publications | John B. Sigman

Conference Papers and Journal Articles

2020

Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images

John B Sigman , Gregory P Spell , Kevin J Liang , and 1 more author

In Proc.SPIE , 2020

2019

Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection

Kevin J Liang , John B Sigman , Gregory P Spell , and 5 more authors

Advances in X-ray Analysis, Volume 64, proceedings of the 2020 Denver X-ray Conference, 2019

2018

Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach

Kevin J Liang , Geert Heilmann , Christopher Gregory , and 6 more authors

In Proc.SPIE – Invited Paper , 2018
Accounting for the influence of salt water in the physics required for processing underwater UXO EMI signals

Fridon Shubitidze , Benjamin E Barrowes , Irma Shamatava , and 2 more authors

In Proc.SPIE , 2018

2017

High-frequency electromagnetic induction sensing of non-metallic materials

John Brevard Sigman , Benjamin Barrowes , Kevin O’Neill , and 4 more authors

IEEE Transactions on Geoscience and Remote Sensing, 2017
Initial Development of a High-frequency EMI Sensor for Detection of Subsurface Intermediate Electrically Conductive (IEC) Targets

Janet E Simms , John B Sigman , Benjamin E Barrowes , and 4 more authors

Journal of Environmental and Engineering Geophysics, 2017
A hybrid coil system for high frequency electromagnetic induction sensing

John B. Sigman , Benjamin E. Barrowes , Kevin O’Neill , and 4 more authors

2017
Void and landmine detection using the HFEMI sensor

Benjamin E. Barrowes , Fridon Shubitidze , John B. Sigman , and 4 more authors

2017
Ultra-wideband EMI sensing for subsurface DU detection

Fridon Shubitidze , Benjamin E. Barrowes , John B. Sigman , and 1 more author

2017

2016

Detection of Conductivity Voids and Landmines using High Frequency Electromagnetic Induction

B. E. Barrowes , J. B. Sigman , K. O’Neill , and 4 more authors

2016
UXO Classification Procedures Applied to Advanced EMI Sensors and Models

F. Shubitidze , B. E. Barrowes , J. B. Sigman , and 2 more authors

2016
Carbon fiber and void detection using high-frequency electromagnetic induction techniques

Benjamin E. Barrowes , John B. Sigman , YinLin Wang , and 5 more authors

2016

Abs

Ultrawide band electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal unexploded ordnance. The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, even less conductive saturated salts, and even voids embedded in conducting soils, higher frequencies up to the low megahertz range are required in order to capture characteristic responses. To predict EMI phenomena at frequencies up to 15 MHz, we first modeled the response of intermediate conductivity targets using a rigorous, first-principles approach, the Method of Auxiliary Sources. A newly fabricated benchtop high-frequency electromagnetic induction instrument produced EMI data at frequencies up to that same high limit. Modeled and measured characteristic relaxation signatures compare favorably and indicate new sensing possibilities in a variety of scenarios.
A high power EMI sensor for detecting and classifying small and deep targets

F. Shubitidze , B. E. Barrowes , Yinlin Wang , and 4 more authors

2016

Abs

Detecting and classifying small (i.e., with calibers ranging from 20 to 60 mm) and deep targets (burial depth more than 11 times targets diameter) is still a challenging problem using current advanced EMI sensors and signal processing approaches. In order to overcome this problem, the standard time-domain NRL TEMTADS 2x2 electromagnetic induction (EMI) instrument is updated. Namely, the NRL TEMTADS 2x2 system’s transmitter electronics is modified to increase transmitter (Tx) currents from 6 Amperes to 14 Amperes. The instrument has a Tx array with four coplanar square coils, together with four tri-axial receivers (Rx) placed at the center of each Tx. Each Rx cube contains three orthogonal coils and thus registers all three vector components of the impinging signals. The Tx coils, with transmitter currents of 14 A, illuminate a buried target, and the target responses are collected with a 500 kHz sample rate after turn off of the excitation pulse. The system operates in both static (cued) and dynamic modes. For cued mode, the raw decay measurements are grouped into 121 logarithmically-spaced “gates” whose center times range from 25 μs to 24.35 ms with 5% widths. The sensor is placed on a cart which provides a sensor-to-ground offset of 20 cm or less. In this paper, studies for APG Calibration, Blind, and Small Munitions Grids are presented and analyzed. The areas are arranged in grids of test cells and the cell center positions are known. Each target position is flagged with a non-metallic pin flag using cm-level GPS. The sensor is positioned over each target in turn. With the system positioned over the target, each Tx is activated sequentially and during off the Tx current, all four Rx record data. The capabilities of this sensor platform is rigorously investigated for UXO classification at APG blind and small munitions grids.
Advanced EMI models for survey data processing: targets detection and classification

