steven B. Leeb and PhD Candidate Al-Thaddeus Avestruz, for

their continued guidance and wisdom. I would also like to thank my colleagues from LEES and

friends at MIT for their help and encouragement. They include, but are not limited to, Shahriar

Khushrushahi, William Thompson, Brian Sennett, Kawin (North) Surakitbovorn, Arthur Chang,

John Donnal, Jim Paris, Shuyi James Chen and Ashley Brown.

I would finally like to thank my family, especially my parents Christine and Dan George, for

their constant support throughout my time as an undergraduate and graduate student, and beyond.

Contents

1 Introduction

15

1.2

Previous Work . . . .

16

2 Sensor Circuitry and Setup 19 2.1 Overview . . . . 19

2.2 High Voltage Source . . . . 20

2.3 Sensing Circuitry . . . . 21 2.3.1 Front-end Amplifier . . . . 21 2.3.2 Signal Conditioning . . . . 23 2.4 Circuit Changes . . . . 26 2.4.1 Signal Conditioning . . . . 26 2.4.2 Signal Synthesis . . . . 27

2.4.3

HV Selection . . . .

27

5 Imaging Techniques 5.1 Setup .... ... 5.2 T heory . . . . 5.3 Forward Problem . . . . 5.4 Inverse Problem . . . . 5.4.1 Constraints . . . .

5.4.2

CV X . . . .

5.5.1 Linear Back Projection . . . .

5.5.2 Least Norm . . . .

5.5.3 2-Norm with 2-Norm Regularization

5.5.4 1-Norm with 2-Norm Regularization

5.5.5 2-Norm with 1-Norm Regularization

5.5.6 Landweber/Newton-Raphson .

5.5.7 Image Segmentation . . . .

5.6 Improvements . . . .

5.6.1 Influence Matrix . . . .

5.6.2 Subpixel . . . .

6 Occupancy Detection Results and System Performance 6.1 Sensor Range . . . .

6.2 Noise and Drift . . . .

6.3 Object Detection . . . .

6.3.1

Scalability of Objects . . . .

6.3.2 Multiple Materials . . . .

7 Conclusion and Future Work

7.1

Sum mary . . . .

7.2

Future Work

. . . .

7.2.1

Electrode Configurations . . . . .

7.2.2

Digital Demodulation . . . .

7.2.3

Laptop Gesture Sensor . . . .

7.2.4

Floor Shoe Scanner . . . .

A Code

A.1 Sensor Interfacing Software in

43

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A.1.2 recorder.m

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66

A.1.6 BM atrix.m . . . .

78

A.1.7 AM atrixPM .m . . . .

79

A.1.8 ImSeg.m . . . .

83

A.2 Sensor Board Software . . . . 85

A.2.1

FullyDiff8.c . . . .

86

B Schematics and Boards 103 B.1 High Voltage Signal Source . . . . 104

B.1.1

Full Schematic . . . 104

B.1.2 PCB Layout . . . 105

B.1.3 Bill of M aterials . . . . 106

B.2 Standalone Sensor Electrodes . . . . 107

B.2.1 Capacitive M ode Electrodes . . . . 107

B.2.2 Resistive Mode Electrodes . . . . 111

B.3 Sensor Signal Conditioning . . . . 115

B.3.1 Full Schematic . . . . 115

B.3.2 PCB Layout . . . . 128

B.3.3 Bill of M aterials . . . . 129

B.4 Atmega32U4 Breakout Board . . . . 131

B.4.1 Full Schematic . . . . 131

B.4.2 PCB Layout

. . . .

132

List of Figures

2-1 A lumped element model for the capacitive sensor system [1]. . . . . 19

2-2 Diagram of the generalized standalone occupancy sensor. . . . . 20

2-3 _{The circuit model for the PI controller and plant. vi, is taken from the output of the}

multi-frequency source from the sensor board and v0,t drives the source plates directly. 20

2-4 The high voltage source board. . . . . 21

2-5 These figures were taken from a single frame of a digital oscilloscope. . . . . 22

2-6 A close-up of the front-end amplifier on one of the receive electrodes. . . . . 23

2-7 Schematic for the half circuit common mode feedback of the front end amplifier. Feedback capacitances represent lumped T-networks. Electrode circuitry is left of the cable and sensor board circuitry to the right. . . . . 24

2-8 Block diagram for the common mode feedback of the front end amplifier. . . . . 24

2-9 The generalized block diagram for the signal conditioning circuitry. The first gain stage is singled ended and located on each respective electrode. The following stages are all fully differential and reside on the sensor board. The dashed box is replicated four tim es. . . . . 25

2-10 One-channel circuit implementation of the synchronous demodulator. . . . . 25

2-11 The sensor board with DDS signal creation in the bottom-right corner, microcontroller breakout in the center-right and demodulation signal creation top-right. The front-end amplifier is at the input to the board on the left. The signal is delivered to the board from the active receive electrodes via driven coaxial cables to the MMCX connectors. ... ... 26

2-12 Schematic for one channel of the high voltage source multiplexer board. Three relays are stacked in series to standoff the 500 V peak voltage. The remaining two channels and shield switches are not shown. . . . . 27 2-13 Revision 2.0 of the high voltage multiplexer board. Large separation between channels

2-14 Receive electrodes placed on plastic shelves in the lab space. Sensor electronics are

beyond the shelves on the ground and source electrodes are on a table across the lab

space. ...

