Gradient Index Metasurface Lens for Microwave Imaging

This paper presents the design, simulation and experimental validation of a gradient-index (GRIN) metasurface lens operating at 8 GHz for microwave imaging applications. The unit cell of the metasurface consists of an electric-LC (ELC) resonator. The effective refractive index of the metasurface is controlled by varying the capacitive gap at the center of the unit cell. This allows the design of a gradient index surface. A one-dimensional gradient index lens is designed and tested at first to describe the operational principle of such lenses. The design methodology is extended to a 2D gradient index lens for its potential application as a microwave imaging device. The metasurface lenses are designed and analyzed using full-wave finite element (FEM) solver. The proposed 2D lens has an aperture of size 119 mm (3.17λ) × 119 mm (3.17λ) and thickness of only 0.6 mm (0.016λ). Horn antenna is used as source of plane waves incident on the lens to evaluate the focusing performance. Field distributions of the theoretical designs and fabricated lenses are analyzed and are shown to be in good agreement. A microwave nondestructive evaluation (NDE) experiment is performed with the 2D prototype lens to image a machined groove in a Teflon sample placed at the focal plane of the lens.

Bioelectromagnetic Platform for Cell, Tissue, and In Vivo Stimulation

Magnetogenetics is a new field that utilizes electromagnetic fields to remotely control cellular activity. In addition to the development of the biological genetic tools, this approach requires designing hardware with a specific set of demands for the electromagnets used to provide the desired stimulation for electrophysiology and imaging experiments. Here, we present a universal stimulus delivery system comprising four magnet designs compatible with electrophysiology, fluorescence and luminescence imaging, microscopy, and freely behaving animal experiments. The overall system includes a low-cost stimulation controller that enables rapid switching between active and sham stimulation trials as well as precise control of stimulation delivery thereby enabling repeatable and reproducible measurements.

Swimming direction of the glass catfish is responsive to magnetic stimulation

Several marine species have developed a magnetic perception that is essential for navigation and detection of prey and predators. One of these species is the transparent glass catfish that contains an ampullary organ dedicated to sense magnetic fields. Here we examine the behavior of the glass catfish in response to static magnetic fields which will provide valuable insight on function of this magnetic response. By utilizing state of the art animal tracking software and artificial intelligence approaches, we quantified the effects of magnetic fields on the swimming direction of glass catfish. The results demonstrate that glass catfish placed in a radial arm maze, consistently swim away from magnetic fields over 20 μT and show adaptability to changing magnetic field direction and location.

Capacitive imaging for adhesive bonds and quality evaluation

Defective adhesive bonds pose significant threats towards structural integrity due to reduced joint strength. The nature of the adhesion of two solids remains poorly understood since the adhesion phenomenon is relevant to so many scientific and technological areas. A concept that has been gaining our attention from the perspective of non-destructive testing is the properties discontinuity of the adhesion. Discontinued properties depend significantly on the quality of the interface that is formed between adhesive and substrate. In this research, discontinued electrical properties at the interface are considered. The simplified model is free from multidisciplinary knowledge of chemistry, fracture mechanics, mechanics of materials, rheology and other subjects. From a practical standpoint, this emphasizes the need to establish a good relationship between electrical properties of adhesive bonds and corresponding measurements. Capacitive imaging (CI) is a technique where the dielectric property of an object is determined from external capacitance measurements. Thus, it is potentially promising since adhesive and substrate differ in terms of dielectric property. At the interface between adhesive and substrate, discontinuity of the dielectric properties causes abrupt changes in electric field spatial distribution and thus alters capacitance measurement by simulating defects in adhesive joints regarding permittivity uncertainties. Further understanding of the cause of degraded adhesion quality can be obtained. This article is part of the theme issue 'Advanced electromagnetic non-destructive evaluation and smart monitoring'.

Non-invasive neuromodulation using rTMS and the electromagnetic-perceptive gene (EPG) facilitates plasticity after nerve injury

BACKGROUND

Twenty million Americans suffer from peripheral nerve injury. These patients often develop chronic pain and sensory dysfunctions. In the past decade, neuroimaging studies showed that these changes are associated with altered cortical excitation-inhibition balance and maladaptive plasticity. We tested if neuromodulation of the deprived sensory cortex could restore the cortical balance, and whether it would be effective in alleviating sensory complications.

OBJECTIVE

We tested if non-invasive repetitive transcranial magnetic stimulation (rTMS) which induces neuronal excitability, and cell-specific magnetic activation via the Electromagnetic-perceptive gene (EPG) which is a novel gene that was identified and cloned from glass catfish and demonstrated to evoke neural responses when magnetically stimulated, can restore cortical excitability.

METHODS

A rat model of forepaw denervation was used. rTMS was delivered every other day for 30 days, starting at the acute or at the chronic post-injury phase. A minimally-invasive neuromodulation via EPG was performed every day for 30 days starting at the chronic phase. A battery of behavioral tests was performed in the days and weeks following limb denervation in EPG-treated rats, and behavioral tests, fMRI and immunochemistry were performed in rTMS-treated rats.

