Elliptical Image Research Articles

Elliptic tangent imaging method for layered plate interfacial defects based on non-dispersive guided waves

Layered structures play an increasingly pivotal role in diverse fields such as aerospace, pipeline transportation, and petrochemical engineering. However, defects situated at the interfaces between layers pose a challenge for conventional Non-Destructive testing (NDT) methods, impeding accurate imaging. This research introduces the elliptic tangent imaging method based on interface wave modes to visualize interfacial defects within layered structures. The interface wave mode, characterized by high-frequency non-dispersive traits, facilitates precise localization of defects. Its energy concentration in the interface enhances sensitivity to interfacial defects. The elliptic tangent imaging method proposed in this paper capitalizes on the transmission coefficients of the oblique incidence technique for surface waves, enabling the concurrent determination of defect location and orientation using one pair of transducers. The application of the orthogonal matched pursuit algorithm for sparse representation of the raw signal further enhances positioning accuracy. This paper establishes an automatic detection system for interface waves, demonstrating swift transducer switching. Experimental validation of wave velocity aligns closely with theoretical calculations. In experiments focusing on interfacial defect imaging in bilayer aluminum-steel plates, the system exhibits the capability to accurately depict interfacial defects with varying orientations.

Image based ECC Mutual Authentication Scheme for Cloud Assisted TMIS

In this modern era, cloud-based services like e-commerce, e-gate, and so on provide immense services to humans. Healthcare centers are gradually moving to cloud-based services. In which, both the hospital and patients are connected remotely online and patient gets treatment quickly. Increasing the demand in Telecare Medical Information System (TMIS) needs to ensure the security and privacy of the healthcare centers and patients’ information. In this paper, we have proposed an e?cient and provably secure Elliptic Curve cryptography image based mutual authentication scheme for cloud assisted TMIS. The proposed authentication schemes ensure the secured treatment provided to patients from healthcare center through online. The patient can upload their health condition data to cloud via mobile device for the treatment. The proposed authentication scheme required minimum computational cost with minimum communication overhead. The proposed authentication scheme preserves patient anonymity and withstands the known and chosen plaintext attack. The security analysis for the proposed scheme shows that the proposed authentication scheme is more secure. It shows that the proposed authentication scheme is performing well compare to the related authentication schemes.

Advances on the detection and measurement of bubble contours during subcooled boiling in microgravity

A method for the detection of bubble contours in images of subcooled boiling in microgravity is proposed. The method refines an initial, rough contour approximation obtained from conventional background subtraction. This refinement entails the warping of an elliptical ring image region into a polar image, where bubble contour detection becomes simpler. The proposed method exhibits robustness to a wide variety of visual phenomena that typically hinder the detection of bubble contours. This is achieved by the grouping of continuous image edges. A ground truth annotated data set is provided that enables the quantitative and comparative evaluation of the proposed methods.

Discrete non-elliptical probability imaging based on UGWs for anisotropic anti-corrosive structure damage detection of rim-driven thruster

Aiming at health monitoring for anisotropic anti-corrosion structure of rim-driven thruster (RDT), a discrete non-elliptical probability imaging (DNEPI) based on ultrasonic guided waves is proposed. The elliptical location without analytical solution for anisotropic structures is analyzed theoretically. Through reverse thinking, it’s avoided by discretization and introducing reference point. Combined with Hausdorff damage sensitivity index (DSI), the damage probability is estimated. All paths’ probability is fused and displayed by images. The proposed DNEPI is verified by experiments. The results show that the damage probability energy ratios (Er) of DNEPI (Hausdorff DSI and scattering DSI) and elliptical probability imaging (EPI) are 0.29%, 0.22%, and 0.26% respectively, and improved to 4.55%, 1.89%, and 1.10% with 95% threshold. Compared with EPI, the Er of DNEPI imaging is improved by 3.45%. The robustness of Hausdorff DSI is better than scattering DSI. The proposed DNEPI not only visually presents the damage but also provides higher accuracy.

