Thomson Scattering in the Lower Corona in the Presence of Sunspots

Polarized scattered light from low (few tens of megameter altitudes) coronal transients has been recently reported in Solar Dynamics Observatory/Helioseismic and Magnetic Image (HMI) observations. In a classic paper, Minnaert (1930) provided an analytic theory of polarization via electron scattering in the corona. His work assumed axisymmetric input from the photosphere with a single-parameter limb-darkening function. This diagnostic has recently been used to estimate the free-electron number and mass of HMI transients near the solar limb, but it applies equally well to any coronal material, at any height. Here we extend his work numerically to incorporate sunspots, which can strongly effect the polarization properties of the scattered light in the low corona. Sunspot effects are explored first for axisymmetric model cases, and then applied to the full description of two sunspot groups as observed by HMI. We find that (1) as previously reported by Minnaert, limb darkening has a strong influence, usually increasing the level of linear polarization tangential to the limb; (2) unsurprisingly, the effects of the sunspot generally increase at the lower scatterer altitudes, and increase the larger the sunspot is and the closer to their center the scatterer subpoint is; (3) assuming the Stokes Q > 0 basis to be tangential to the limb, sunspots typically decrease the Stokes Q/I polarization and the perceived electron densities below the spotless case, sometimes dramatically; and (4) typically, a sizeable non-zero Stokes U/I polarization component will appear when a sunspot’s influence becomes non-negligible. However, that is not true in rare cases of extreme symmetry (e.g., scattering mass at the center of an axisymmetric sunspot). The tools developed here are generally applicable to an arbitrary image input.

Recovering the Magnetic Image of Mars from Satellite Observations

One of the possible approaches to reconstructing the map of the distribution of magnetization parameters in the crust of Mars from the data of the Mars MAVEN orbiter mission is considered. Possible ways of increasing the accuracy of reconstruction of the magnetic image of Mars are discussed.

Nuclear Magnetic Image Segmentation of Rectal Cancer Based on Improved Genetic Neural Network

Image segmentation is the basis of image analysis and understanding, and has an unshakable position in the field of computer vision. In order to improve the accuracy of nuclear magnetic image segmentation of rectal cancer, this paper proposes an improved genetic neural network algorithm for the problems of traditional BP neural network algorithm. In order to enhance the network performance, this paper improves the genetic neural network from the two aspects of fitness function and genetic operator, which makes the training speed and convergence precision greatly improved. Target samples were analyzed by image histogram analysis, and the improved genetic neural network was used to learn the samples to obtain the training network. Taking the pixel matrix of the image as the input vector, it is put into the trained network for classification, and finally the segmentation is realized. The simulation experiment proves that compared with the classical image segmentation method, the improved genetic neural network image segmentation method has a good segmentation effect and is a feasible image segmentation method.

Quantitative Nondestructive Testing of Wire Ropes Based on Features Fusion of Magnetic Image and Infrared Image

Magnetic flux leakage (MFL) detection is one of the most widely used and best performing wire rope nondestructive testing (NDT) methods for more than a decade. However, the traditional MFL detection has the disadvantages of single source of information, low precision, easy to miss detection, and false detection. To solve these problems, we propose a method of fusion recognition of magnetic image features and infrared image features. A denoising algorithm based on Hilbert vibration decomposition (HVD) and wavelet transform is proposed to denoise the MFL signal, and the modulus maxima method is used to locate and segment the defect. An infrared image acquisition system was designed to collect the infrared image of the surface of the wire rope. Digital image processing techniques are used to segment infrared defect images. The features of the MFL image and the infrared image are extracted separately for fusion. The fusion feature is input into the nearest neighbor (NN) algorithm for quantitative identification, and the same data are input into the backpropagation (BP) neural network for comparison verification. The experimental results show that the fusion of MFL features and infrared features effectively improves the recognition rate of wire rope defects and reduces the recognition error.

Difference in real-time magnetic image analysis of colonic looping patterns between males and females undergoing diagnostic colonoscopy

Background and study aim Magnetic imaging technology is of proven benefit to trainees in colonoscopy, but few studies have examined its benefits in experienced hands. There is evidence that colonoscopy is more difficult in women. We set out to investigate (i) associations between the looping configurations in the proximal and distal colon and (ii) differences in the looping prevalence between the sexes. We have examined their significance in terms of segmental intubation times and position changes required for the completion of colonoscopy. Patients and methods We analyzed 103 consecutive synchronized luminal and magnetic image videos of diagnostic colonoscopies with normal anatomy undertaken by a single experienced operator. Results Deep transverse loops and sigmoid N-loops were more common in females. A deep transverse loop was more likely to be present if a sigmoid alpha-loop or N-loop had formed previously. Patients with sigmoid N-loops were turned more frequently from left lateral to supine before the sigmoid-descending junction was reached, but there was no statistical correlation between completion time and looping pattern. Conclusions This study has reexamined the prevalence of the common looping patterns encountered during colonoscopy and has identified differences between the sexes. This finding may offer an explanation as to why colonoscopy has been shown to be more difficult in females. Although a deep transverse loop following a resolved sigmoid alpha-loop was the most commonly encountered pattern, no statistical correlation between completion time and looping pattern could be shown. It is the first study to examine segmental completion times using a magnetic imager in expert hands.

Undersampled MR Image Reconstruction with Data-Driven Tight Frame

Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

Identifying effective interpretation methods for magnetic data by profiling and analyzing human data interactions

Geoscientific data interpretation is a highly subjective and complex task because human intuition and biases play a significant role. Based on these interpretations, however, the mining and petroleum industries make decisions with paramount financial and environmental implications. To improve the accuracy and efficacy of these interpretations, it is important to better understand the interpretation process and the impact of different interpretation techniques, including data processing and display methods. As a first step toward this goal, we aim to quantitatively analyze the variability in geophysical data interpretation between and within individuals. We carried out an experiment to analyze how individuals interact with magnetic data during the process of identifying prescribed targets. Participants performed two target spotting exercises where the same magnetic image was presented at different orientations. The task was to identify the magnetic response from porphyry-style intrusive systems. The experiment involved analyzing the data observation pattern during the interpretation process using an eye tracker system that captures the interpreter’s eye gaze motion and the target-spotting performance. The time at which targets were identified was also recorded. Fourteen participants with varying degrees of experience and expertise participated in this study. The results show inconsistencies within and between the interpreters in target-spotting performance. The results show a correlation between a systematic data observation pattern and target-spotting performance. Improved target-spotting performance was obtained when the magnetic image was observed from multiple orientations. These findings will help to identify and quantify the effective interpretation practices, which can provide a roadmap for the training of geoscientific data interpreters and contribute toward the understanding of the uncertainties in the data interpretation process.