Patent Publication Number: US-2022230049-A1

Title: Systems and methods for identifying anomalous nuclear radioactive sources

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims priority to U.S. Provisional Application No. 63/108,006, entitled “SYSTEMS AND METHODS FOR IDENTIFYING ANOMALOUS NUCLEAR RADIOACTIVE SOURCES,” and filed on Oct. 30, 2020. The entire contents of the above-listed application are hereby incorporated by reference for all purposes. 
    
    
     TECHNICAL FIELD 
     This disclosure is directed to systems and methods for radiation detection, and more particularly to compact and efficient systems and methods for identification of anomalous nuclear radioactive sources. 
     BACKGROUND 
     Anomalous radioactive source detection is a critical concept for various areas of concern such as public health, border security, and overall national security. However, previous attempted techniques generally require high detector output spectrum (e.g., greater than 125 counts per second) and post-processing of the data using a central processing unit (CPU) or general processing unit (GPU) machine. Indeed, such conventional hardware includes CPU-based or GPU-based equipment. 
     Fast evolution of machine learning (ML) methodologies has encouraged industry and research to explore hardware implementation beyond CPUs and GPUs. Consequently, neuromorphic computing is experiencing a resurgence to overcome efficiency bottlenecks of conventional parallel computing and will be the platform of choice for applications requiring small size, low weight, low power (SWaP) and fast computing. The design and development of components and architectures whose functions are simulating the brain&#39;s spiking neural network (SNN) are the fundamental aspects of neuromorphic computing. For the past few years, multiple large-scale neuromorphic platforms have been developed and tested. However, these platforms are costly to construct, rely on proprietary hardware, and are not readily accessible to most of the community. 
     Thus, there remains a need for improved isotope identification including compact portable devices having low power, fast processing e, and capability to process extremely sparse data. 
     SUMMARY 
     Implementations of the disclosed technology are generally directed to systems and methods for real-time monitoring of the sparse detector output in high radiation background for isotope identification. Implementations may include compact, portable, and low-power electronics that can process extremely sparse data with fast processing time for anomaly detection. In certain implementations, an unmanned aerial vehicle (UAV) may be embedded with such a detector, which would be useful for radiation detection without unnecessary exposure to an operator. 
     Implementations may include a field-programmable gate array (FPGA)-based neuromorphic architecture that can be utilized for fast anomaly detection. Anomaly detection may be based on recognizing grayscale two-dimensional (2D) image data in which pixel intensity represents the counts in each channel. 
     Implementations may include a neuromorphic architecture that includes a fully parallel neural network with a chain of identical neurons that can learn and recognize the input information processed as different patterns. Each neuron may be used to store a prototype vector. The neurons may be fully connected through a parallel bus that could have bi-directional communication for write and readout. Each neuron may have the ability to learn and recall their pattern spontaneously without any supervision, and recognize the incoming signal by autonomously evaluating the distance between the reference patterns stored in their memory and input vectors. If this distance falls within a range called the active influence field, for example, the neuron may fire and return a decision that may consist of the distance, category, and neuron identifier. The system may advantageously learn the signature of a detector and differentiate the anomaly source from background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an example of a system for identifying anomalous nuclear radioactive sources in accordance with certain implementations of the disclosed technology. 
         FIG. 2  is a flow diagram illustrating an example of a method for identifying anomalous nuclear radioactive sources in accordance with certain implementations of the disclosed technology. 
         FIG. 3  is a block diagram illustrating an example of a neuromorphic architecture having a fully parallel neural network with a chain of identical neurons that can learn and recognize the input information processed as different patterns. 
         FIG. 4A  illustrates an example of three-dimensional (3D) histograms of seven detected background spectra with channel number in x-axis, counts in y-axis, and time in z-axis. 
         FIG. 4B  illustrates an example of the 3D histograms of the seven simulated source spectra in same time with those of  FIG. 4A . 
         FIG. 4C  illustrates an example of the seven spectra of the background plus the source in 3D histograms. 