An intelligent colony optimization imaging method for composites health monitoring using ultrasonic guided wave

Damage imaging technology based on ultrasonic guided wave can characterize and visualize structural damage. However, current imaging methods require high accuracy in wave packets information extraction, resulting in artifacts, large spots and false imaging. Therefore, an intelligent colony optimization imaging method is proposed to transform the damage imaging problem into the scattering sources search problem. The number of imaging trajectories through potential scattering sources is employed as the fitness function to assess the damage possibility in this individual. The fitness function is further implemented to particle swarm optimization algorithm for iterative search of actual scattering sources to find out the damage area with highest damage possibility. After the scattering sources search, the location of the damage can be identified through observing the distribution of the population. The experimental results illustrate that the proposed intelligent imaging method can accurately locate the crack damage of different lengths and delamination damage in composites, and the damage imaging effect is better than that of elliptic imaging.

Elliptic curve cryptographic image encryption using Henon map and Hopfield chaotic neural network

Elliptic curve cryptographic image encryption using Henon map and Hopfield chaotic neural network

Research and application of Rayleigh wave extraction method based on microtremors signal analysis

This study investigates the method and application of single-channel, three-component microtremor signal co-directional Rayleigh surface-wave extraction. The research focuses on filtering linear polarization waves using polarizability wave and phase-difference filtering, which were analyzed based on both simulated data and real microtremor signals. Additionally, the study examines the use of time-frequency analysis to analyze microtremor signals and identify Rayleigh wave propagation direction. The combination of these methods leads to a set of procedures for extracting high-SNR co-directional Rayleigh surface waves from microtremor signals, which was applied to the elliptical polarizability imaging method. Results indicate that the proposed data processing process effectively filters linear polarization waves and accurately determines the propagation direction of the Rayleigh wave, leading to significant improvement in the accuracy of elliptical polarizability exploration results. This provides a reference for obtaining high signal-to-noise ratio data in microtremor Rayleigh wave seismic exploration.

Accelerated Back-Projection SAR Processor on Arbitrary Elliptical Imaging Grid With Azimuth Spectrum Unfolding

Accelerated Back-Projection SAR Processor on Arbitrary Elliptical Imaging Grid With Azimuth Spectrum Unfolding

Intelligent defect location of a U-shaped boom using helical guided waves

Truck cranes are indispensable heavy-duty loading and unloading equipment in industrial production. A boom is the key load-bearing component of a truck crane, and its health has a vital influence on a crane’s lifting performance and safety in production. Therefore, it is urgent to develop a structural health monitoring (SHM) method for a boom structure. In this research, an improved intelligent defect location algorithm based on helical guided waves is applied to the defect detection of a U-shaped boom. The improved intelligent algorithm is a defect location algorithm based on the ellipse imaging principle, which combines an evolutionary algorithm with a K-means algorithm and can identify the location of defects through the distribution of individuals. According to the propagation characteristics of the helical guided waves in the structure of a U-shaped boom, an optimization scheme for the improved intelligent defect location algorithm is proposed that fully considers the fact that the group velocity of the helical guided waves varies with the wall thickness, and the corresponding group velocity is used to accurately calculate the arrival time of each initial individual to improve the accuracy of the defect location. Numerical simulations and experiments are conducted to verify the effectiveness of the proposed improved intelligent defect location algorithm in the defect location algorithm. When only one group velocity is considered, the improved intelligent defect location algorithm is in good agreement with the elliptical imaging algorithm. When the group velocities for different wall thicknesses are fully considered, the detection results of the optimized intelligent defect location algorithm have a higher resolution. This optimization algorithm provides a tool for the SHM of a U-shaped boom based on guided waves.