F. Shubitidze , B. E. Barrowes , Yinlin Wang , and 3 more authors

2016

Abs

This paper describes procedures and approaches our team took to demonstrate the capability of advanced electromagnetic induction (EMI) forward and inverse models to perform subsurface metallic objects picking and classification at live-UXO sites from dynamic data sets. Over the past seven years, blind classification tests at live-UXO sites have revealed two main challenges: 1) consistent selection of targets for cued interrogation, (e.g., for the recent SWPG2 study, two independent performers that processed the same MetalMapper dynamic data picked different targets for cued interrogation); and 2) positioning of the cued sensor close enough to the actual cued target to accurately perform classification (particularly when multiple targets or magnetic soils are present). To overcome these problems, in this paper we introduced an innovative and robust approach for subsurface metallic targets picking and classification from dynamic data sets. This approach first inverts for target locations and polarizabilities from each dynamic data point, and then clusters the inverted locations and defines each cluster as a target/source. Finally, the method uses the extracted polarizabilities for classifying UXO from non-UXO items. The studies are done for the 2x2 TEMTADS dynamic data set collected at Camp Hale, CO. The targets picking and classification results are illustrated and validated against ground truth.
Coil design considerations for a high-frequency electromagnetic induction sensing instrument

John B. Sigman , Benjamin E. Barrowes , Yinlin Wang , and 5 more authors

2016

Abs

Intermediate electrical conductivity (IEC) materials (101S/m < σ < 104S/m), such as carbon fiber (CF), have recently been used to make smart bombs. In addition, homemade improvised explosive devices (IED) can be produced with low conducting materials (10-4S/m < σ < 1S/m), such as Ammonium Nitrate (AN). To collect unexploded ordnance (UXO) from military training ranges and thwart deadly IEDs, the US military has urgent need for technology capable of detection and identification of subsurface IEC objects. Recent analytical and numerical studies have showed that these targets exhibit characteristic quadrature response peaks at high induction frequencies (100kHz − 15MHz, the High Frequency Electromagnetic Induction (HFEMI) band), and they are not detectable with traditional ultra wideband (UWB) electromagnetic induction (EMI) metal detectors operating between 100Hz − 100kHz. Using the HFEMI band for induction sensing is not so simple as driving existing instruments at higher frequencies, though. At low frequency, EMI systems use more wire turns in transmit and receive coils to boost signal-to-noise ratios (SNR), but at higher frequencies, the transmitter current has non-uniform distribution along the coil length. These non-uniform currents change the spatial distribution of the primary magnetic field and disturb axial symmetry and thwart established approaches for inferring subsurface metallic object properties. This paper discusses engineering tradeoffs for sensing with a broader band of frequencies ever used for EMI sensing, with particular focus on coil geometries.

2015

2014

West Mesa Metal Mapper Data Inversion and Classification

Irma Shamatava , Benjamin Barrowes , John Sigman , and 1 more author

2014

Abs

High frequency (>100 kHz) electromagnetic induction (HFEMI) sensing phenomena are investigated for nonmetallic ordnances detection and discrimination. HFEMI responses are studied using numerical and experimental data. The numerical modeling is done via the method of auxiliary sources, and data are collected using a new HEMI system, that has been developed at our lab. The comparisons between modeled and actual data are illustrated for a non-metallic 105 mm projectile.
Detecting and classifying small and deep targets using improved EMI hardware and data processing approach