RESULTS

The results demonstrate that neuromodulation significantly improved long-term mobility, decreased anxiety and enhanced neuroplasticity. The results identify that both acute and delayed rTMS intervention facilitated rehabilitation. Moreover, the results implicate EPG as an effective cell-specific neuromodulation approach.

CONCLUSION

Together, these results reinforce the growing amount of evidence from human and animal studies that are establishing neuromodulation as an effective strategy to promote plasticity and rehabilitation.

Optimization and Validation of Rotating Current Excitation with GMR Array Sensors for Riveted Structures Inspection

In eddy current non-destructive testing of a multi-layered riveted structure, rotating current excitation, generated by orthogonal coils, is advantageous in providing sensitivity to defects of all orientations. However, when used with linear array sensors, the exciting magnetic flux density ( B x ) of the orthogonal coils is not uniform over the sensor region, resulting in an output signal magnitude that depends on the relative location of the defect to the sensor array. In this paper, the rotating excitation coil is optimized to achieve a uniform B x field in the sensor array area and minimize the probe size. The current density distribution of the coil is optimized using the polynomial approximation method. A non-uniform coil design is derived from the optimized current density distribution. Simulation results, using both an optimized coil and a conventional coil, are generated using the finite element method (FEM) model. The signal magnitude for an optimized coil is seen to be more robust with respect to offset of defects from the coil center. A novel multilayer coil structure, fabricated on a multi-layer printed circuit board, is used to build the optimized coil. A prototype probe with the optimized coil and 32 giant magnetoresistive (GMR) sensors is built and tested on a two-layer riveted aluminum sample. Experimental results show that the optimized probe has better defect detection capability compared with a conventional non-optimized coil.

A numerical model to study auscultation sounds under pneumothorax conditions

A 2D viscoelastic finite-difference time-domain (FDTD) is used to simulate sound propagation of lung sounds in the human thorax. Specifically, the model is employed to study the effects of pneumothorax on the sounds reaching the thoracic surface. By simulating varying degrees of severity of the disease, the model assists in determining the key frequency bands that contain the most information to aid in diagnosis. The work thus lends itself for development of advanced auscultatory techniques for detection of pneumothorax using noninvasive acoustic sensors.

A computational investigation of microwave breast imaging using deformable reflector

In recent years, active microwave breast imaging is increasingly being viewed as a promising complementary imaging modality for cancer detection. In this paper, we present a novel deformable reflector microwave tomography technique for noninvasive characterization of the breast tissue. In contrast to conventional multitransceiver designs, the proposed technique utilizes a continuously deformable reflector with metallic coating to acquire field measurements for imaging. Computational feasibility of the proposed technique to image heterogeneous dielectric tissue property is evaluated using simplified 2-D breast models. The robustness of the deformable reflector-based tomography technique in imaging the spatial distribution of the tissue dielectric property in the presence of measurement noise is investigated using first-order Tikhonov regularization. Preliminary results obtained for the 2-D breast models appear promising and indicate further investigation of the new microwave tomography technique for breast imaging.

Scanning probe recognition microscopy investigation of tissue scaffold properties

Scanning probe recognition microscopy is a new scanning probe microscopy technique which enables selective scanning along individual nanofibers within a tissue scaffold. Statistically significant data for multiple properties can be collected by repetitively fine-scanning an identical region of interest. The results of a scanning probe recognition microscopy investigation of the surface roughness and elasticity of a series of tissue scaffolds are presented. Deconvolution and statistical methods were developed and used for data accuracy along curved nanofiber surfaces. Nanofiber features were also independently analyzed using transmission electron microscopy, with results that supported the scanning probe recognition microscopy-based analysis.

Computational feasibility of deformable mirror microwave hyperthermia technique for localized breast tumors

PURPOSE

Computational feasibility of a new non-invasive microwave hyperthermia technique that employs dual deformable mirror is investigated using simplified computational tools and anatomically realistic breast models.

MATERIALS AND METHODS

The proposed technique employs two pairs of electromagnetic sources and continuously deformable mirrors to focus the electromagnetic radiation at the target site for hyperthermia. The mirror functions like a continuum of radiating elements that offer effective scan coverage inside the breast with efficient field focusing at the target location. The electric field focusing and temperature mapping in the two-dimensional numerical simulations are investigated using wave propagation and bio-heat transfer models respectively. The method of moments, a popular numerical simulation tool, is used to model the electric field maintained by the deformable mirrors for continuous wave excitation. The electromagnetic (EM) energy deposited by the mirrors is used in the steady state bio-heat transfer equation to quantify the temperature distribution inside two-dimensional anatomically realistic breast models.

RESULTS

Feasibility of the proposed technique is evaluated using numerical breast models derived from magnetic resonance images of patients with variation in breast density, age and pathology.

CONCLUSIONS

The computational study indicates preferential EM energy deposition and temperature elevation inside tumor tissue with minimum collateral damage to the neighboring normal tissues. Simulation results obtained for the magnetic resonance (MR) breast data appear promising and indicate the merit in pursuing the investigation using 3D computational models.