...

29

3-1

Block diagram of manual demodulation tuning test. . . . .

32

3-2 Gradually increasing the demodulation frequency to within 0.01 Hz of the independent

source frequency. ...

...

32

3-3 With the demodulation frequency within 0.01 Hz of the source frequency, the beat

frequency is observed over time. . . . .

33

3-4 Block diagram of manual demodulation tuning test. . . . .

33

3-5 These figures were taken from a single frame of the spectrum analyzer used with our

front-end amplifier and electrodes.

. . . .

34

3-6

These figures were taken from a single frame of the spectrum analyzer used with our

front-end amplifier and electrodes. The laptop was placed on the same shelf as the

left-side electrode.

. . . .

35

3-7

These figures were taken from a single frame of the spectrum analyzer used with our

front-end amplifier and electrodes.

. . . .

36

4-1 The conductivity and permittivity of human muscle as a function of frequency [2,3].

39

4-2 The conductivity of aluminum as a function of frequency.

. . . .

40

4-3 Difference between COMSOL-modeled human image at 100kHz and 20kHz . . . . .

40

4-4 A COMSOL-modeled test case distribution with solutions by the LBP and SVD

algorithm s.

. . . .

41

4-5 The same test case distribution with solution by the Landweber algorithms. . . . . .

41

4-6

The experimental setup and test case for hyperspectral ECT. In this test case, pixels

1 and 4 were filled with water and pixels 2 and 3 were filled with wood. On the right,

the difference between the permittivity distributions at 10kHz and 100kHz. . . . . .

42

5-1

The 4x4 grid in 2D space used as the spacial reference for all imaging. . . . .

44

5-2 Partial image of the 4x4 floor grid used for all experiments. . . . .

44

5-3 On the left, individual pixels are filled with dielectric material to calculate the values

of the sensitivity matrix. On the right, the result of the superposition of all pixels is

shown...

...

46

5-4 A pictorial representation of the linear back-project algorithm for "hard-field"

tomo-graph ies. . . . .

51

5-5

Fitting data for measurements taken at each pixel in 13x13 grid: source position 1,

6-1 _{The response of the sensor as a single source electrode is moved away from the receive}

electrodes. . . . . 58 6-2 _{The relative difference between the levels of human material calculated by the 11}

algorithms with 11 _{regularization for two people of different sizes. . . . . 60}

6-3 _{Occupancy values in the 4x4 gridspace with two people differentiated by size. . . . . 61}

6-4 _{The occupancy maps of human and metal presence in the lab space. In this case,} noise-free data is used in the sensitivity calibration and as the experimental data and a perfect detection is achieved. . . . . 62

List of Tables

2.1 Stray capacitance measurements at 100kHz . . . . 28

3.1 Lab Space Independent Source List . . . . 37

6.1 Noise Floor Measurements . . . . 59

6.2 Repeated Calibration Data . . . . 59

6.3 Multiple Object Data Pairs . . . . 59

Chapter 1

Introduction

1.1

Motivation

1.1.1

Performance objectives

The need for a simple, cheap occupancy detector and material detector has motivated the research

presented in this thesis. This sensor would take precise differential current measurements to create an

accurate depiction of the material occupancy of a given space through the use of image reconstruction

algorithms. The sensor's low SNR would make it possible to reconstruct an image of a resolution

relevant to the size of an average person.

1.1.2

Applications

Several possible applications are envisioned and detailed here. Airport body-image scanners have

un-dergone many changes in recent years, first due to legislation mandating increased security measures

at airport checkpoints following the 9/11 terrorist attacks. The introduction of full-body scanners

was followed quickly by backlash from privacy groups likening the scanners to a "virtual strip search"

with many also fearing the effects of the scanners' radiation. Many called for new legislation to

pro-tect them from such invasive technologies [4]. The capacitive sensing system described in this paper

could be used to replace existing airport x-ray scanners to provide a minimally invasive alternative

which would pick up the presence of metals, suspicious plastics used in making explosives, and other

dangerous substances.

One of the biggest areas of development in Human Interface Devices (HIDs) is in gesture

recog-nition. Capacitive touch sensor with multi-touch capabilities have been realized in many commercial

products including mousepads, smartphones and touchscreens. The research focus has shifted

to-wards methods of sensing without maintaining physical contact between the user and the device

[5].

This idea is quite new, with only a few products currently out in the market boasting this technology.

However, the current implementation which requires a dedicated camera and significant processing

power could greatly benefit from integration with a low-power solution.

A touchless capacitive gesture recognition system installed on a 12" laptop screen was proposed

and built in 2006 [6] using four long metal electrodes arranged around the screen's bezel. This system

was only capable of differentiating a few gestures, with low reliability. A similar system for low cost

gesture sensing on the centimeter scale was demonstrated in 2011 [7]. Another simple touchless

capacitive user interface using an array of small electrodes in a row to track user movement has

been implemented for use in interactive textiles for patient care [8]. Combining these technologies,

a screen using multiple arrays of electrodes around its keyboard and screen could provide refined

tracking and a larger range of gestures supported. This sensor's unique ability to distinguish between

different types of materials present in an area using hyperspectral detection has not been realized

in its current applications, and will be discussed here.