         FIG. 5A  illustrates an example of spectra from 20-140 channels. 
         FIG. 5B  illustrates an example of spectra from 1024 channels. 
         FIG. 6  illustrates an example of 1024 spectra presented by a 2D grayscale image. 
     
    
    
     DETAILED DESCRIPTION 
     Machine learning methodologies such as support vector machine (SVM), kernel-based gaussian process, artificial neural network (ANN) and convolutional neural network (CNN) have been expanding the field in radiation detection. These advances have encouraged industry and research to explore hardware implementation beyond CPU and GPU-based systems. As Moore&#39;s law continues to reach its limitation, the spiking neural network (SNN) inspired by the human brain aims to emulate a realistic neural network model in biology, which can improve the computing efficiency, compactness, and power consumption performance. The main structure of the neural network in the human brain consists of neurons and synapses where the most important characteristics include neuron spiking and synaptic plasticity. Simulating brain and SNN from fundamental hardware scale is the idea of neuromorphic computing. 
     Implementations of the disclosed technology generally include a field-programmable gate array (FPGA)-based neuromorphic architecture and spiking neural network (SNN) that can be used for radiation anomaly detection. Such implementations may include compact, portable, and low-power electronics that can process extremely sparse data with fast processing time for anomaly detection. These implementations may be physically small, readily portable, and use reduced power and, as such, generally do not require saving large amounts of data, and cloud computing may be used for processing. For example, an unmanned aerial vehicle (UAV) may be embedded with such a detector for radiation detection without subjecting an operator to unnecessary radiation exposure. 
       FIG. 1  is a block diagram illustrating an example of a system  100  for identifying anomalous nuclear radioactive sources in accordance with certain implementations of the disclosed technology. In the example, the system  100  includes a radiation detector  104  configured to collect spectra data corresponding to a radioactive source  102 , a multi-channel analyzer  106  configured to convert the collected spectra data to at least one two-dimensional (2D) image, and a neuromorphic chip learning architecture  108  that includes configurable logic  110 , e.g., SRAM, implementing a plurality of neurons  116  configured to detect a radioactive anomaly based on the at least one 2D image. 
     In the example, the neuromorphic architecture  108  is a fully parallel neural network and the plurality of neurons  116  includes a chain of identical neurons. The neuromorphic architecture also includes a parallel bus having bi-directional communication between the plurality of neurons  116 . 
     The chain of identical neurons may be configured to learn and recognize the collected spectra data processed as different patterns, and each neuron in the chain of identical neurons may be configured to store a prototype vector. Each neuron in the chain of identical neurons may be configured to learn and recall its pattern spontaneously and autonomously evaluate a distance between reference patterns stored in a memory, e.g., a storage device  112 , and input vectors. 
     Responsive to the distance falling within a specified range, e.g., an active influence field, for at least one neuron, the at least one neuron may be configured to fire and return a decision that may include, for example, any one or more of: the distance, a category, and an identifier corresponding to the neuron that fired. 
     In certain embodiments, the neuromorphic architecture may be configured to learn a signature corresponding to the radiation detector, and also differentiate the radioactive anomaly from the background based at least in part on the learned signature. Alternatively or in addition thereto, the neuromorphic architecture may be configured to memorize patterns from the background, and also detect the radioactive anomaly based at least in part on the memorized patterns. 
     In certain embodiments, each neuron in the chain of identical neurons is configured to recognize its pattern within a sliding window moving in the 2D image. In such embodiments, the neuron may be configured to store its pattern as a vector in a memory of the neuron, and the sliding window may determine the vector size. 
     In certain embodiments, the 2D image has a first axis denoting time and a second axis denoting channel number. The count of each channel, e.g., in 1,024 channels, may be converted to a pixel intensity, and the collected spectra data may be presented as a three-dimensional (3D) histogram that may have, for example, a first axis denoting channel number, a second axis denoting a count, and a third axis denoting present time. 