Investigation and Analysis of EM Pulse Propagation Inside Human Head for High-Resolution UWB Elliptical SAR Imaging

In this paper, an ultra-wideband (UWB) microwave imaging system based on an elliptical synthetic aperture radar (ESAR) is proposed as a possible solution for human head imaging. The system along with the image reconstruction method operates in the time domain. We have adopted the global back-projection technique to an elliptical trajectory of data acquisition, to shape around the head. A miniaturized, low profile custom-designed, UWB patch antenna capable of operating in the proximity of the head tissues is used as a sensor. The antenna aperture impedance is matched to the head tissues to enable pumping radiating electromagnetic waves into the human head needles of any coupling liquid. The impact of operational bandwidth and radiated power on system key performance factors i.e., penetration depth, range-resolution, and safety aspects are fully investigated based on calculations as well as full-wave simulations. It is observed that by increasing the bandwidth of radiated pulse from 0.8∼2.8 GHz (which was used in most of the previous works) to 1.0∼5.2 GHz, the range resolution can be improved remarkably from 23.0 mm to 16.0 mm deep inside the head. However, further increase in operational bandwidth will not improve the resolution of the system and a significant fraction of the radiated power will be dissipated in the brain tissues. To demonstrate the system performance, a constellation of 12 antenna elements is placed in an elliptical shape directly attached to the 3D phantom to acquire the raw data using full-wave simulations. Finally, an image from a 3D MRI-derived phantom is reconstructed to validate the imaging capability of the designed system. The proposed system can be used as a tool for early diagnosis as well as for treatment monitoring while getting therapy like chemo or radiotherapies.

A New Probabilistic Ellipse Imaging Method Based on Adaptive Signal Truncation for Ultrasonic Guided Wave Defect Localization on Pressure Vessels.

Pressure vessels are prone to defects due to environmental conditions, which may cause serious safety hazards to industrial production. The probabilistic ellipse imaging method, based on ultrasonic guided wave, is a common method for locating defects on plate-like structures. In this paper, the research showed that the accuracy of the traditional probabilistic ellipse imaging method was severely affected by the truncation length of the signal. In order to improve the defect location accuracy of the probabilistic elliptic imaging algorithm, an adaptive signal truncation method based on signal difference analysis was proposed, and a novel probabilistic elliptic imaging method was developed. Firstly, the relationship model between the signal difference coefficient (SDC) and the distance coefficient was constructed. Through this model, the distance coefficient of each group signal can be calculated, so that the adaptive truncation length for each group of signals can be determined and the truncated signals used for defect imaging. Secondly, in order to improve the robustness of the new imaging method, the relationship between the defect location accuracy and SDC thresholds were investigated and the optimal threshold was determined. The experimental results showed that the probabilistic ellipse imaging algorithm, based on the new adaptive signal truncation method, can effectively locate a single defect on a pressure vessel.

Elliptical curve cryptography image encryption scheme with aid of optimization technique using gravitational search algorithm

<p>Image <span>encryption enables users to safely transmit digital photographs via a wireless medium while maintaining enhanced anonymity and validity. Numerous studies are being conducted to strengthen picture encryption systems. Elliptical curve cryptography (ECC) is an effective tool for safely transferring images and recovering them at the receiver end in asymmetric cryptosystems. This method's key generation generates a public and private key pair that is used to encrypt and decrypt a picture. They use a public key to encrypt the picture before sending it to the intended user. When the receiver receives the image, they use their private key to decrypt it. This paper proposes an ECC-dependent image encryption scheme utilizing an enhancement strategy based on the gravitational search algorithm (GSA) algorithm. The private key generation step of the ECC system uses a GSA-based optimization process to boost the efficiency of picture encryption. The image's output is used as a health attribute in the optimization phase, such as the peak signal to noise ratio (PSNR) value, which demonstrates the efficacy of the proposed approach. As comparison to the ECC method, it has been discovered that the suggested encryption scheme offers better optimal PSNR </span>values.</p>

Optimization with learning-informed differential equation constraints and its applications

Inspired by applications in optimal control of semilinear elliptic partial differential equations and physics-integrated imaging, differential equation constrained optimization problems with constituents that are only accessible through data-driven techniques are studied. A particular focus is on the analysis and on numerical methods for problems with machine-learned components. For a rather general context, an error analysis is provided, and particular properties resulting from artificial neural network based approximations are addressed. Moreover, for each of the two inspiring applications analytical details are presented and numerical results are provided.

