Segmentation System Research Articles

PARCS: A Deployment-Oriented AI System for Robust Parcel-Level Cropland Segmentation of Satellite Images

Cropland segmentation of satellite images is an essential basis for crop area and yield estimation tasks in the remote sensing and computer vision interdisciplinary community. Instead of common pixel-level segmentation results with salt-and-pepper effects, a parcel-level output conforming to human recognition is required according to the clients' needs during the model deployment. However, leveraging CNN-based models requires fine-grained parcel-level labels, which is an unacceptable annotation burden. To cure these practical pain points, in this paper, we present PARCS, a holistic deployment-oriented AI system for PARcel-level Cropland Segmentation. By consolidating multi-disciplinary knowledge, PARCS has two algorithm branches. The first branch performs pixel-level crop segmentation by learning from limited labeled pixel samples with an active learning strategy to avoid parcel-level annotation costs. The second branch aims at generating the parcel regions without a learning procedure. The final parcel-level segmentation result is achieved by integrating the outputs of these two branches in tandem. The robust effectiveness of PARCS is demonstrated by its outstanding performance on public and in-house datasets (an overall accuracy of 85.3% and an mIoU of 61.7% on the public PASTIS dataset, and an mIoU of 65.16% on the in-house dataset). We also include subjective feedback from clients and discuss the lessons learned from deployment.

Dynamic adjustment of submaximal effort soccer side-foot kicks

ABSTRACT We aimed to illustrate kicking leg dynamics during submaximal effort soccer side-foot kicks. Side-foot kicks with three effort levels (50, 75 and 100% effort levels based on maximal effort) of eight male university soccer players were captured (500 Hz) while initial ball velocities were monitored simultaneously. Systematic regulation in joint kinetics (angular impulses) was clearly demonstrated for hip flexion and knee extension moments thereby supporting the interpretation that the final foot velocity is controlled in a context of a planar, sequential segmental system. Out of the thigh-shank plane motion (hip external rotation moment) was also found to be systematically adjusted. Kinematic contributions of knee extension angular velocity to the final foot velocity increased significantly in the maximal effort while that of hip external rotation reduced significantly, coinciding with a more straightforward approach-run. The adjustable range of the foot-ball interaction was found to be rather smaller in side-foot kicks. However, significantly smaller ball/foot velocity ratios in the two submaximal conditions suggested ankle joint fixation was manipulated towards ball impact. Players and coaches ought to recognise that the intensities of side-foot kicks were regulated by the motions within and without the thigh-shank plane alongside several kinematic changes.

P-166 The association between pronuclei parameters and blastocyst formation — a pixel-level time-lapse imaging study based on AI segmentation

Abstract Study question Do the morphokinetics of pronuclei in zygote associate with embryo developmental potential? Summary answer The position, distance to the central, size and fading time point of pronuclei were associated with blastocyst formation. What is known already Pronucleus is the nucleus formed by sperm or oocyte during fertilization containing the genetic material, which is important for further embryo development. We previously developed an AI-based morphology segmentation system for time-lapse imaging and it is able to capture the morphology changes and movement of pronuclei at the pixel level with high accuracy. In this study, we analysed the pixel-level morphokinetics of pronuclei with the blastocyst formation of the embryo under time-lapse incubation. Study design, size, duration The study was conducted in a university-affiliated IVF clinic from 2017 to 2019. There were 631 normal fertilized embryos with known blastulation results. They were cultured in the time-lapse incubator and taken images every 10 minutes from fertilization to embryo transfer/embryo freezing/discard. The images were extracted and the area of each pronucleus was segmented by the AI system. We reconstructed the segmented area of the pronucleus and count the morphokinetics parameters. Participants/materials, setting, methods The parameters included size, distance to the central, distance between each pronucleus, the movement pathway of each pronucleus and the fading time point. Quartiles of the parameters were divided into 25th (Q1), 50th (Q2), 75th (Q3) and 100th (Q4). In each quartile, the median of this quartile was presented with IQR. The chi-square test was used for the distribution comparison of the blastocyst formation. The FDR method was used to adjust the multi-comparison P-value. Main results and the role of chance We defined the pronucleus that was close to cortical as pn2 while the other was pn1. For the distance from each pronucleus to the zygote's geometric centroids (pn1_dist and pn2_dist), though pn1_dist did not show any significance, the pn2_dist showed a higher blastulation rate in Q2 (Q2:29.10% vs 17.90%). For the distance between the pronucleus (pn1_pn2_dist), the closer of PN got less blastocyst formation outcomes (Q1: 21.39% vs 31.44%). The results in the size of pronuclei (pn1_sz and pn2_sz) both showed that there was less blastocyst formation in small PN size (pn1_sz: Q1:19.90% vs 34.06%; pn2_sz: Q1:20.15% vs 33.19%). The duration from fertilization to pronuclei fading also had different blastulation rates. In the longest duration, there were the least blastocyst formation outcomes (Q4: 18.66% vs 35.81%). During the pn2 movement to the pn1, it might have an appropriate distance for the PN fusion. The result of the pn1_pn2_dist also showed that if the male and female pronuclei were too close (less than 14.98-17.24 μm ), the proportion of blastocyst formation was lower. If the movement of pn2 showed a decrease to the centre and then an increasing distance during development, more blastocyst formation was expected. Limitations, reasons for caution This was a retrospective and descriptive study and the end point of embryo developmental potential was blastocyst formation. The further association of the pronuclei parameters needs further endpoints to dig out. The diagnostic value of the pronucleus parameters needs further study to validate. Wider implications of the findings The pronucleus parameters are associated with blastocyst formation, which could serve as a non-invasive morphological tool to assess embryo developmental potential. Our study showed the explainable application of AI for enhancing the morphokinetics study. We believe both the pronucleus parameters and the AI tool could shed the light on embryology. Trial registration number ChiCTR1900025776

