# US 11,079,502 B2 · Method and apparatus for resolving signals in data

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- Number: US 11,079,502 B2
- Document ID: US11079502B2
- Status: Granted
- Kind: B2
- Application number: 15/102,852
- Filed: 2014-12-11
- Published: 2016-10-27
- Granted: 2021-08-03
- Earliest claimed: 2013-12-11
- Assignees: Southern Innovation International Pty Ltd
- Applicants: Southern Innovation International Pty Ltd.
- Inventors: Paul Andrew Basil Scoullar; Christopher Charles McLean; Shane Michael Tonissen
- Web page: https://patentgrep.com/patent/US11079502B2

## CPC

- G01T 1/366: {with semi-conductor detectors}
- G01T 1/171: {Compensation of dead-time counting losses}
- G06F 17/148: {Wavelet transforms}
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06F 2111/10: Numerical modelling
- G01T: MEASUREMENT OF NUCLEAR OR X-RADIATION (radiation analysis of materials, mass spectrometry G01N23/00; tubes for determining the presence, intensity, density or energy of radiation or particles H01J47/00)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method and apparatus for resolving individual signals in detector output data are disclosed. One inventive aspect includes a processing circuit configured to receive detector output data wherein the detector output data may be stepped data or non-stepped data; transform the detector output data to produce stepped data wherein the detector output data is received as non-stepped data; detect at least one signal at least partially based on the stepped data; and estimate a parameter associated with the signal, wherein estimating the parameter may preferably comprise estimating a signal energy or signal time of arrival associated with the signal.

## Claims

### Claim 1

An apparatus for identifying and measuring individual radiation particles comprising:
a radiation detector configured to receive a plurality of individual radiation particles and generate detector output data in the form of a stream of digital samples, the detector output data comprising a plurality of individual pulse signals corresponding to the individual radiation particles, some of which are overlapping, each of the individual pulse signals having a pulse shape with a rising edge and a falling edge above a baseline; and
a processing circuit configured to:
receive the detector output data;
transform the detector output data such that the rising edge of each of the individual pulse signals is compressed in the time domain and the falling edge of each of the individual pulse signals is extended in the time domain so as to produce digital stepped data comprising a transformed stream of samples approximating an ideal step function, by modeling each of the individual pulse signals using a falling edge variable and a rising edge variable, transforming the rising edge variable such that the rising time of each of the individual pulse signals is substantially zero, and transforming the falling edge variable such that the falling time of each of the individual pulse signals is substantially infinite, wherein the digital stepped data is produced without having previously determined an energy of each of the individual pulse signals;
identify each of the individual pulse signals using the digital stepped data; and
determine one or more of the energy or the time of arrival of each of the identified individual pulse signals using the digital stepped data,
wherein the processing circuit is further configured to transform the detector output data using the following operation:
y(k)=y(k−1)+x(k)−x(k−1)·(e−a+e−b)+x(k−2)·e−a·e−b
where:
x(n) is a value of the received detector output data stream at sample ‘n’,
a, b determine the falling and rising edges of the pulse shape with a double exponential waveform, and
y(n) is the digital stepped data.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the processing circuit is further configured to:
determine the energy of each of the individual pulse signals using the difference between an average value of the digital stepped data following the signal and an average value of the digital stepped data prior to the signal.

### Claim 3 (depends on 2)

The apparatus of claim 2, wherein the processing circuit is further configured to:
generate a model of the detector output data using the energy of each of the individual pulse signals;
determine the difference between the model and the detector output data; and
reject portions of the detector output data that differ from the model by more than a threshold.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the double-exponential waveform is given by:
p(t)=A·(e−at−e−bt)
where:
p(t) is the individual pulse signal,
A is the energy of the individual pulse signal,
a is the rising edge variable, and
b is the falling edge variable.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the processing circuit is further configured to delete at least one of the samples in the digital stepped data within a window surrounding the rising edge of one of the individual pulse signals.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the processing circuit is further configured to identify each of the individual pulse signals by:
determining one or more detection metrics, wherein the one or more detection metrics are determined by fitting an expected signal shape to one or more sliding windows of samples in the digital stepped data, and estimating a parameter fit between the expected signal shape and the digital stepped data for each window;
analyzing one or more of the detection metrics; and
determining if there are any signals in the digital stepped data using said analysis of the one or more detection metrics.

### Claim 7 (depends on 6)

The apparatus of claim 6, wherein the processing circuit is further configured to analyze one or more of the detection metrics by:
comparing one or more detection metrics to a threshold; or,
modeling one or more of the detection metrics with a quadratic approximation applied to one or more sliding windows of the detection metric data, and comparing quadratic coefficients of a plurality of sliding windows of the detection metric.