Standard room sensors detect motion, not occupancy, and cannot differentiate between the

pres-ence of a human or other objects. This sensor would present a great improvement over standard

room detectors as it could tell the relative occupancy level of a space and would be able to detect

hu-man presence when stationary. The goal of this sensor is to be able to distinguish between different

objects and materials.

To obtain detailed information about the usage of potentially inefficient loads on a utility network

can be a hard problem. Smart-metering technologies can be expensive when sensors must be installed

to monitor every load in a network. Non-Intrusive Load Monitoring (NILM) is a proposed solution

to the smart-metering problem which inserts one sensor into a network and uses smart processing

to differentiate loads from one-another. A passive capacitive scanner which takes the frequency

signature of a space with a very high frequency could perform this task in a

load-saturated

area. The

passive scanner is an exciting tool that can amplify the signals present in a room (from fluorescent

lamps to monitors refreshing) and use these signals to detect objects in the space.

1.2

Previous Work

The first iterations of this sensor were electrically coupled to fluorescent lamps. Electrodes on the

lamp would synchronously detect changes in the electric field produced by the excitation of the

fluorescent ballast and use this information to detect human presence [9,10]. The application in this

sensor was primarily in building energy management. The lamp could be turned off when human

presence was no longer detected. Previous sensors achieved capacitance measurements as low as

10fF [11].

frequency and amplitude provided the ability to perform hyperspectral imaging. Furthermore, the absence of the fluorescent lamp removes the physical limitations on the setup posed by its fixed location. The analog signal conditioning path was also improved in this iteration and the front-end amplifier was redesigned to emphasize common-mode rejection. This amplifier was relocated to the receive electrodes of the sensor to shorten the sensing node. This made the sensing node less susceptible to parasitic capacitances from an undriven cable and the environment.

1.3

Thesis Overview

With the improvements to the decoupled capacitive sensor discussed above, it is now possible to perform hyperspectral imaging on a room scale. This thesis discusses various methods for object detection using this capacitive sensor system. Image reconstruction algorithms and methods are evaluated and optimum configurations and applications for the sensor are presented.

Chapter 2 presents changes and additions to the sensor's schematic, layout and wiring since William Thompson's thesis. The use of the sensor as a passive room scanner to detect asynchronous sources, such as fluorescent lamp ballasts and laptop signals, is discussed in Chapter 3. Hyperspectral imaging techniques are introduced in Chapter 4 and image reconstruction algorithms are compared in Chapter 5. The overall system performance and results of the system tests are discussed in Chapter 6. Finally, the thesis is concluded in Chapter 7 with a discussion of the results, system limitations, and future work. The microcontroller code, Matlab code, relevant schematics and board layouts follow in the appendices.

Chapter 2

Sensor Circuitry and Setup

2.1

Overview

The circuit model shown in Figure 2-1 depicts the electrical path from the high voltage signal source to the active receive electrodes. A default capacitance Csen is observed between source and receive plates and small changes in that capacitance is measured as AC from the positive and negative

differential receive electrodes.

+AC -AC S

idl id2

Zd Zd

icrn Zern

Figure 2-1: A lumped element model for the capacitive sensor system [1].

The capacitive sensor circuitry detailed in this chapter can be summarized by the block diagram in Figure 2-2. A high voltage source converts a low voltage sinusoid from a digital synthesizer to a

200-500 V waveform to drive a source electrode. The source can be configured as single-ended or

differential. The source electrodes are driven, thus creating an electric field in the space between the source and receive electrodes. Small currents are picked up by the front-end amplifier and are converted to voltages via a transimpedance amplifier for signal processing. The signals are amplified and sent to a demodulation block which uses in-phase and quadrature demodulation signals derived

from the original source frequencies. These demodulated signals are passed through a bank of

low-pass filters to leave a dc value representing the amount of a given demodulation frequency and phase

in the input signal. This slowly-changing dc value is then sampled by an ADC and the digital values

are sent to a PC running Matlab to be processed.

Noise

Sources

HV Source +Target (s) + Receive .Sensor + PC

Source HElectrodeH Electrodes I BoardH

Figure 2-2: Diagram of the generalized standalone occupancy sensor.

Four channels of the signal conditioning circuitry are used to measure the two frequencies and

two phase components of the input signal simultaneously.

2.2

High Voltage Source

The high voltage signal source is made up of several blocks. On the sensor board itself, two DDS

chips are programmed by the Atmega32u4 microcontroller to output sinusoids of 10.4 kHz and 83.3

kHz respectively. Because both are derived from the same clock signal, they have the same phase.

The dc component is then removed and an output amplifier drives a 15 ft cable to bring this low

voltage signal to the source board across the space.

Vout

9

VIn K(Ts + 1) _ Power

Rd2

Cb

Figure 2-3: The circuit model for the PI controller and plant. vi" is taken from the output of the

multi-frequency source from the sensor board and v

t drives the source plates directly.