F. Shubitidze , B. E. Barrowes , J. B. Sigman , and 3 more authors

2014

Abs

The appearance of next-generation EMI sensors has been accompanied by the development of advanced EMI models and new interpretation and inversion schemes that take advantage of the richness and diversity of the data provided by these instruments. The technologies have been successfully tested in various scenarios, including ESTCP live-UXO classification studies, and have demonstrated superb classification performances. The studies have shown that the system’s ability to detect and classify small targets (i.e., calibers ranging from 20 to 60 mm) and deep targets (burial depth more than 11 times the target’s diameter) is still a challenging problem when an existing system is used. To overcome this problem, first the standard approach is analyzed, then targets detections are studied for different transmitter coil combinations and transmitter current magnitudes. The results are validated experimentally. The studies are done for a 37mm projectile placed at 42cm and 86 cm under the 2×2 TEMTADS instrument. The target detection and classification performances are illustrated for 6, 11 and 14 Ampere Tx currents using the joint diagonalization and ortho normalized volume magnetic source techniques.
High frequency electromagnetic induction sensing for non-metallic ordnances detection

F. Shubitidze , J. Sigman , K. O’Neill , and 2 more authors

Sep 2014

Abs

High frequency (>100 kHz) electromagnetic induction (HFEMI) sensing phenomena are investigated for nonmetallic ordnances detection and discrimination. HFEMI responses are studied using numerical and experimental data. The numerical modeling is done via the method of auxiliary sources, and data are collected using a new HEMI system, that has been developed at our lab. The comparisons between modeled and actual data are illustrated for a non-metallic 105 mm projectile.
Advanced EMI models for survey data processing: targets detection and classification

F. Shubitidze , J. B. Sigman , Yinlin Wang , and 5 more authors

Sep 2014

Abs

One of the most challenging aspects of survey data processing is target selection. The fundamental input for the classification is dynamic data collected along survey lines. These data are different from the static data obtained in cued mode and used for target classification. Survey data are typically collected using just one transmitter loop (the Z-axis loop) and feature short data point collection times and short decay transience. The collection intervals for each data point are typically 0.1 s, and the signal repetition rates are typically 90 or 270 Hz (in other words, the transient decay times are 2.7 ms or 0.9 ms). Reliable classification requires multiple side/angle illumination; i.e., to conduct reliable classification it is necessary to combine and jointly invert multiple data points. However, picking data points that provide optimal information for classifying targets is a difficult task. The traditional method plots signal amplitudes on a 2D map and picks peaks of signal level without properly accounting for the underlying physics. In this paper, the joint diagonalization is applied to survey data sets to improve data pre-processing and target picking. The JD technique is an EMI data analysis and target classification technique and is applicable for all next-generation multi-static array EMI sensors. The method extracts multi-static response data matrix eigenvalues. The eigenvalues are main characteristics of the data. Recent studies have demonstrated that the method has great potential to quickly estimate the number of potential targets and moreover classify these targets at the data pre-processing stage, in real time and without the need for a forward model. Another advantage of JD is that it provides the ability to separate signal from noise making it possible to de-noise data without distorting the signal due to the targets. In this paper the JD technique is used to process dynamic data collected at South West Proving Ground and Aberdeen Proving Ground (APG) sites using the 2 × 2 TEMTADS and OPTEMA systems, respectively. The joint eigenvalues are extracted as functions of time for each data point and summed/stacked together before being used to create detection maps. Once targets are detected, a set of data is chosen for each anomaly and inverted using the ortho-normalized volume magnetic source technique.
Automatic classification of unexploded ordnance applied to live sites for MetalMapper sensor

John Brevard Sigman , Kevin O’Neill , Benjamin Barrowes , and 2 more authors

Sep 2014

Abs

This paper extends a previously-introduced method for automatic classification of Unexploded Ordnance (UXO) across several datasets from live sites. We used the MetalMapper sensor, from which extrinsic and intrinsic parameters are determined by the combined Differential Evolution (DE) and Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms. The inversion provides spatial locations and intrinsic time-series total ONVMS principal eigenvalues. These are fit to a power-decay empirical model, providing dimensionality reduction to 3 coefficients (k, b, and g) for polarizability decay. Anomaly target features are grouped using the unsupervised clustering Weighted-Pair Group Method with Averaging (WPGMA) algorithm. Central elements of each cluster are dug, and the results are used to train the next round of dig requests. A Naive Bayes classifier is used as a supervised learning algorithm, in which the product of each feature’s independent probability density represents each class of UXO in the feature space. We request ground truths for anomalies in rounds, until there are no more Targets of Interest (TOI) in consecutive requests. This fully automatic procedure requires no expert intervention, saving time and money. Naive Bayes outperformed previous efforts with Gaussian Mixture Models(GMM) in all cases.
An expert-free technique for live site uxo target classification