     In certain embodiments, the system  100  comprises a display  114  configured to visually display output results corresponding to the detected radioactive anomaly. The system  100  may also include at least one power source  118  to provide power for any or all of the detector  104 , multi-channel analyzer  106 , and neuromorphic architecture  108 . 
       FIG. 2  is a flow diagram illustrating an example of a method  200  for identifying anomalous nuclear radioactive sources in accordance with certain implementations of the disclosed technology. 
     At  202 , a radiation detector, such as the radiation detector  104  of the system  100  illustrated by  FIG. 1 , collects spectra data corresponding to a radioactive source. 
     At  204 , a multi-channel analyzer, such as the multi-channel analyzer  106  of the system  100  illustrated by  FIG. 1 , converts the spectra data collected by the radiation detector at  202  to at least one two-dimensional (2D) image. 
     At  206 , a neuromorphic architecture that includes a plurality of neurons, such as the neuromorphic chip learning architecture  108  of the system  100  illustrated by  FIG. 1 , detects a radioactive anomaly based on the at least one 2D image that results from  204 . 
     At  208 , a display of the neuromorphic architecture, such as the display  114  of the system  100  illustrated by  FIG. 1 , visually presents output results corresponding to the detected radioactive anomaly. 
     At  210 , a memory of the neuromorphic architecture, such as the storage  112  of the system  100  illustrated by  FIG. 1 , visually presents output results corresponding to the detected radioactive anomaly. 
       FIG. 3  is a block diagram illustrating an example of a system  300  having a neuromorphic architecture  302  that includes a fully parallel neural network with a chain of identical neurons that can each learn and recognize the input information processed as different patterns. In the example, the neurons are fully connected through a parallel bus that may have bi-directional communication to facilitate both write and readout actions, and each neuron may receive an input vector  308  and global context  306 . 
     In the example, each neuron has the ability to learn and recall their model spontaneously without any supervision. They recognize the incoming signal by autonomously evaluating the distance between the reference models stored in their memory and input vectors  308 , for example. If this distance falls within a range called the minimum influence field, the neuron may fire and return a decision that consists of the distance (active influence field), category, and neuron identifier or other suitable information. 
     In an example, background radiation was collected using a radiation detector and the background data has 14077 samples of spectra, each sample collected in 1 s. The average of the count rate is 40±16 counts/s. The background gamma-ray has an energy spectrum from 0-3 MeV because of naturally occurring radioactive material (NORM). The source spectra were statistically simulated where gammas: (i) emit via a Poisson random number with mean equal to source activity, (ii) are counted based on geometric efficiency, and (iii) are binned via N(d,σ 2 ), where d represents the photopeak channel and σ 2  is defined by detector resolution. 
       FIGS. 4A-4C  each show three-dimensional (3D) histograms  400 ,  405 ,  410  of seven detected background spectra with channel number in x-axis, counts in y-axis, and time in z-axis. In  FIG. 4B , the 3D histograms of the seven simulated source spectra  405  are in the same time as those  400  in  FIG. 4A .  FIG. 4C  shows the seven spectra  410  of the background plus the source in 3D histograms. The total channel number is 1024 but, for better viewing, only 20-140 channels are exhibited in the example. 
     The radiation spectrum from  FIGS. 4A-4C  may be used to create the spectra information illustrated by  FIGS. 5A-5B , which illustrate an example of spectra from 20-140 channels  500  and an example of spectra from 1024 channels  505 , respectively. For example, a two-dimensional (2D) figure could be derived from  FIG. 4C  if the counts are presented as the grayscale intensity. For anomaly detection, the neural network can memorize the patterns from the past and process the new signal to detect whether there is anomaly. For example, in  FIGS. 5A-5B , up to 7077 seconds, there is background spectra and from 7077 seconds to 14077 seconds, there are background spectra plus source spectra. It is very clear that after 7077 seconds, a different pattern appears. 