As is shown in Figure 2-3, the input signal is gained up by a power amplifier and

autotrans-former with a PI feedback network. An autotransautotrans-former has several advantages over a two-winding

a feedback path to be wrapped around the transformer to the PI controller. An autotransformer

also requires less copper than its equivalent two-winding transformer. Finally, the autotransformer

is more efficient electrically. It experiences lower losses through the secondary leakage impedances

than a two-winding transformer.

Figure 2-4: The high voltage source board.

2.3

Sensing Circuitry

2.3.1

Front-end Amplifier

The receive electrodes, like the source electrodes, are copper planes on a printed circuit board. The

front-end amplifiers of the differential pair are placed on the electrodes themselves to prevent against

the parasitic capacitive loading of the sensing node by an undriven cable. The amplifier on these

active electrodes is configured as a transimpedance amplifier and thus will convert an input current

to an output voltage

Vo=

-in

.

(2.1)

The input current is provided by the signal source, vin, and the sensed capacitance to the receive

electrode, Cen, by

iin = Vin = Vin " sen.

Ie~

Ru 57 II Ie Icq 22 Dec I1302III:5II 8:0II 3 II

...

Chi 200rnY M 40.0ps 5.0GS&

A Ch1 r 424mnY Curs1 POs 800.Ons Curs2 Pos 96.58ps

0i:

800.Ons

p:

984.On

a:

(a) The summed

T k. R H

sinusoids at the divided output of the high voltage source

107 A' 22~ D 1 3 71 I T-I -.. .I .. .. .-I si M 20.Oms 250kS/s 4.0psAx A Ch1 f 396mV

(b) FFT taken of the divided output of the high voltage source

Figure 2-5: These figures were taken from a single frame of a digital oscilloscope.

Curs1 POs

10.4kHz

Curs2 Pos

|-83.345kHz

1 1 4kH

Tek Run HI Res

57

02:53:03

200"

Simplifying these equations gives us the theoretical relationship between the front-end transfer

func-tion and the sensed capacitance

Vo

t

_

SRf Cen

_(2.3)

vi,,

1+sRCf

Another important aspect of this front-end is its common-mode feedback for common-mode

rejection. The electrodes are meant to measure the small changes in the differential signal between

them corresponding to a change in the occupation or location of materials in the space, and

common-mode signals must be rejected. To do so, the output from the active electrodes, given by

Vout

+

2

(2.4)

is sent to the sensor board; the common-mode signal is then extracted and sent back to the active

electrodes to be canceled. A circuit diagram is presented in Figure 2-7 and the corresponding block

diagram in Figure 2-8.

Figure 2-6: A close-up of the front-end amplifier on one of the receive electrodes.

2.3.2

Signal Conditioning

Once the signal is received and its common-mode component is rejected, it is ready for processing.

The diagram in Figure 2-9 summarizes this three to four channel signal conditioning block. The

synchronous detector used in this sensor design collects both the in-phase and quadrature data from

the input to the front-end. To do so, the input signal is multiplied separately by a clock signal and

a signal

900

out-of-phase. Collecting in-phase and quadrature information reduces the impact of

any phase error between the input signal and the demodulating signals. The synchronous detector

Cf

CS

ceive

de

__C.

IGc

Sensor Boar

vo_

Figure 2-7: Schematic for the half circuit common mode feedback of the front end amplifier. Feedback

capacitances represent lumped T-networks. Electrode circuitry is left of the cable and sensor board

circuitry to the right.

Interference

Fig 2:

B

k d

g

H

x 10

Figure 2-8: Block diagram for the common mode feedback of the front end amplifier.

Active Re

Electro

is realized with the MAX4523. This set of analog switches is configured in a full bridge topology and driven by the in-phase s and quadrature s clock signals to form a square wave multiplier as in Figure 2-10. The 900 phase shift between multiplier clock signals is generated by a chain of D flip

flops.

Vinl _G1

-Synch. Det. Instr. Amp.

G2 G3 0 & -+ & I b

LPF ADC

ii

Vin2 _G,

--x4

Figure 2-9: The generalized block diagram for the signal conditioning circuitry. The first gain stage is singled ended and located on each respective electrode. The following stages are all fully differential and reside on the sensor board. The dashed box is replicated four times.

The passive low-pass filter block following the multiplier suppresses unwanted high frequency content. The filter poles have been placed according to a compromise between settling time and multiplier ripple aliasing rejection.

Vin+ 0-V -Figu '

_VOWt,+

A

PC

---ire 2-10: One-channel circuit implementation of the synchronous demodulator.

Next the AD8230, an instrumentation amplifier, converts the differential signal to single-ended

and adds a reference voltage. It does so to shift the signal to the middle of the analog-to-digital

(ADC) converter rail. A configurable gain may also be added to make use of its full range. Finally,

the output of the instrumentation amplifier is sent to the ADC. The LTC2484 is a 24-bit E-A ADC

with an SPI interface for sampling and reading from the converter. A photo of the sensor board with

four channels (two frequencies with in-phase and quadrature information) is shown in Figure 2-11.

Figure 2-11: The sensor board with DDS signal creation in the bottom-right corner, microcontroller

breakout in the center-right and demodulation signal creation top-right. The front-end amplifier is

at the input to the board on the left. The signal is delivered to the board from the active receive

electrodes via driven coaxial cables to the MMCX connectors.

