Electromagnetic Induction Spectroscopy Research Articles

Effect of electromagnetic induction spectroscopy of femur bone on electromagnetic signal strength

Bone implant is a promising technique to heal the fracture site where it focusing to stabilize and support the bone. Improvement in bone implant are required in order to ensure better healing process because some failures may happen due to the loss of implant stability. Previous studies showed there are lack of investigation on bone implant. Early detection of screw implant loosening is crucial for faster repairing and a healthy ready-to-operate. The purpose of this study is to investigate the signal strength of electromagnetic field induction on human bone by employing a single channel magnetic induction spectroscopy using flat spiral coil sensor. This study also to evaluate the various type of coil sensitivity of the magnetic induction spectroscopy on femur bone. The electromagnetic field induction will be analyse using the COMSOL Multiphysics simulation software to collect the data of received signal strength. The promising experimental results suggested the feasibility of the proposed electromagnetic induction spectroscopy.

Effect of electromagnetic induction spectroscopy of screw crack implant on electromagnetic signal strength

Abstract Screws in bone implant are widely used in medical technology which focus on stabilizing and supporting the broken bone in means of healing. Improvement in bone implant are required in order to ensure better healing process because some failures may happen due to the crack of the screw implant. Previous studies showed there are lack of investigation on screw crack detection in bone screw implant. Early detection of screw implant loosening is crucial for faster repairing and a healthy ready-to-operate. The purpose of this study is to investigate the received signal strength of electromagnetic field induction on implant screw by employing a single channel magnetic induction spectroscopy using flat spiral coil sensor. This study also to evaluate the screw implant under the action of electromagnetic field induction. The electromagnetic method is choose to be implement using the COMSOL Multiphysics’s simulation software. The magnetic induction spectroscopy technique was used in this study to evaluate the received signal strength data for the different screw crack and non-crack. In this study, a high-cycle fatigue test using the flat coil sensor was performed in order to monitor cracks in Titanium Ti-6Al-4V screw.. The results suggested the feasibility of the proposed electromagnetic induction spectroscopy.

A comparison of calibration sampling schemes at the field scale

High-resolution digital soil sensing and mapping is an important and emerging new technology that helps meet the strong and growing global demand for high-resolution soil property data. However, the combination of geophysical sensing and pedometrical techniques to produce soil property maps is complex and requires a well-structured design, from the initial steps of data collection right through to final model validation. In this study, we compare different sampling design strategies – an extension of conditioned Latin hypercube sampling, fuzzy k-means sampling and response surface sampling – as a basis for predicting soil texture, soil organic carbon and soil pH-value at two soil depth intervals using electromagnetic induction (EM38DD and EM31) and gamma spectroscopy (U, K, Th) data. Two different sample set sizes, two different regression approaches (multiple linear least squares and random forests), as well as several resampling and independent validation approaches are compared. In addition to these real-world datasets, we also compared the investigated methods for two comparable simulated datasets.Our accuracy estimation results reveal that an optimal combination of Latin hypercube sampling and random forest regression should be adopted. This is the case for both the real world examples as well as the two synthetic datasets. The analysis conducted indicates that this stems from optimized spread within the state space of the sensors.Iterative LHS subsampling with increasing sample set sizes may potentially be a successful approach for incrementally analyzing and validating the model and thus can help reduce laboratory costs when a certain desired accuracy level is achieved.Comparison between the different validation approaches reveals their complexity and highlights the necessity for adequate independent validation approaches. However, based on the findings of our study, we recommend ‘leave-group-out’ cross-validation and ‘.632 bootstrapping’ as the best estimates to use.Finally, this study shows that there are complex interactions between sampling design, regression approaches and validation approaches, which can greatly influence the final soil property maps and their accuracy estimates.Future work should focus on detailed analysis of Latin hypercube sampling and why it outperformed the other approaches. Therefore, comparisons with other sampling approaches should be conducted, as well as specific ‘sampling-for-validation’ approaches. Therefore we provide the simulated datasets as Supplementary reference material for future comparative analysis.

Assessment of shallow subsurface characterisation with non-invasive geophysical methods at the intermediate hill-slope scale

Abstract. Hill-slopes of several hectares in size represent a difficult scale for subsurface characterisation, as these landscape units are well beyond the scope of traditional point-scale techniques. By means of electromagnetic induction (EMI) and gamma-ray spectroscopy, spatially distributed soil proxy data were collected from a heterogeneous hill-slope site. Results of EMI mapping using the EM38DD showed that soil electrical conductivity (ECa) is highly variable at both temporal and spatial scales. Calibration of the integral ECa signal to a specific target like soil moisture is hampered by the ambiguous response of EMI to the clay-rich hill-slope underground. Gamma-ray results were obtained during a single survey, along with EMI measurements and selected soil sampling. In contrast to ECa, a noticeable correlation between Total Count and K emission data and soil-water content seemed to be present. Relevant proxy variables from both methods were used for k means clustering in order to distinguish between hill-slope areas with different soil conditions. As a result, we obtained a suitable partition of hill-slope that was comparable with a previously obtained zonation model based on ecological factors.