John B. Sigman , Yinlin Wang , Kevin O’Neill , and 2 more authors

Mar 2014
A Combined Joint Diagonalization-Music Algorithm For Estimating Locations Of Subsurface Targets

Y. Wang , J.B. Sigman , B.E. Barrowes , and 2 more authors

Mar 2014
A combined joint diagonalization-MUSIC algorithm for subsurface targets localization

Yinlin Wang , John B. Sigman , Benjamin E. Barrowes , and 2 more authors

Mar 2014

Abs

This paper presents a combined joint diagonalization (JD) and multiple signal classification (MUSIC) algorithm for estimating subsurface objects locations from electromagnetic induction (EMI) sensor data, without solving ill-posed inverse-scattering problems. JD is a numerical technique that finds the common eigenvectors that diagonalize a set of multistatic response (MSR) matrices measured by a time-domain EMI sensor. Eigenvalues from targets of interest (TOI) can be then distinguished automatically from noise-related eigenvalues. Filtering is also carried out in JD to improve the signal-to-noise ratio (SNR) of the data. The MUSIC algorithm utilizes the orthogonality between the signal and noise subspaces in the MSR matrix, which can be separated with information provided by JD. An array of theoreticallycalculated Green’s functions are then projected onto the noise subspace, and the location of the target is estimated by the minimum of the projection owing to the orthogonality. This combined method is applied to data from the Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS). Examples of TEMTADS test stand data and field data collected at Spencer Range, Tennessee are analyzed and presented. Results indicate that due to its noniterative mechanism, the method can be executed fast enough to provide real-time estimation of objects’ locations in the field.

2013

Automatic classification of unexploded ordnance applied to Spencer Range live site for 5x5 TEMTADS sensor

John B. Sigman , Benjamin E. Barrowes , Kevin O’Neill , and 1 more author

Mar 2013

Abs

This paper details methods for automatic classification of Unexploded Ordnance (UXO) as applied to sensor data from the Spencer Range live site. The Spencer Range is a former military weapons range in Spencer, Tennessee. Electromagnetic Induction (EMI) sensing is carried out using the 5x5 Time-domain Electromagnetic Multi-sensor Towed Array Detection System (5x5 TEMTADS), which has 25 receivers and 25 co-located transmitters. Every transmitter is activated sequentially, each followed by measuring the magnetic field in all 25 receivers, from 100 microseconds to 25 milliseconds. From these data target extrinsic and intrinsic parameters are extracted using the Differential Evolution (DE) algorithm and the Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms, respectively. Namely, the inversion provides x, y, and z locations and a time series of the total ONVMS principal eigenvalues, which are intrinsic properties of the objects. The eigenvalues are fit to a power-decay empirical model, the Pasion-Oldenburg model, providing 3 coefficients (k, b, and g) for each object. The objects are grouped geometrically into variably-sized clusters, in the k-b-g space, using clustering algorithms. Clusters matching a priori characteristics are identified as Targets of Interest (TOI), and larger clusters are automatically subclustered. Ground Truths (GT) at the center of each class are requested, and probability density functions are created for clusters that have centroid TOI using a Gaussian Mixture Model (GMM). The probability functions are applied to all remaining anomalies. All objects of UXO probability higher than a chosen threshold are placed in a ranked dig list. This prioritized list is scored and the results are demonstrated and analyzed.

Technical Reports

2016

Resonant Tunnel Detection

John Sigman , Benjamin Barrowes , Fridon Shubitidze , and 2 more authors

Oct 2016

2015

Resolving and Discriminating Overlapping Anomalies from Multiple Objects in Cluttered Environments

Fridon Shubitidze , John Sigman , Yinlin Wang , and 4 more authors

Oct 2015

Patents

2014

Photograph initiated appraisal process and application

N.P. Den Herder , J.B. Sigman , N. Lande , and 1 more author

Mar 2014

US Patent App. 13/614,705
Location driven appraisal data extraction, past appraisal and value comparison and comparable property finder

N.P. Den Herder , J.B. Sigman , N. Lande , and 1 more author

Mar 2014

US Patent App. 13/614,686