2.4

Circuit Changes

Since Version 2.0 of the sensing circuitry, there have been several changes made to cut out extraneous

hardware and add extra capabilities to the system.

2.4.1

Signal Conditioning

Along the signal conditioning path, a second differential pair of channels was added to provide the

ability to add a second pair of receive electrodes. Jumpers are used to allow either a second electrode

pair or a divided source signal input to collect drift and noise measurements.

The potentiometers at the output of the AD8230 instrumentation amplifiers were also removed

to allow for the placement of matching resistors to set the gain of this amplifier. The gain, given by

g

2

1+

(2.5)

RG

can be calculated to take advantage of the full input range of the ADC. The adjustable delay

blocks before the flip flops were also removed because they were unnecessary. The relative phase

delay between the DDS-derived source signal and the in-phase demodulation signal is unimportant

because the delay introduced between the DDS signal and the received signal is unknown. Also, when

of phase delay.

2.4.2

Signal Synthesis

In the signal synthesis block, several parts were removed or replaced to cut down the total BOM cost and reduce complexity. The resistor reference summed with the two DDS outputs in Revision 2.0 was replaced here with an op-amp buffer reference. Without the op-amp buffer, the reference (used to subtract the combined dc offset from the summed DDS outputs) would be sensitive to changes in the gain of the summing amplifier. This would mean that without the gain of 10 originally used in this circuit, a dc offset whose value is scaled by this gain would be added to the summed sine waves from the DDS.

2.4.3

HV Selection

To multiplex the three high voltage source electrodes, a National Instruments DAQ device was used with high voltage, RF relays to switch the input signal between possible combinations of electrodes. Three relays were used in series to increase the standoff voltage to 200 x 3 = 600V. This left plenty of room for the high voltage maximum amplitude of 500 V. The NI-DAQ device was controlled with a Matlab interface. Each of three digital outputs were toggled in binary order to create seven possible combinations of source electrodes, and one "off" state.

Vout

+ Vd,

VNI

Vin

Figure 2-12: Schematic for one channel of the high voltage source multiplexer board. Three relays are stacked in series to standoff the 500 V peak voltage.: The remaining two channels and shield switches are not shown.

Two issues arose while creating the final source setup. First, coupling was causing a signal to appear on source electrodes that were supposed to be switched off. Second, unwanted oscillation of

Table 2.1: Stray capacitance measurements at 100kHz

Rev.

Cnii

1.0

3 pF

2.71 pF

2.68 pF

7.3 pF

6.46 pF

6.35 pF

2.23 pF

1.97 pF

2.15 pF

2.0

0.25 pF

0.23 pF

0.23 pF

0.2 pF

0.18 pF

0.18 pF

0.08 pF

0.08 pF

0 pF

the source would occur when all electrodes were connected.

To solve these problems, several important layout and wiring changes were made. First, the

source multiplexer board was rebuilt on unplated perfboard and all components were soldered

to-gether with short pieces of wire wrap wire. Layout changes were also made. The three relays for

each channel were placed close together in series with 2" separations between each channel. The

shields of the output connectors were also switched with three relays in series and a 1" separation

was left between each channel and its respective shield. Finally, a distance of about 5" was left

be-tween the closest output and the input. With these changes made, there was an order of magnitude

improvement in stray capacitance as illustrated in Table 2.1.

Next, a ground plane in the form of a long piece of copper tape was added across the outputs of

the multiplexer board to shunt any source radiation from the input connector to the board. This is

to prevent any coupling from input to output on each channel. The new board revision is shown in

Figure 2-13.

Figure 2-13: Revision 2.0 of the high voltage multiplexer board. Large separation between channels

and input and output provided a significant reduction in coupling capacitances.

the multiplexer board through the cable carrying the digital output signals from the NI-DAQ box,

USB isolators were added between the NI-DAQ box and the computer, and the computer and the

sensor board. Ferrite beads were positioned around the circuit to suppress EMI.

The NI-DAQ box was also moved next to the high voltage source. The NI-DAQ cable was

originally fed across the room along the floor about 15', adding an unshielded coupling path for the

source signal. With the NI-DAQ box close to the source, the signal transmission path is almost

solely along an isolated, well-shielded USB cable, blocking a previous source of coupling.

2.5

Setup

There are several experimental setups used in this thesis. The primary configuration of electrodes

in the lab space is shown in Figure 2-14

Figure 2-14: Receive electrodes placed on plastic shelves in the lab space. Sensor electronics are

beyond the shelves on the ground and source electrodes are on a table across the lab space.

Chapter 3

Passive Scanning

3.1

Analog Demodulation

When used without an active source, this system becomes a "passive scanner." By sweeping the

demodulation frequency, the contribution of asynchronous, independent sources within the frequency

range of the sensor (such as fluorescent lamps, laptop drives, etc.) can be determined. Unfortunately,

there are several issues with using an asynchronous demodulation signal. These issues and several

solutions are discussed here.