     In another example, detector spectra data collected may be converted as a function of time to an image. The count of each channel may be converted to pixel intensity, and the source and background spectrum may be presented by a grayscale 2-D figure. Anomaly detection may be cast as a computer vision task. The neural network may memorize the patterns for background in the past and process the new signal to detect if there is anomaly. A sliding window may move in the image and the neural network may recognize the pattern within the window. The pattern may be stored as a vector in memory of each neuron, and the vector may come from the information within each window. For example, a 2D figure may be derived from 3D figure as the counts presented by grayscale intensity. 
       FIG. 6  illustrates an example of 1024 spectra presented by a 2D grayscale image (i.e., 1024×1024 pixels) in which the x-axis denotes time and the y-axis denotes channel number. One spectrum is shown in the right part of the figure if the intensity in one pixel is unfolded to counts in the histogram. The left part of the figure shows a more clear view of channel 20-140. A window in the left bottom part of the middle image may slide in directions indicated by the arrows. 
       FIG. 6  illustrates an example 600 that presents the background spectra up to 512 seconds and, from 512 seconds to 1024 seconds, there are background spectra with injected source spectra. It is clear that after 512 seconds, a different pattern appears around the source channel. 
     In certain examples, two decisions may need to be made: the size of the sliding window; and the stride of sliding. In a given detection scenario, the window size generally defines the vector length and time it takes to detect anomaly. The stride generally determines the channel resolution for the source. Thus, there is typically a trade-off between the anomaly detecting accuracy, channel resolution, and the time needed for recognizing. 
     It will be appreciated that implementations may include implementing the disclosed neuromorphic architecture and SNN in a FPGA and training the architecture for processing anomaly detection. 
     Aspects of the disclosure may operate on particularly created hardware, firmware, digital signal processors, or on a specially programmed computer including a processor operating according to programmed instructions. The terms controller or processor as used herein are intended to include microprocessors, microcomputers, Application Specific Integrated Circuits (ASICs), and dedicated hardware controllers. 
     One or more aspects of the disclosure may be embodied in computer-usable data and computer-executable instructions, such as in one or more program modules, executed by one or more computers (including monitoring modules), or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The computer executable instructions may be stored on a computer readable storage medium such as a hard disk, optical disk, removable storage media, solid state memory, Random Access Memory (RAM), etc. As will be appreciated by one of skill in the art, the functionality of the program modules may be combined or distributed as desired in various aspects. In addition, the functionality may be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits, FPGAs, and the like. 
     Particular data structures may be used to more effectively implement one or more aspects of the disclosure, and such data structures are contemplated within the scope of computer executable instructions and computer-usable data described herein. 
     The disclosed aspects may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed aspects may also be implemented as instructions carried by or stored on one or more or computer-readable storage media, which may be read and executed by one or more processors. Such instructions may be referred to as a computer program product. Computer-readable media, as discussed herein, means any media that can be accessed by a computing device. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. 
     Computer storage media means any medium that can be used to store computer-readable information. By way of example, and not limitation, computer storage media may include RAM, ROM, Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Video Disc (DVD), or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, and any other volatile or nonvolatile, removable or non-removable media implemented in any technology. Computer storage media excludes signals per se and transitory forms of signal transmission. 
     Communication media means any media that can be used for the communication of computer-readable information. By way of example, and not limitation, communication media may include coaxial cables, fiber-optic cables, air, or any other media suitable for the communication of electrical, optical, Radio Frequency (RF), infrared, acoustic or other types of signals. 
     The previously described versions of the disclosed subject matter have many advantages that were either described or would be apparent to a person of ordinary skill. Even so, these advantages or features are not required in all versions of the disclosed apparatus, systems, or methods. 
     Additionally, this written description makes reference to particular features. It is to be understood that the disclosure in this specification includes all possible combinations of those particular features. Where a particular feature is disclosed in the context of a particular aspect or example, that feature can also be used, to the extent possible, in the context of other aspects and examples. 
     Also, when reference is made in this application to a method having two or more defined steps or operations, the defined steps or operations can be carried out in any order or simultaneously, unless the context excludes those possibilities. 
     Although specific examples of the invention have been illustrated and described for purposes of illustration, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, the invention should not be limited except as by the appended claims.