### Claim 8 (depends on 6)

The apparatus of claim 6, wherein the processing circuit is further configured to determine the one or more detection metrics using a least-squares fit of the expected signal shape with the digital stepped data, where the detection for a sample k is given by:
{circumflex over (x)}(k)=(AT·A)−1·AT·b(k)
where: k is the sample,
x(k) is the detection metric for the sample k,
b(k) is a segment of the stepped data, centered on the sample k, and
A is a centered vector of the expected signal shape.

### Claim 9 (depends on 6)

The apparatus of claim 6, wherein the processing circuit is further configured to incrementally update the one or more detection metric from a detection metric for an earlier sample using addition, subtraction, and/or scaling.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein the processing circuit is further configured to discard signals which are separated from each of the individual pulse signals in the digital stepped data by less than a threshold number of samples.

### Claim 11 (depends on 1)

The apparatus of claim 1, wherein the processing circuit is further configured to transform the detector output data using convolution of a finite impulse response filter (FIR) with the detector output data after integration.

### Claim 12

A method for identifying and measuring individual radiation particles comprising:
receiving a plurality of individual radiation particles in a radiation detector and generating detector output data in the form of a stream of digital samples, the detector output data comprising a plurality of individual pulse signals corresponding to the individual radiation particles, some of which are overlapping, each individual pulse signal having a pulse shape with a rising edge and a falling edge above a baseline;
receiving the detector output data;
transforming the detector output data such that the rising edge of each individual pulse signal is compressed in the time domain and the falling edge of each individual pulse signal is extended in the time domain so as to produce digital stepped data comprising a transformed stream of samples approximating an ideal step function, by modeling each of the individual pulse signals using a rising edge variable and a falling edge variable, transforming the rising edge variable such that the rising time of each of the individual pulse signals is substantially zero, and transforming the falling edge variable such that the falling time of each of the individual pulse signals is substantially infinite, wherein the digital stepped data is produced without having previously identified each of the individual pulse signals and without having previously determined an energy of each of the individual pulse signals;
identifying each of the individual pulse signals using the digital stepped data; and
determining one or more of the energy or the time of arrival of each of the identified individual pulse signals using the digital stepped data,
wherein the transforming of the detector output data is performed using the following operation:
y(k)=y(k−1)+x(k)−x(k−1)·(e−a+e−b)+x(k−2)·e−a·e−b
where:
x(n) is a value of the received detector output data stream at sample ‘n’,
a, b determine the falling and rising edges of the pulse shape with a double exponential waveform, and
y(n) is the digital stepped data.

### Claim 13 (depends on 12)

The method of claim 12, further comprising:
determining the energy of each of the individual pulse signals using the difference between an average value of the digital stepped data following the signal and an average value of the digital stepped data prior to the signal.

### Claim 14 (depends on 12)

The method of claim 12, further comprising:
generating a model of the detector output data using the energy of each of the individual pulse signals;
determining the difference between the model and the detector output data; and
rejecting portions of the detector output data that differ from the model by more than a threshold.

### Claim 15 (depends on 12)

The method of claim 12, further comprising:
deleting at least one of the samples in the digital stepped data within a window surrounding the rising edge of one of the individual pulse signals.

### Claim 16 (depends on 12)

The method of claim 12, further comprising:
determining one or more detection metrics, wherein the one or more detection metrics are determined by fitting an expected signal shape to one or more sliding windows of samples in the digital stepped data and estimating a parameter fit between the expected signal shape and the digital stepped data for each window;
analyzing one or more of the detection metrics; and
determining if there are any signals in the digital stepped data using said analysis of one or more of the detection metrics.

### Claim 17 (depends on 16)

The method of claim 16, wherein the step of analyzing the one or more detection metrics comprises:
comparing one or more of the detection metrics to a threshold; or,
modeling one or more of the detection metric with a quadratic approximation applied to one or more sliding windows of the detection metric data, and comparing quadratic coefficients of a plurality of sliding windows of the detection metric.

### Claim 18 (depends on 16)

The method of claim 16, wherein the step of determining one or more detection metrics further comprises:
determining the one or more detection metrics using a least-squares fit of the expected signal shape with the digital stepped data, where the detection metric for a sample k is given by:
{circumflex over (x)}(k)=(AT·A)−1·AT·b(k)
where: k is the sample,
x(k) is the detection metric for the sample k,
b(k) is a segment of the digital stepped data, centered on sample k, and
A is a centered vector of the expected signal shape.

### Claim 19 (depends on 16)

The method of claim 16, further comprising:
incrementally updating the one or more detection metrics from a detection metric for an earlier sample using addition, subtraction, and/or scaling.