Ultrasound PIV Uncertainty Quantification

Ultrasound Particle image velocimetry (UPIV) is a non-invasive flow measurement technique where acousticopaque flow tracers are injected into a working fluid and ensonified to create ultrasound images. These images are processed using PIV cross-correlation based algorithms to measure the velocity field (Kim et al., 2004). UPIV is useful for opaque flows, primarily where complex flows exist, accordingly, it is used in many industrial and clinical research applications such as studying intracardiac flow (Crase et al., 2007). Furthermore, the measurement provides suitable temporal and spatial resolutions for improved diagnostic metrics. Mentioned applications and the sensitive diagnostic industrial and clinical decisions made based on these measurements intensifies the importance of characterizing the UPIV measurement accuracy and associated uncertainty. However, quantifying UPIV measurement uncertainty is non-trivial due to the complexity of possible uncertainty sources, their combination, and propagation through the measurement chain.
 The formation of a particle image by ultrasound significantly differs from optical imaging, introducing unique aspects to image quality that must be considered. Particle images are formed across several ultrasound scan lines, yielding an elliptical particle image shape. Furthermore, the particle’s reflected pressure wave is converted to a digital signal that undergoes signal modulation, and this process forms a non-Gaussian point spread function (PSF) along the scan line direction. Additionally, clusters of tracers produce a single, bright image intensity and speckle image pattern. Compared to conventional PIV images, UPIV incurs significantly higher image noise due to lack of filtration for the ultrasound reflection of the non-tracer obstacles.

Evolutionary Strategy-Based Location Algorithm for High-Resolution Lamb Wave Defect Detection With Sparse Array.

Intelligent defect location algorithms based on the times-of-flight (ToFs) of Lamb waves are attractive for nondestructive testing (NDT) and structural health monitoring (SHM) of structures with large geometric sizes. Unlike the classical imaging algorithm based on projecting the amplitude information of scattering signals into a discrete spatial grid on the structure via their propagation characteristics, intelligent defect location algorithms are more efficient in specific applications. In our previous work, an intelligent algorithm for the location of defects in plates was proposed by considering the statistical, diversity, and fuzzy characteristics of the classical defect location algorithm. This approach can realize the efficient location of different defects under a suitable parameter selection. However, interfering components remain in the results, which decreases the detection resolution. Because the measurement uncertainty is directly related to the time, an optimized intelligent location algorithm is provided for the efficient defect location with Lamb waves and a sparse transducer array in this study. The defect position is identified with high resolution by analyzing the distribution of individuals. Several specific data and a fuzzy control parameter are introduced to the proposed algorithm. The K-means algorithm was adopted to realize the adaptive updating of individuals. The influence of parameter values on the detection results was analyzed. A combined analysis of the individuals was provided to ensure the detection robustness by eliminating the influence of fuzzy control parameters on the detection. Compared with the elliptic imaging algorithm, the intelligent defect location algorithm has higher location resolution and executes approximately 65 times faster.

Ellipse detection-based visual servo control for capturing upper-stage rocket body

The quantity of space debris in orbit around the Earth including separated rockets, malfunctioning satellites, and small particles is continuously increasing. One such type of space debris is the upper stage of the H-series rockets, which has a joint, called the Payload Adapter Fitting (PAF), where the satellite to be deployed is connected. Because the PAF is circular, it offers a natural and desirable target marker for detecting the rocket stage attitude and capture the stage using image processing. Traditional visual servo control based on a single elliptical image of a circular feature cannot determine the normal direction of the PAF and is unable to maintain the imaged ellipse inside the camera field of view as the motion in the optical axis direction may exceed the desired speed. To overcome these problems, a method of control gain switching in visual servo control based on two elliptical images of two circular features of the PAF is proposed, in which the convergence of the projected ellipse to a circle is prioritized, followed by forward motion to adjust the relative distance. A 1/10 scale PAF model and a six degrees-of-freedom robotic manipulator equipped with a camera on its end-effector were used to validate the proposed control method. The experimental results show that the convergence time for the proposed method is shorter than that without control gain switching.

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first; then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter setting on the detection.

