Alex Bijamov

Optical Response of Magnetic-Fluorescent Microspheres Used for Force Spectroscopy in the Evanescent Field

Force spectroscopy based on magnetic tweezers is a powerful technique for manipulating single biomolecules and studying their interactions. The resolution in magnetic probe displacement, however, needs to be commensurate with molecular sizes. To achieve the desirable sensitivity in tracking displacements of the magnetic probe, some recent approaches have combined magnetic tweezers with total internal reflection fluorescence microscopy. In this situation, a typical force probe is a polymer microsphere containing two types of optically active components: a pure absorber (magnetic nanoparticles for providing the pulling force) and a luminophore (semiconducting nanoparticles or organic dyes for fluorescent imaging). To assess the systems capability fully with regard to tracking the position of the force probe with subnanometer accuracy, we developed a body-of-revolution formulation of the method of auxiliary sources (BOR-MAS) to simulate the absorption, scattering, and fluorescence of microscopic spheres in an evanescent electromagnetic field. The theoretical formulation uses the axial symmetry of the system to reduce the dimensionality of the modeling problem and produces excellent agreement with the reported experimental data on forward scattering intensity. Using the BOR-MAS numerical model, we investigated the probe detection sensitivity for a high numerical aperture objective. The analysis of both backscattering and fluorescence observation modes shows that the total intensity of the bead image decays exponentially with the distance from the surface (or the length of a biomolecule). Our investigations demonstrate that the decay lengths of observable optical power are smaller than the penetration depth of the unperturbed excitation evanescent wave. In addition, our numerical modeling results illustrate that the expected sensitivity for the decay length changes with the angle of incidence, tracking the theoretical penetration depth for a two-media model, and is sensitive to the bead size. The BOR-MAS methodology developed in this work for near-field modeling of bead-tracking experiments fully describes the fundamental photonic response of microscopic BOR probes at the subwavelength level and can be used for future improvements in the design of these probes or in the setup of bead-tracking experiments.

Quantitative modeling of forces in electromagnetic tweezers

This paper discusses numerical simulations of the magnetic field produced by an electromagnet for generation of forces on superparamagnetic microspheres used in manipulation of single molecules or cells. Single molecule force spectroscopy based on magnetic tweezers can be used in applications that require parallel readout of biopolymer stretching or biomolecular binding. The magnetic tweezers exert forces on the surface-immobilized macromolecule by pulling a magnetic bead attached to the free end of the molecule in the direction of the field gradient. In a typical force spectroscopy experiment, the pulling forces can range between subpiconewton to tens of piconewtons. In order to effectively provide such forces, an understanding of the source of the magnetic field is required as the first step in the design of force spectroscopy systems. In this study, we use a numerical technique, the method of auxiliary sources, to investigate the influence of electromagnet geometry and material parameters of the magnetic core on the magnetic forces pulling the target beads in the area of interest. The close proximity of the area of interest to the magnet body results in deviations from intuitive relations between magnet size and pulling force, as well as in the force decay with distance. We discuss the benefits and drawbacks of various geometric modifications affecting the magnitude and spatial distribution of forces achievable with an electromagnet.

Camp Butner Live-Site UXO Classification Using Hierarchical Clustering and Gaussian Mixture Modeling

We demonstrate in detail a semisupervised scheme to classify unexploded ordnance (UXO) by using as an example the data collected with a time-domain electromagnetic towed array detection system during a live-site blind test conducted at the former Camp Butner in North Carolina, USA. The model that we use to characterize targets and generate discrimination features relies on a solution of the inverse UXO problem using the orthonormalized volume magnetic source model. Unlike other classification techniques, which often rely on library matching or expert knowledge, our combined clustering/Gaussian-mixture-model approach first uses the inherent properties of the data in feature space to build a custom training list that is then used to score all unknown targets by assigning them a likelihood of being UXO. The ground truth for the most likely candidates is then requested and used to correct the model parameters and reassign the scores. The process is repeated several times until the desired statistical margin is reached, at which point a final dig is produced. Our method could decrease intervention by human experts and, as the results of the blind test show, identify all targets of interest correctly while minimizing false-alarm counts.

