An Aeromagnetic Compensation Method for Suppressing the Magnetic Interference Generated by Electric Current with Vector Magnetometer.

In aeromagnetic detection, the magnetic interference conducted by electric currents in onboard electronic (OBE) equipment is gradually being taken seriously with the development of aeromagnetic compensation technology. Here, we propose a compensation method based on the synthetically total magnetic field (STMF) measured by an onboard fluxgate vector magnetometer. In this method, a compensation model is firstly built to suppress the electric current magnetic interference (ECMI) which is jointly measured by a scalar magnetometer and a fluxgate vector magnetometer. The singular spectrum analysis (SSA) method is introduced to accurately extract the characteristic signal of the ECMI from the compensated STMF. In addition, in order to better suppress the geomagnetic gradient interference, the International Geomagnetic Reference Field (IGRF) model is introduced to modify the existing geomagnetic gradient compensation model. Based on these, a novel compensation model including the traditional aeromagnetic compensation model, modified geomagnetic gradient model, and ECMI compensation model is proposed. The results in the field experiment show that this model has better compensation performance than the TLG model, which is extended from the T-L compensation model.

Automatic Video Analysis Framework for Exposure Region Recognition in X-Ray Imaging Automation.

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.

Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method.

PURPOSE: The expression of human epidermal growth factor receptor 2 (HER2) in breast cancer is critical in the treatment with targeted therapy. A 3-block-DenseNet-based deep learning model was developed to predict the expression of HER2 in breast cancer by ultrasound images. METHODS: The data from 144 breast cancer patients with preoperative ultrasound images and clinical information were retrospectively collected from the Shandong Province Tumor Hospital. An end-to-end 3-block-DenseNet deep learning classifier was built to predict the expression of human epidermal growth factor receptor 2 by ultrasound images. The patients were randomly divided into a training (n = 108) and a validation set (n = 36). RESULTS: Our proposed deep learning model achieved an encouraging predictive performance in the training set (accuracy = 85.79%, AUC = 0.87) and the validation set (accuracy = 80.56%, AUC = 0.84). The effectiveness of our model significantly exceeded the clinical model and the radiomics model. The score of the proposed model showed significant differences between HER2-positive and -negative expression (p < 0.001). CONCLUSIONS: These results demonstrate that ultrasound images are predictive of HER2 expression through a deep learning classifier. Our method provides a non-invasive, simple, and feasible method for the prediction of HER2 expression without the manual delineation of the regions of interest (ROI). The performance of our deep learning model significantly exceeded the traditional texture analysis based on the radiomics model.

Bioinspired dynamic soaring simulation system with distributed pressure sensors.

Inspired by the albatross, this paper presents the construction of a dynamic soaring simulation system with distributed pressure sensors. The advantage of our system lies in harvesting energy from the wind shear layer and estimating the wind information using a pressure-based sensor system. Specifically, the dynamic soaring simulation system contains an offline training stage and an online estimation and control stage. In the offline training stage, computational fluid dynamics simulations are conducted and used as the data source. A surrogate model is established to correlate the local flow conditions and the surface pressure at optimal sensor positions. In the online estimation and control stage, through sensing the pressure information, the real-time wind velocity and wind gradient are estimated by the surrogate model trained in the offline stage. Moreover, wind information is adopted in the simulation of dynamic soaring control. In this study, the simulation system was applied to linear and circular path-following tasks. It was found that the dynamic soaring simulation system with distributed pressure sensors provides an acceptable estimation of wind velocity and wind gradient with a certain time delay caused by numerical differentiation.

An Aeromagnetic Compensation Method Based on a Multimodel for Mitigating Multicollinearity.

Aeromagnetic surveys play an important role in geophysical exploration and many other fields. In many applications, magnetometers are installed aboard an aircraft to survey large areas. Due to its composition, an aircraft has its own magnetic field, which degrades the reliability of the measurements, and thus a technique (named aeromagnetic compensation) that reduces the magnetic interference field effect is required. Commonly, based on the Tolles-Lawson model, this issue is solved as a linear regression problem. However, multicollinearity, which refers to the case when more than two model variables are highly linearly related, creates accuracy problems when estimating the model coefficients. The analysis in this study indicates that the variables that cause multicollinearity are related to the flight heading. To take this point into account, a multimodel compensation method is proposed. By selecting the variables that contribute less to the multicollinearity, different sub-models are built to describe the magnetic interference of the aircraft when flying in different orientations. This method restricts the impact of multicollinearity and improves the reliability of the measurements. Compared with the existing methods, the proposed method reduces the interference field more effectively, which is verified by a set of airborne tests.

Electromagnetic Fields Due to the Wake of a Moving Slender Body in a Finite-Depth Ocean with Density Stratification.

Weak electric currents are induced in moving seawater by cutting the geomagnetic fields. These electric currents can produce measurable electromagnetic fields that may be used for some purposes such as monitoring of ocean internal waves. This article is aimed at presenting the procedure to calculate the electromagnetic fields owing to the wake raised by an undersea moving slender body. A pair of Havelock point sources are introduced to model the moving body, which generate the similar wake at places far from the body. The ocean is taken to be of finite-depth with density stratification due to thermocline. Three distinct forms of water-flow wake can be identified, the free-surface Kelvin wake, the internal interfacial wake, and the localized volume wake. The electric currents evoked by the motional wake may produce observable electromagnetic fields, which may be solved using rigorous electromagnetic field theory. At the sea level, the magnitudes of the induced electric field and magnetic field are on the order of a few microvolts per meter and one nano-Tesla, respectively, which are appreciable in terms of nowadays marine electric and magnetic sensors.