Influence of Boundary Deformation on Image Reconstruction in Electrical Impedance Tomography

For electrical impedance tomography (EIT) lung imaging, chest expansion and contraction could change the electrode placement and deform the boundary of the image area, which changes the electric field distribution and distorts the reconstructed images. EIT images of lung function often produce severe artifacts due to the expansion of the thoracic cage, so it is necessary to study the effect of thoracic boundary deformation on EIT images. In this paper, we established several elliptical image reconstruction models, which deform from 1% to 15% in the anterior–posterior (AP) dimension from the circle field domain, to emulate chest expansion. The Quantitative evaluation of the influence of the deformation on the reconstruction images has been made. Images are estimated by parameter DORI and SSIM. The results show that these two parameters can evaluate image quality effectively, and image error increases with the increasing of boundary deformation. Although the error is small when the deformation extent of the boundary shape is small, images are seriously distorted when the deformation increases. Image error increases significantly when the boundary deformation increases, which may lead to image artifacts and maybe result in misdiagnosis.

Serial and Parallel Active Decoupling Characterization Using RF MEMS Switches for Receiver Endoluminal Coils at 1.5 T

MEMS (Micro Electro Mechanical System) switches were assessed and compared to PIN diode in fulfilling the task of active decoupling of Receiver Endoluminal Coils (RECs). Three prototype RECs with the PIN diode in parallel ( ${p}$ PIN), MEMS in parallel ( ${p}$ MEMS) and MEMS in series ( ${s}$ MEMS) with the REC loop were built. Quality factors (Q-values), decoupling efficiency and switching delays were characterized on bench and Signal-to-Noise Ratios (SNRs) established on images at 1.5 T. Q-values were equal to 62.5, 41.2 and 65.1 for ${p}$ PIN, ${s}$ MEMS and ${p}$ MEMS, respectively. In the decoupled state, reflection coefficients S11 and S21 at resonance frequency both indicated proper decoupling. Switching delays were less than $0.7~\mu \text{s}$ and $10~\mu \text{s}$ for ${p}$ PIN and MEMS RECs, respectively. Decoupling/coupling delays of MEMS remained compatible with most Magnetic Resonance (MR) clinical applications. For all prototypes, MR images displayed no signal saturation and similar elliptical image sensitivity patterns. No artifacts due to active decoupling failure were observed. Mean SNR values obtained with ${p}$ MEMS REC were higher than those obtained with ${s}$ MEMS REC but lower than with ${p}$ PIN REC because of the use of additional instrumentation to render the scanner compatible with the MEMS utilization. MEMS in parallel are an interesting alternative to PIN diode for decoupling and could lead to better SNR with a compatible MR system (dedicated control signal). The MEMS in series can be used for both decoupling and reconfiguration of the REC loop geometry for colon wall examination.

Synchronous Software Control over 62 Heliostats for Enhancing Function Capabilities of a Big Solar Furnace

A comparative analysis of the performance accuracy of the tracking systems of process and energy heliostats was carried out based on the long-term experience of operating a two-mirror big solar furnace (BSF). It is shown that a new additional automatic control system (ACS) is being developed for the upgrade intended to expand the function capabilities of the BSF. The automated control system must maintain dynamics while simultaneously tracking the intersection of two axes of elliptical optical images of the Sun or other celestial objects in the focus of the concentrator from all 62 heliostats simultaneously. In addition, the software-based ACS in development should maintain the previously embedded functions of the automatic temperature control system (ATCS) with the departure of the center of elliptical optical images from all 62 heliostats within 25 angular minutes in order to expand the zone of uniform density. It is proven that the newly developed system of software-based management of heliostats must expand their function capabilities. The optical and geometric parameters (manufacturing accuracy, focal length, and aperture angle) of this installation allow using the solar installation as an astronomical telescope with a midsection size of 42 × 54 m2 for registering secondary Cherenkov radiation from gamma-ray sources in the night sky, creating a favorable zone for obtaining a powerful stream of laser beams, as well as efficiently synthesizing molecular hydrogen by thermolysis and electrolysis. It is shown that the automatic software control packages already created or in the process of being created must synchronously and regularly ensure the coincidence of centers of the Sun’s optical images, which have their own variable shapes and densities, depending on the value of the aperture angle with the focal point of the concentrator, for each of the 62 heliostats.