MPV-II: an enhanced vector man-portable EMI sensor for UXO identification

The Man-Portable Vector (MPV) electromagnetic induction sensor has proved its worth and flexibility as a tool for identification and discrimination of unexploded ordnance (UXO). TheMPV allows remediation work in treed and rough terrains where other instruments cannot be deployed; it can work in survey mode and in a static mode for close interrogation of anomalies. By measuring the three components of the secondary field at five different locations, the MPV provides diverse time-domain data of high quality. TheMPV is currently being upgraded, streamlined, and enhanced to make it more practical and serviceable. The new sensor, dubbedMPV-II, has a smaller head and lighter components for better portability. The original laser positioning system has been replaced with one that uses the transmitter coil as a beacon. The receivers have been placed in a configuration that permits experimental computation of field gradients. In this work, after introducing the new sensor, we present the results of several identification/discrimination experiments using data provided by the MPV-II and digested using a fast and accurate new implementation of the dipole model. The model performs a nonlinear search for the location of a responding target, at each step carrying out a simultaneous linear least-squares inversion for the principal polarizabilities at all time gates and for the orientation of the target. We find that the MPV-II can identify standard-issue UXO, even in cases where there are two targets in its field of view, and can discriminate them from clutter.

Combining electromagnetic induction and automated classification in a UXO discrimination blind test

The Strategic Environmental Research and Development Program (SERDP) is administering benchmark blind tests of increasing realism to the UXO community. One of the latest took place at Aberdeen Proving Ground in Maryland: 214 cells, each one containing at most one buried target, were interrogated with the TEMTADS electromagnetic induction (EMI) sensor array. Each item could be one of six standard ordnance or could be harmless clutter such as shrapnel. The test called for singling out potentially dangerous items and classifying them. Our group divided the task into three steps: location, characterization, and classification. For the first step the HAP method was used. The method assumes a pure dipolar response from the target and finds the position and orientation using the measured field and its associated scalar potential, the latter computed using a layer of equivalent sources. For target characterization we used the NSMS model, which employs an ensemble of dipole sources arranged on a spheroidal surface. The strengths of these sources are normalized by the primary field that strikes them; their surface integral is an electromagnetic signature that can be used as a classifier. In this work we look into automating the classification step using a multi-category support vector machine (SVM). The algorithm runs binary SVMs for every combination of pairs of target candidates, apportions votes to the winners, and assigns unknown examples to the category with the most votes. We look for the feature combinations and SVM parameters that result in the most expedient and accurate classification.

Inversion and classification studies of live-site production-level MetalMapper data sets

This paper illustrates the discrimination performance of a set of advanced models at an actual UXO live site. The suite of methods, which combines the orthonormalized volume magnetic source (ONVMS) model, a data-preprocessing technique based on joint diagonalization (JD), and differential evolution (DE) minimization, among others, was tested at the former Camp Beale in California. The data for the study were collected independently by two UXO production teams from Parsons and CH2M HILL using the MetalMapper (MM) sensor in cued mode; each set of data was also processed independently. Initially all data were inverted using a multi-target version of the combined ONVMS-DE algorithm, which provided intrinsic parameters (the total ONVMS amplitudes) that were then used to perform classification after having been inspected by an expert. Classification of the Parsons data was conducted by a Sky Research production team using a fingerprinting approach; analysis of the CH2M HILL data was performed by a Sky/Dartmouth R&D team using unsupervised clustering. During the classification stage the analysts requested the ground truth for selected anomalies typical of the different clusters; this was then used to classify them using a probability function. This paper reviews the data inversion, processing, and discrimination schemes involving the advanced EMI methods and presents the classification results obtained for both the CH2M HILL and the Parsons data. Independent scoring by the Institute for Defense Analyses reveals superb all-around classification performance.

Inversion-free discrimination of unexploded ordnance in real time

ESTCP live-site UXO classification results are presented for cued data collected with two advanced EMI instruments, the cart-based 2 × 2 3D TEMTADS array and the Man Portable Vector (MPV) handheld sensor, at the former Camp Beale in California. There were two sets of targets of interest (TOI): the main set consisted of 105-mm, 81-mm, 60-mm, 37-mm and ISO projectiles, and the other (optional) set comprised site-specific fuzes and fuze fragments of varous sizes. The advanced models used for inversion and classification combine: 1) a joint-diagonalization (JD) algorithm that estimates the number of potential targets generating an anomaly directly from the measured data without need for inversion; 2) the ortho-normalized volume magnetic source (ONVMS) model, which locates targets, represents their EMI responses, and extracts their intrinsic feature vectors; and 3) a Gaussian mixture algorithm that uses extracted discrimination features to classify the corresponding buried objects as TOI or clutter. Initially the data are inverted using a combination of ONVMS and the differential evolution direct-search algorithm; this allows the determination of relevant intrinsic parameters, which in turn are classified by a mixture of clustering and library-matching techniques. This paper describes in more detail the main steps of the classification process and demonstrates the results obtained for the 2 × 2 3D TEMTADS and MPV data taken at Camp Beale, as scored independently by the Institute for Defense Analyses. The advanced models are seen to produce superb classification in both cases.