In the test depicted by Figure 3-1, an independent source was created using our source boards and

a function generator. Because this source is not synchronous with the detection circuit, it provides

a way to observe and study the effect of a controlled asynchronous source on the sensor. This source

was placed across the room and its signal was then picked up by our receiver and demodulated

using a separate function generator. The demodulation frequency was tuned by hand to be closest

to the source frequency, as in Figure 3-2. Here, only the difference in frequency between the source

and receive frequencies matters as all phase information is contained in the IQ measurements. The

difference or "beat" frequency between the independent source and demodulation frequencies is

defined as

A

A cos(2rrfit)

A cos(2rrf

t) =2 (cos(2rr(fi + f

)t) + cos(2rr(fi

f

)t))

(3.1)

where the difference frequency is given as

fbeat = Ifi -

f21.

(3.2)

The bandwidth of the low-pass filter after the demodulation block is not low enough to attenuate

this beat frequency so it is passed to the input of the ADC. Therefore, the digital output of the

vi(s)

v,.(S)

0 Source EActrives _Amplifier

User variable in-put demodulation

frequency from signal generator

Figure 3-1: Block diagram of manual demodulation tuning test.

ADC is no longer entirely DC as shown in Figure 3-3.

a) a) L) 2.7

2.6

2.5

2.2

2.1

2

1.9

1.8

x

10-

Tuning Demodulation Frequency to Independent Source

0

20

30

40

50

60

Time (s)

Figure 3-2: Gradually

source frequency.

increasing the demodulation frequency to within 0.01 Hz of the independent

Another factor effecting the ability to precisely demodulate an asynchronous source such as a

fluorescent lamp is source frequency drift. Fluorescent lamp switching frequencies in the laboratory

space range from 26 kHz to 49 kHz and have exhibited frequency drifts of at least t 1 Hz. It becomes

very difficult to demodulate a drifting source without a synchronous receive signal.

3.2

Spectrum Analyzer

A real-time spectrum analyzer (RTSA) was used with our active electrodes and front-end amplifiers

to analyze the frequency content of the lab space. Table 3.1 shows the various distinct peaks we

-. -. . . . -.

-. ..-. ....-. .... -. .

0) 0) 0 0) 0) 0 0) 0) 0) 0~ C

6

5

4

3

2

1

0

-1

-2

-31

0

X 10

Difference Frequency with Independent Source

400kHz

200

400

600

800

1000

Time (s)

Figure 3-3: With the demodulation

frequency is observed over time.

1200

1400

1600

1800

frequency within 0.01 Hz of the source frequency, the beat

space and by approaching each bank with a oscilloscope probe with pick-up loop the peak size at

each frequency was matched with its physical location in the room.

The spectrum analyzer used was the Tektronix RSA3303A 3GHz Real-time Spectrum Analyzer.

All figures below were captured in single-shot trace mode.

Vi

Source Acl tives Amplifier

Figure 3-4: Block diagram of manual demodulation tuning test.

3.3

Applications

This "passive scanner" could be an excellent companion to previous non-intrusive load monitoring

capabilities developed by Professor Steven Leeb. Our sensor provides the ability to observe the

frequency characteristics and amplitude of various high voltage electrical sources in an area with no

electrical contact required. By replacing the spectrum analyzer in this system with the electronics

described in Section

7,

we would avoid the problems of asynchronous detection described above.

Additionally, this scanner could be used in conjunction with our hyperspectral EIT techniques.

By locking onto one of these sources and observing changes in signal amplitude when various objects

.. .. . . . ... .. . .... . .. .. .

.. . . . .

...

.... ...

..-

.

...

Power Spectrum Fluorescent Lamp

...

.

.

-.

... ..

-30

--50

-.-.--9

0..

... ..

...

.. .... . ..

. .

-110

(a) Left Fluorescent Lamp at 26.54 kHz, RBW = 5 Hz

Power Spectrum Fluorescent Lamp

4 4.12 4.24 4.36 4.48 4.6 4.72 4.84 4.96 5.08 5.2

Frequency (Hz)

x 104 (b) Front and Back Fluorescent Lamps over 41-49 kHz, RBW = 10 Hz

Figure 3-5: These figures were taken from a single frame of the spectrum analyzer front-end amplifier and electrodes.

used with our

E 0 0L

-10

-30

-40

-50

--70

-80

--90

-Mn

...

...

...

-.

..

.

.

.

.

.

.

....

--

....

-

. ...

.. .

-..

En M CD CL En M0 CD

Power Spectrum Laptop Touchpad

-70 -75 . --80 - - --85 - -.--90 --95 -100 - --105 -110 -.--115 ---1 5 -40 -50 -60 -70 -80 -90 -100 .2 5.21 5.22 5.23 5.24 5.25 5.26 5.27 5.28 5.29 5. Frequency (Hz) x 10

(a) Laptop Touchpad Signal at 52.53 kHz, RBW 10 Hz

Power Spectrum Laptop Hard Drive

....-.

.--.

... ..-.

..

-.

.

.

-.

..-.

..-

--

..

.-.

-.

--.

.... .... ..

.. ... .

-.

.

.

..

Figure 3-6: These figures were taken from a single frame of the spectrum analyzer used with our front-end amplifier and electrodes. The laptop was placed on the same shelf as the left-side electrode.

Power Spectrum Second Harmonics -50 -55 - --60 -- ----65

---T

---

-7 0

..

...

...

.

..

. .

-..-.-.-

.

