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.

Ultra-Wideband Microwave Imaging System for Root Phenotyping.

The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method's feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system's capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system's potential for root imaging.

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.

Negative Index Metamaterial Lens for Subwavelength Microwave Detection.

Metamaterials are engineered periodic structures designed to have unique properties not encountered in naturally occurring materials. One such unusual property of metamaterials is the ability to exhibit negative refractive index over a prescribed range of frequencies. A lens made of negative refractive index metamaterials can achieve resolution beyond the diffraction limit. This paper presents the design of a metamaterial lens and its use in far-field microwave imaging for subwavelength defect detection in nondestructive evaluation (NDE). Theoretical formulation and numerical studies of the metamaterial lens design are presented followed by experimental demonstration and characterization of metamaterial behavior. Finally, a microwave homodyne receiver-based system is used in conjunction with the metamaterial lens to develop a far-field microwave NDE sensor system. A subwavelength focal spot of size 0.82λ was obtained. The system is shown to be sensitive to a defect of size 0.17λ × 0.06λ in a Teflon sample. Consecutive positions of the defect with a separation of 0.23λ was resolvable using the proposed system.

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.

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.

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'.

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.

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.

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.

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.