Cell classification by moments and continuous wavelet transform methods

Image processing techniques are bringing new insights to biomedical research. The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features. In this work, a simple rule-based decision tree classifier is developed to classify typical features of mixed cell types investigated by atomic force microscopy (AFM). A combination of continuous wavelet transform (CWT) and moment-based features are extracted from the AFM data to represent that shape information of different cellular objects at multiple resolution levels. The features are shown to be invariant under operations of translation, rotation, and scaling. The features are then used in a simple rule-based classifier to discriminate between anucleate versus nucleate cell types or to distinguish cells from a fibrous environment such as a tissue scaffold or stint. Since each feature has clear physical meaning, the decision rule of this tree classifier is simple, which makes it very suitable for online processing. Experimental results on AFM data confirm that the performance of this classifier is robust and reliable.

Finite-Element Neural Networks for Solving Differential Equations

The solution of partial differential equations (PDE) arises in a wide variety of engineering problems. Solutions to most practical problems use numerical analysis techniques such as finite-element or finite-difference methods. The drawbacks of these approaches include computational costs associated with the modeling of complex geometries. This paper proposes a finite-element neural network (FENN) obtained by embedding a finite-element model in a neural network architecture that enables fast and accurate solution of the forward problem. Results of applying the FENN to several simple electromagnetic forward and inverse problems are presented. Initial results indicate that the FENN performance as a forward model is comparable to that of the conventional finite-element method (FEM). The FENN can also be used in an iterative approach to solve inverse problems associated with the PDE. Results showing the ability of the FENN to solve the inverse problem given the measured signal are also presented. The parallel nature of the FENN also makes it an attractive solution for parallel implementation in hardware and software.

An incremental learning algorithm with confidence estimation for automated identification of NDE signals

An incremental learning algorithm is introduced for learning new information from additional data that may later become available, after a classifier has already been trained using a previously available database. The proposed algorithm is capable of incrementally learning new information without forgetting previously acquired knowledge and without requiring access to the original database, even when new data include examples of previously unseen classes. Scenarios requiring such a learning algorithm are encountered often in nondestructive evaluation (NDE) in which large volumes of data are collected in batches over a period of time, and new defect types may become available in subsequent databases. The algorithm, named Learn++, takes advantage of synergistic generalization performance of an ensemble of classifiers in which each classifier is trained with a strategically chosen subset of the training databases that subsequently become available. The ensemble of classifiers then is combined through a weighted majority voting procedure. Learn++ is independent of the specific classifier(s) comprising the ensemble, and hence may be used with any supervised learning algorithm. The voting procedure also allows Learn++ to estimate the confidence in its own decision. We present the algorithm and its promising results on two separate ultrasonic weld inspection applications.

Scanning probe microscopy with landmark referenced control for direct biological investigations

We report the successful use of continuous wavelet transforms applied to atomic force microscope data sets for landmark recognition of biological features. The data sets were images of mixed red and white blood cells. Contrast enhancement followed by continuous wavelet transform of the data was used to successfully distinguish erythrocytes from neutrophil and monocyte leukocytes within the mixed cell images. All of the above are spherical objects between 6 and 8 microns in diameter, which demonstrates the ability to sort similar biological objects into distinct classes. The implications for development of on-line scanning probe recognition microscopy are discussed.

Boundary detection using simulation of particle motion in a vector image field

This paper introduces a novel approach in image processing based on a vector image model. A major advantage of the model is that it allows vector operations to be performed on an image. An example of a vector operation is the computation of mechanical moments for detecting inhomogeneities in an object or equivalently edges in an image. A new edge operator derived from a vector image model yields an edge vector field analogous to the Hamiltonian gradient field of the image. The distinct feature of the edge vector field is that edge vectors form current loops encompassing the objects. This feature is exploited to develop a new boundary extraction algorithm based on particle motion in a force field. The edge vector field forces a particle to move along the edges while an orthogonal normalized Laplacian gradient vector field guarantees that the particle will not drift away from the edges. The object boundary can be obtained from the convergent path of the particle trajectory. Using a fine stepping factor, the extracted boundary can achieve subpixel accuracy. The proposed algorithm has major advantages over the conventional edge-detection, edge-thinning, and edge-linking techniques in that it effectively utilizes both direction and magnitude of edges. The algorithm is simple, robust and performs very well even on high curvature objects.

Frequency invariant classification of ultrasonic weld inspection signals

Automated signal classification systems are finding increasing use in many applications for the analysis and interpretation of large volumes of signals. Such systems show consistency of response and help reduce the effect of variabilities associated with human interpretation. This paper deals with the analysis of ultrasonic NDE signals obtained during weld inspection of piping in boiling water reactors. The overall approach consists of three major steps, namely, frequency invariance, multiresolution analysis, and neural network classification. The data are first preprocessed whereby signals obtained using different transducer center frequencies are transformed to an equivalent reference frequency signal. Discriminatory features are then extracted using a multiresolution analysis technique, namely, the discrete wavelet transform (DWT). The compact feature vector obtained using wavelet analysis is classified using a multilayer perceptron neural network. Two different databases containing weld inspection signals have been used to test the performance of the neural network. Initial results obtained using this approach demonstrate the effectiveness of the frequency invariance processing technique and the DWT analysis method employed for feature extraction.