-~-80

8.35

8.7801

(a) Fluorescent Lamps Second Harmonics, RBW= 50 Hz

Power Spectrum Third Harmonics -G0 -55 -60 -.-.-.--65

T

23

(b) Fluorescent Lamps Third Harmonics, RBW 50 Hz

Figure 3-7: These figures were taken from a single frame of the spectrum analyzer used with our front-end amplifier and electrodes.

Table 3.1: Lab Space Independent Source List Frequency (kHz) Power (dBm) Identity 26.54 -39.5 Left Lamp

42.09

-42.6

Back Lamp

47.13-48.73

~-44

Front Lamp

68.77

-43.17

Laptop HDD

are introduced into the lab space, the same image reconstruction could be performed without the

need for our own HV source. Preliminary experiments showed a roughly 10dB change in signal of

the closest fluorescent lamp as a person moved along the 2 meter horizontal marker in front of the

receive electrodes.

Chapter 4

Hyperspectral Imaging

4.1

Background

Many products and systems have used capacitive technologies for touchless sensing as discussed

in Chapter 1. However, this technology's unique ability to distinguish between different types of

materials present in an area using hyperspectral detection has not been realized in its current

ap-plications. This ability stems from the different permittivity responses as a function of frequency in

different materials, as shown in Figures 4-2 and 4-1. It is clear that while the conductivity of muscle

changes by orders of magnitude across this frequency range, the conductivity of aluminum is

rela-tively constant. Similarly, the relative permittivity of muscle changes dramatically with frequency,

while the relative permittivity of metals is 1 and is not frequency dependent. To take advantage of

this property, we drive our ECT system with several frequencies and are able to differentiate humans

from metal in a 100 ft2 area.

10"

10-0.3

10

103

10

10

10

106

Frequency (Hz)

Figure 4-1: The conductivity and permittivity of human muscle as a function of frequency [2,3].

Image reconstruction techniques are then used to locate objects. Ideally, the images produced

are sparsely populated in our 16-pixel space; this reduces the demands on reconstruction and allows

for accurate detection and differentiation of a small number of different objects in a larger space.

5.96

5.94

>

5.92

8

5.88

103

104

105

106

Figure 4-2: The conductivity of aluminum as a function of frequency.

In this chapter, we present an ECT system operated at several frequencies which not only pro-duces a reconstructed image of the space occupancy, but provides information about the material composition of occupied areas.

4.2

Previous Work

First, a COMSOL Multiphysics model for the 2D system was created and capacitance data was derived as in a traditional ECT system described in Section 5.2. Matlab simulations were run then for various image reconstruction algorithms for use in an ECT system. These algorithms were compared using COMSOL test cases for interesting permittivity distributions. Figure 4-3 shows the modeled difference between the distributions of a test case of a human at two difference frequencies. Clever manipulations of the permittivity vs. frequency responses of different materials can lead to the differentiation of these materials when many types occupy one region.

X

-010

10 3

1 2 3 4 5 6 7 8 9 101

Figure 4-3: Difference between COMSOL-modeled human image at 100kHz and 20kHz To verify this, a simple proof-of-concept experiment was performed with help from post-doctoral

Original

2i

'IL

Table 1: Case 1: c, 80

LBP

Image Error: 0.962, Capacitance Residual: 1.0047, CC: 0.2103 Image Error: 1.0282, Capacitance Residual: 1.0074, CC: 0.14%

SVD

Figure 4-4:

rithms.

Image Error: 0.7802, Capacitance Residual: 0.99M5, CC: 0.7363 1 Image Error: 0.9680, Capacitance Residual: 0.9996, CC: 0.8261

A COMSOL-modeled test case distribution with solutions by the LBP and SVD

algo-Image Error: 0.7676, Capacitance Residual: 0.9978 CC: 0.9347 algo-Image Error: 0.9787, Capacitance Residual: 0.9996, CC: 0.7404

box shielded by grounded aluminum foil. Inside, four plastic boxes represented four "pixels" which

could be filled with air (empty), distilled water or solid wood blocks. The box containing the pixels

was surrounded by four copper foil electrodes. Using a impedance analyzer, the capacitances between

each pair of electrodes were found. With the capacitance matrix and sensitivity matrix construction

described in Section 5.2, the inverse problem involving these matrices was solved to produce the

distribution shown in Figure 4-6b. In this experiment pixels 1 and 4 were filled with water and

pixels 2 and 3 were filled with wood. By taking measurements of the discrete distribution at two

different frequencies, 10 kHz and 100 kHz, the location of the water in the setup could be reproduced

by using the information from both data sets in the inverse problem solution.

G(100kHz)-G(10kHz) 0.M 0 -0.02 -OM raw# 3

(b) Resulting difference between

per-(a) Experimental test case

mittivity distributions

Figure 4-6: The experimental setup and test case for hyperspectral ECT. In this test case, pixels 1

and 4 were filled with water and pixels 2 and 3 were filled with wood. On the right, the difference

between the permittivity distributions at 10kHz and 100kHz.

Chapter 5

Imaging Techniques

This chapter will discuss the theory of tomographic imaging and the algorithms employed in this research. Given prior knowledge about the nature of the of the space, it is much easier to narrow down the possible solutions to an ill-posed inverse problem and pick a "most-likely" solution.