### Claim 20 (depends on 12)

The method of claim 12, wherein the double-exponential waveform is given by:
p(t)=A·(e−at−e−bt)
where:
p(t) is the individual pulse signal,
A is the individual pulse signal energy,
a is the rising edge variable, and
b is the falling edge variable.

### Claim 21 (depends on 12)

The method of claim 12, wherein the step of transforming the detector output data is performed using convolution of a finite impulse response filter (FIR) with the detector output data after integration.

### Claim 22

An apparatus, comprising:
means for receiving a plurality of individual radiation particles and generating detector output data in the form of stream of digital samples, the detector output data comprising a plurality of individual pulse signals corresponding to the individual radiation particles, some of which are overlapping, each individual pulse signal having a pulse shape with a rising edge and a falling edge above a baseline;
means for receiving the detector output data;
means for transforming the detector output data such that the rising edge of each individual pulse signal is compressed in the time domain and the falling edge of each individual pulse signal is extended in the time domain so as to produce digital stepped data comprising a transformed stream of samples approximating an ideal step function, by modeling each of the individual pulse signals using a rising edge variable and a falling edge variable, transforming the rising edge variable such that the rising time of each of the individual pulse signals is substantially zero, and transforming the falling edge variable such that the falling time of each of the individual pulse signals is substantially infinite, wherein the digital stepped data is produced without having previously identified each of the individual pulse signals and without having previously determined an energy of each the individual pulse signals;
means for identifying each of the individual pulse signals using the digital stepped data; and
means for determining one or more of the energy or the time of arrival of each of the identified individual pulse signal using the digital stepped data,
wherein the means for transforming is further configured to transform the detector output data using the following operation:
y(k)=y(k−1)+x(k)−x(k−1)·(e−a+e−b)+x(k−2)·e−a·e−b
where:
x(n) is a value of the received detector output data stream at sample ‘n’,
a, b determine the falling and rising edges of the pulse shape with a double exponential waveform, and
y(n) is the digital stepped data.

### Claim 23 (depends on 22)

The apparatus of claim 22, further comprising:
means for determining the energy of each of the individual pulse signals using the difference between an average value of the digital stepped data following the signal and an average value of the digital stepped data prior to the signal.

### Claim 24 (depends on 22)

The apparatus of claim 22, further comprising:
means for generating a model of the detector output data using the energy of each of the individual pulse signals;
means for determining the difference between the model and the detector output data; and
means for rejecting portions of the detector output data that differ from the model by more than a threshold.

### Claim 25 (depends on 22)

The apparatus of claim 22, further comprising:
means for deleting at least one of the samples in the digital stepped data within a set window surrounding the rising edge of one of the individual pulse signals.

### Claim 26 (depends on 22)

The apparatus of claim 22, wherein the means for identifying each individual pulse signal further comprises:
means for determining one or more detection metrics, wherein the one or more detection metrics are determined by fitting an expected signal shape to one or more sliding windows of samples in the digital stepped data and estimating a parameter fit between the expected signal shape and the digital stepped data for each window;
means for analyzing one or more of the detection metrics; and
means for determining if there are any signals in the digital stepped data using said analysis of one or more detection metrics.

### Claim 27 (depends on 26)

The apparatus of claim 26, wherein the means for analyzing one or more of the detection metrics further comprises:
means for comparing one or more detection metrics to a threshold; or,
means for modeling one or more of the detection metrics with a quadratic approximation applied to one or more sliding windows of the detection metric data, and comparing quadratic coefficients of a plurality of sliding windows of the detection metric.

### Claim 28 (depends on 26)

The apparatus of claim 26, wherein:
means for determining the one or more detection metrics is uses a least-squares fit of the expected signal shape with the digital stepped data, where the detection metrics for a sample k is given by:
{circumflex over (x)}(k)=(AT·A)−1·AT·b(k)
where: k is the sample,
x(k) is the detection metric for sample k,
b(k) is a segment of the digital stepped data, centered on sample k, and
A is a centered vector of the expected signal shape.

### Claim 29 (depends on 26)

The apparatus of claim 26, further comprising:
means for incrementally updating the one or more detection metrics from a detection metric for an earlier sample using addition, subtraction, and/or scaling.

### Claim 30 (depends on 22)

The apparatus of claim 22, wherein the double-exponential waveform is given by:
p(t)=A·(e−at−e−bt)
where:
p(t) is the individual pulse signal,
A is the individual pulse signal energy,
a is the rising edge variable, and
b is the falling edge variable.

### Claim 31 (depends on 22)

The apparatus of claim 22, wherein the means for transforming the detector output data uses convolution of a finite impulse response filter (FIR) with the detector output data after integration.