Numerical Analysis for Remote Identification of Materials With Magnetic Characteristics

There is a variety of methods used for remote sensing of objects such as acoustic, ground penetration radar detection, electromagnetic induction spectroscopy, infrared imaging, thermal neutron activation, core four-pole resonance, neutron backscattering, X-ray backscattering, and magnetic anomaly. The method that has to be used can be determined by the type of material, geographical location (underground or water), etc. Recent studies have been concentrated on the improvement of the criteria such as sensing distances, accuracy, and power consumption. In this paper, anomalies created by materials with magnetic characteristics at the perpendicular component of the Earth magnetic field have been detected by using a KMZ51 anisotropic magnetoresistive sensor with high sensitivity and low power consumption, and also, the effects of physical properties of materials on magnetic anomaly have been investigated. By analyzing the graphics obtained by 2-D motion of the sensor over the material, the most appropriate mathematical curves and formulas have been determined. Based on the physical properties of the magnetic material, the variations of the variables constituting the formulas of the curves have been analyzed. The contribution of this paper is the use of the results of these analyses for the purpose of identification of an unknown magnetic material. This is a new approach for the detection and determination of materials with magnetic characteristics by sensing the variation at the perpendicular component of the Earth magnetic field. The identification process has been explained in detail in this paper.

Identifying Landmines by Incorporating Measurement Uncertainties into EMIS Library and Decision Threshold

Determining decision thresholds to separate landmines from clutter is a critical but difficult task in landmine identification procedures because such thresholds depend upon local geology, distribution of landmines and clutter, data acquisition, and cultural and environmental noise at a given landmine cleanup site. We describe a learn-as-you-go approach to build an electromagnetic induction spectroscopy (EMIS) library and decision thresholds for humanitarian demining operations, which include uncertainties regarding the target∕sensor position and orientation, target depth, geological and environmental noise, and measurement errors. This approach is then simulated mathematically using electromagnetic (EM) data obtained over the calibration plot at a test site. The resulting EMIS library and thresholds are tested on the EM data colleted in the test squares at the same site. Apparent conductivity converted from the data is used as a detector function, and both spectral shape and amplitude response are used quantitatively in the matching algorithm. Our results show that the detection rates are increased from 92% to 98% and identification rates from 76.2% to 95%, and the false alarm rate is reduced from 13% to 5%, compared with the previous results obtained from the same dataset.

Automated identification of buried landmines using normalized electromagnetic induction spectroscopy

Electromagnetic induction spectroscopy (EMIS) is used to identify a buried metallic object such as a landmine, based on its EMI spectrum. EMIS, however, depends on the object's electrical conductivity, magnetic permeability, shape, size, depth, and orientation. For a given mine made of specific metals, shape, and size, however, the only variables are the mine's burial depth and orientation. This paper describes a method of identifying a landmine using a normalized EMIS spectrum, which is independent of the orientation or depth. We assume that the target is small compared with its distance to or size of the sensor so that the source field at a target is uniform. In this case, the normalized spectrum will be range-independent and, therefore, the target identification is based on only spectral shapes. We have developed and tested an algorithm that matches a normalized EMIS spectrum to those of library targets. We applied the new process to 1) numerically simulated data, 2) experimental data from controlled sites using inert mines, mine simulants, and clutter items, and 3) finally extensive field data collected at a blind test site established by the U.S. Army. Test results show that targets are correctly identified with a misfit of less than 10%; they also show that 80% of clutter may be rejected, based on a misfit over 30%.

Electromagnetic induction spectroscopy for clearing landmines

An estimated 110 million landmines, mostly antipersonnel mines laid in over 60 countries, kill or maim over 26000 people a year. One of the dilemmas for removing landmines is the amount of false alarms in a typical minefield. Broadband electromagnetic induction spectroscopy (EMIS) is a promising technology that can both detect and identify buried objects as landmines. By reducing the number of false alarms, this approach significantly reduces costs associated with landmine removal. Combining the EMIS technology and a broadband EMI sensor, the scientific phenomenology that has potential applications for identifying landmines, unexploded ordnance, and hidden weapons at security checkpoints can now be explored.

Electromagnetic Induction Spectroscopy

An object, made partly or wholly of metals, has a distinct combination of electrical conductivity, magnetic permeability, and geometrical shape and size. When the object is exposed to a low‐frequency electromagnetic field, it produces a secondary magnetic field. By measuring the broadband spectrum of the secondary field, we obtain a distinct spectral signature that may uniquely identify the object. Based on the response spectrum, we attempt to “fingerprint” the object. This is the basic concept of Electromagnetic Induction Spectroscopy (EMIS).EMIS technology can be particularly useful for detecting buried landmines and unexploded ordnance. By fully characterizing and identifying an object without excavation, we should be able to reduce significantly the number of false targets. EMIS is applicable to many other problems where target identification and recognition (without intrusive search) are important. For instance, an advanced EMIS device at an airport security gate may be able to identify a particular ...