Optimizing EMI transmitter and receiver configurations to enhance detection and identification of small and deep metallic targets

Current electromagnetic induction (EMI) sensors of the kind used to discriminate buried unexploded orndance (UXO) can detect targets down to a depth limited by the geometric size of the transmitter (Tx) coils, the amplitudes of the transmitting currents, and the noise floor of the receivers (Rx). The last two factors are not independent: for example, one cannot detect a deeply buried target simply by increasing the amplitude of the Tx current, since this also increases the noise and thus does not improve the SNR. The problem could in principle be overcome by increasing the size of the Tx coils and thus their moment. Current multi-transmitter instruments such as the TEMTADS sensor array can be electronically tweaked to provide a big Tx moment: they can be modified to transmit signals from two, three or more Tx coils simultaneously. We investigate the possibility of enhancing the deep-target detection capability of TEMTADS by exploring different combinations of Tx coils. We model different multi-Tx combinations within TEMTADS using a full-3D EMI solver based on the method of auxiliary sources (MAS).We determine the feasibility of honing these combinations for enhanced detection and discrimination of deep targets. We investigate how to improve the spatial resolution and focusing properties of the primary magnetic field by electronically adjusting the currents of the transmitters. We apply our findings to data taken at different UXO live sites.

Comparison of supervised and unsupervised machine learning techniques for UXO classification using EMI data

Classification tools including Support Vector Machines (SVM) and Neural Networks (NN) are employed, and their performances compared for Unexploded Ordnance (UXO) classification using live site electromagnetic induction (EMI) data. Both SVM and NN are examples of supervised machine-learning techniques, whose purpose is to label the features (extracted from the incoming data of the unknown subsurface anomalies) based on previously trained examples. In this paper a set of three features are extracted from the EMI decay curves of the physics-based intrinsic, effective dipole moment, called the total Normalized Surface Magnetic Source (NSMS). This data is first used to train both the SVM and NN models and further serves as a basis for UXO classification. These techniques are then compared to an unsupervised learning approach, based on agglomerative hierarchical clustering followed by Gaussian Mixture modeling. We found that such combination provides reduction in the amount of required training data (which is being requested solely based on the clustering results) and allows for convenient probabilistic interpretation of the classification. The classification results themselves depend on the UXO caliber, material composition and actual live UXO sites conditions. Therefore, here we report the classification results for a live UXO data set, collected at former Camp San Luis Obispo, CA. This study includes four targets-of-interest: 60-mm, 81-mm, and 4.2-in mortars and 2.36-in rockets. The classification performance between clutters and UXO is studied and the corresponding ROC curves are illustrated.

Assessing EMI noise due to the marine environment to enhance underwater UXO detection and discrimination

We assess the noise level caused by marine environments in underwater UXO discrimination studies. Underwater UXO detection and discrimination is subject to additional noise sources, which are not present in land-based scenarios. Particularly, we study the effects of water surface roughness on the diffusion of EMI (electromagnetic induction) fields through the air-water interface and the interaction effects between an underwater conducting object and its surrounding conductive medium. Numerical simulations are conducted using the 3-dimensional setup of the Method of Auxiliary Sources suitable for low-frequency regime. Water surface roughness is modeled as an interference pattern between a finite number of surface waves with varying amplitudes, wavelengths and propagation directions. The results indicate that the perturbations in diffused and scattered EMI fields due to water surface roughness are negligible (although they depend on the shape of water surface) and that these perturbations decay with distance from the interface. Thus, the conducting water body may be assumed to represent a half-space in subsequent calculations for UXO detection. Finally, it is shown that there is some interaction between a conducting object and its surrounding conductive environment for frequencies above 100 kHz. This interaction is attenuated if the object is surrounded by an insulating shell, but is amplified if the shell is conducting. This non-negligible effect needs to be taken into account for the purposes of UXO detection and discrimination.