5.1

Setup

First, an experimental setup was constructed so that absolute position of objects in the space could be precisely recorded. Between the two rows of electrodes (three source plates and one differential pair of receive plates) the floor tiles formed a 4 x 4 grid. Each group of 4 tiles formed a square and each square marked a pixel in the vector representing the occupancy of the space. Objects were positioned in the middle of each square while data was collected.

5.2

Theory

In this section, the theory behind conventional electrical capacitance tomography (ECT) systems is described. Our approach to this problem is to abstract away the physics and use a binary occupancy value that will be described in Section 5.3.

To derive the capacitances between two electrodes relative to ground, we can first express the charge on each electrode as a linear combination of the voltages on all electrodes. The coefficients of these combinations are known as the coefficients of capacitance, and can be expressed in terms of the circuit capacitances. In a system with two electrodes, the measured capacitances of the circuit

X direction

_____ p

!

x

X

S1

Figure 5-1: The 4x4 grid in 2D space used as the spacial reference for all imaging.

Figure 5-2: Partial image of the 4x4 floor grid used for all experiments.

0

x1

X

are related to the charges and voltages of the electrodes in the following way:

Q1 = c₁1V₁

+

(5.1)

Q2 = c21

V

+

V

(5.2)

Because of the symmetry of the problem, C₁₂ = C21, meaning that there are only three unique

coefficients. Q1 = C

+

(CIG

Q2

C2G2

C

C12)2

(5.4)

+

012

(5.5)

(5.6)

012

(5.7)

Finally, it becomes clear that the coefficients of self-capacitance, cil etc., provide no new infor-mation about the system and thus are generally ignored in our calculations.

The capacitance is measured by sending an ac signal to the source electrode and grounding the receive electrode. The induced current from the receive electrode is proportional to the actual capacitance between the two plates I, = jwVC, including any intervening dielectric material.

To retrieve the original permittivity distribution from these capacitance measurements, it is necessary to solve an inverse problem. An inverse problem is one that requires a solution for x in the matrix equation Ax = b. With an overdetermined or underdetermined system, this problem becomes impossible to solve exactly because A has no inverse. Indeed, ECT most often presents underdetermined problems because of the nature of the setup. In ECT systems, the unknown values are the permittivitties of each pixel in the desired image and the known values are the capacitances measured as detailed above. Thus, the inverse problem can be described by the equation Sg = c where c is a vector containing all capacitance measurements, g is the vector of unknown permittivities and S is a sensitivity coefficient matrix which linearly relates these values. To calculate the sensitivity matrix for a particular configuration, a function of the space and range of permittivities desired, the pixels of the space are individually filled with blocks of the material one wishes to sense. Solving for each value in the sensitivity matrix individually as in this 3 pixel, 2 electrode system gives

12

S

-I

Aci

AE

Here, the complete result is a superposition of the results of filling each pixel with a dielectric

permittivity. The configuration is first measured "empty" and a displacement current I, is observed.

Subsequently, each pixel is filled one-by-one and the changes in current corresponding to changes in

capacitance are recorded and used to fill in the sensitivity matrix: Im

1, = jWV1AC1

Once the

sensitivity matrix for a given configuration is calculated, an inverse problem, as described above, must

be solved to find the permittivity distributions from the sensitivity matrix and given capacitance

measurements. Various methods of choosing these distributions for underdetermined systems are

discussed in Section 5.5.

A6

]QlaX

A1

[AC

I

2

Ac

AE

Ad

jrol AC

(b) Superposition of all pixels

filled

Figure 5-3: On the left, individual pixels are filled with dielectric material to calculate the values of

the sensitivity matrix. On the right, the result of the superposition of all pixels is shown.

5.3

Forward Problem

In our forward model, we would like to abstract away the physics described above. It is easier to

etc. of an object in the space respectively. Thus, each x value represents a value we will henceforward

call the "occupancy" of each pixel, which is a function of many parameters. Our goal is detection,

defined as the ability to determine which pixels in a space are occupied, and eventually, by which

material. Here we will focus solely on detection of occupancy. In Chapter 4 material detection will

be discussed.

The linear system relating system voltage measurements and occupancy is given by

Ax = y

with

xi

c

0,

1

(5.8)

(5.9)

where A is an m x n sensitivity matrix, x is the

n

x 1 pixel occupancy vector and y is the m x 1

measurement vector. Specifically, in our experiment as described in Section 2.5

a₁,₁ a2,1 a3,1 m,1 a₁,₂ a₂,2 a₃,2 am, a₁,₃ a₂,3 a₃,3 am,1 -. - l,n ... a2,n ... a3,n ... _am,1 X2

22

/

The A matrix is filled column-by-column from the calibration measurements using single pixel oc-cupancy; i.e. if only pixel x1 is filled, the equation becomes

C

amx

3m

0

0

0

Y1

where aimx is one vector of calibration measurements. From the above equation, it becomes clear

that

a

=

Q.

In our experimental setup, m = 28 and n = 16 in our 4 x 4 grid. The 28 measurements

taken are in-phase and quadrature measurements at each of two frequencies and seven possible

source combinations (2 x 2 x 7 = 28). All combinations of the three source electrodes are expressed

in binary below with a "0" designating a source electrode which is undriven and a "1" representing