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Biological tissues have a complex impedance, or bio-impedance, profile which changes with respect to frequency. This is caused by dispersion mechanisms which govern how the electromagnetic field interacts with the tissue at the cellular and molecular level. Measuring the bio-impedance spectra of a biological sample can potentially provide insight into the sample's properties and its cellular structure. This has obvious applications in the medical, pharmaceutical and food-based industrial domains. However, measuring the bio-impedance spectra non-destructively and in a way which is practical at an industrial scale presents substantial challenges. The low conductivity of the sample requires a highly sensitive instrument, while the demands of industrial-scale operation require a fast high-throughput sensor of rugged design. In this paper, we describe a multi-frequency magnetic induction spectroscopy (MIS) system suitable for industrial-scale, non-contact, spectroscopic bio-impedance measurement over a bandwidth of 156kHz-2.5MHz. The system sensitivity and performance are investigated using calibration and known reference samples. It is shown to yield rapid and consistently sensitive results with good long-term stability. The system is then used to obtain conductivity spectra of a number of biological test samples, including yeast suspensions of varying concentration and a range of agricultural produce, such as apples, pears, nectarines, kiwis, potatoes, oranges and tomatoes. © 2015 IOP Publishing Ltd.

Walk-through metal detectors are used at check points for preventing personnel and passengers from carrying threatening metallic objects, such as knives and guns, into a secure area. These systems are capable of detecting small metallic items, such as handcuff keys and blades, but are unable to distinguish accurately between threatening objects and innocuous items. This paper studies the extent to which a K-nearest-neighbour classifier can distinguish various kinds of metallic objects, such as knives, shoe shanks, belts and containers. The classifier uses features extracted from the magnetic polarizability tensor, which represents the electromagnetic properties of the object. The tests include distinguishing threatening objects from innocuous ones, classifying a set of objects into 13 classes, and distinguishing between several similar objects within an object class. A walk-through metal detection system is used as source for the test data, which consist of 835 scans and 67 objects. The results presented show a typical success rate of over 95% for recognizing threats, and over 85% for correct classification. In addition, we have shown that the system is capable of distinguishing between similar objects reliably. Overall, the method shows promise for the field of security screening and suggests the need for further research. ?? 2014 IOP Publishing Ltd. cited By (since 1996)0

A tomographic metal detection and characterization system has been designed and built for recovering information about magnetic and/or conductive objects within the detector space. This information is gathered as a result of a 'walk-through' scan of a candidate in the same manner as for a typical security metal detector archway. Following the passage of the candidate, the system uses measurements from an array of coils to calculate the polarizability tensor, which describes the low frequency electromagnetic characteristic of a small metallic object when it interacts with an ac magnetic field. In addition to the magnetic polarizability dyadic tensor, the position of the perturbation is also determined as a product of the inversion algorithm. The system has been tested and is capable of inverting object tensors with

Magnetic induction tomography (MIT) is a new electromagnetic imaging modality which has the potential to image changes in the electrical conductivity of the brain due to different pathologies. In this study the feasibility of detecting haemorrhagic cerebral stroke with a 16-channel MIT system operating at 10 MHz was investigated. The finite-element method combined with a realistic, multi-layer, head model comprising 12 different tissues, was used for the simulations in the commercial FE package, Comsol Multiphysics. The eddy-current problem was solved and the MIT signals computed for strokes of different volumes occurring at different locations in the brain. The results revealed that a large, peripheral stroke (volume 49 cm(3)) produced phase changes that would be detectable with our currently achievable instrumentation phase noise level (17 m degrees ) in 70 (27%) of the 256 exciter/sensor channel combinations. However, reconstructed images showed that a lower noise level than this, of 1 m degrees , was necessary to obtain good visualization of the strokes. The simulated MIT measurements were compared with those from an independent transmission-line-matrix model in order to give confidence in the results.

This article addresses time-domain ultrawide band (UWB) electromagnetic tomography for reconstructing the unknown spatial characteristic of an object from observations of the arrivals of short electromagnetic (EM) pulses. Here, the determination of the first peak arrival of the EM traces constitutes the forward problem, and the inverse problem aims to reconstruct the EM property distribution of the media. In this article, the finite-difference time-domain method implementing a perfectly matched layer is used to solve the forward problem from which the system sensitivity maps are determined. Image reconstruction is based on the combination of a linearized update and regularized Landweber minimization algorithm. Experimental data from a laboratory UWB system using targets of different contrasts, sizes, and shapes in an aqueous media are presented. The results show that this technique can accurately detect and locate unknown targets in spite of the presence of significant levels of noise in the data.

This paper presents an inversion procedure to estimate the location and magnetic polarizability tensor of metal targets from broadband electromagnetic induction (EMI) data. The solution of this inversion produces a spectral target signature, which may be used in identifying metal targets in landmines from harmless clutter. In this process, the response of the metal target is modelled with dipole moment and fitted to planar EMI data by solving a minimization least squares problem. A computer simulation platform has been developed using a modelled EMI sensor to produce synthetic data for inversion. The reconstructed tensor is compared with an assumed true solution estimated using a modelled tri-axial Helmholtz coil array. Using some test examples including a sphere which has a known analytical solution, results show the inversion routine produces accurate tensors to within 12% error of the true tensor. A good convergence rate is also demonstrated even when the target location is mis-estimated by a few centimeters. Having verified the inversion routine using finite element modelling, a swept frequency EMI experimental setup is used to compute tensors for a set of test samples representing examples of metallic landmine components and clutter for a broadband range of frequencies (kHz to tens of kHz). Results show the reconstructed spectral target signatures are very distinctive and hence potentially offer an efficient physical approach for landmine identification. The accuracy of the evaluated spectra is similarly verified using a uniform field forming sensor. © 2015 IOP Publishing Ltd.

In this paper, a walk-through metal detection (WTMD) portal is used for classification of metallic objects. The classification is based on the inversion of the magnetic polarisability tensor (tensor) of the object. The nature of bias and noise components in the tensor are examined by using real walk-through data, and consequently, a novel classifier is introduced. Furthermore, a novel method for detecting poorly inverted tensors is presented, enabling self-diagnostics for the WTMD portal. Based on the results, the novel methods increase the accuracy of metal object classification and have the potential to improve the reliability of a WTMD system.

A previously reported tomographic metal detector which is capable of inverting the location and magnetic polarizability tensor for a single object has been modified such that it is capable of inverting the location and magnetic polarizability tensor for multiple objects. In this paper, the term 'multiple objects' refers to up to three independent metallic objects. The results from this paper show that the algorithm works well for objects vertically separated by greater than 40 cm, however the reliability varies as objects are brought closer together, or are at the same vertical height; the estimation of position for multiple objects tends to perform well, but the estimation of the magnetic polarizability tensor becomes poorer. Interactions taking place between objects is presented as one possible explanation for this. ?? 2014 IOP Publishing Ltd.