Improved Segmentation of Pulmonary Nodules Using Soft Computing Techniques with SegNet and Adversarial Networks

Lung cancer is seen as one of the most common lung diseases. For the patients having symptoms, the presence of lung nodules is checked by using various imaging techniques. Pulmonary nodules are detected in most of the cases having symptoms. But identifying the type of the nodule and the categorization still remains as a challenge. After confirming the presence of a nodule (benign or malignant) it takes several other steps to identify its characteristics. Improved imaging methods produce results within a short span of time. Research works are being conducted to increase the overall efficiency of the system. The proposed system considers authentic data sources for the study. The benign and malignant samples are considered for the generation of realistic large image sets. The generation of a large data set with the help of a generative adversarial network (GAN) is the first part of the work. The generated images using GAN cannot be differentiated from the original images even by a trained radiologist. This proves the importance of images generated using GAN. A GAN is able to generate 1024 × 1024 resolutions for natural images. Real data images are used to finetune the SegNet output. Through transfer learning, these weights are transferred to the system for segmentation of the images. The training process use real and generated images, which improve theefficiency of the network. The original data from LUNA 16 was used to further generate benign and malignant samples using GAN. A total of 440 images and their augmented images were used for training the GAN, and it generated 1,001,000 images. Hence the overall efficiency of the system was improved. To verify the results, the same various combinations and methods were considered and tabulated with various parameters. Methods with SegNet, GAN, and other combinations were evaluated to verify the efficiency of the system. Receiver operating characteristics were also plotted and compared with the area under the curve for verification of the results.

Automatic three-dimensional nasal and pharyngeal airway subregions identification via Vision Transformer

ObjectivesUpper airway assessment requires a fully-automated segmentation system for complete or sub-regional identification. This study aimed to develop a novel Deep Learning (DL) model for accurate segmentation of the upper airway and achieve entire and subregional identification. MethodsFifty cone-beam computed tomography (CBCT) scans, including 24,502 slices, were labelled as the ground truth by one orthodontist and two otorhinolaryngologists. A novel model, a lightweight multitask network based on the Swin Transformer and U-Net, was built for automatic segmentation of the entire upper airway and subregions. Segmentation performance was evaluated using Precision, Recall, Dice similarity coefficient (DSC) and Intersection over union (IoU). The clinical implications of the precision errors were quantitatively analysed, and comparisons between the AI model and Dolphin software were conducted. ResultsOur model achieved good performance with a precision of 85.88–94.25%, recall of 93.74–98.44%, DSC of 90.95–96.29%, IoU of 83.68–92.85% in the overall and subregions of three-dimensional (3D) upper airway, and a precision of 91.22–97.51%, recall of 90.70–97.62%, DSC of 90.92–97.55%, and IoU of 83.41–95.29% in the subregions of two-dimensional (2D) crosssections. Discrepancies in volume and area caused by precision errors did not affect clinical outcomes. Both our AI model and the Dolphin software provided clinically acceptable consistency for pharyngeal airway assessments. ConclusionThe novel DL model not only achieved segmentation of the entire upper airway, including the nasal cavity and subregion identification, but also performed exceptionally well, making it well suited for 3D upper airway assessment from the nasal cavity to the hypopharynx, especially for intricate structures. Clinical significanceThis system provides insights into the aetiology, risk, severity, treatment effect, and prognosis of dentoskeletal deformities and obstructive sleep apnea. It achieves rapid assessment of the entire upper airway and its subregions, making airway management—an integral part of orthodontic treatment, orthognathic surgery, and ENT surgery—easier.

Skin lesion segmentation of dermoscopy images using U-Net

Skin cancer is one of the most threatening cancers as reported and has been on the increase over the past 10 years. The traditional methods of skin cancer segmentation are time-consuming and inefficient. U-Net is a powerful and accurate way of self-segmentation in the medical field. In order to solve this problem, this paper proposes a U-Net skin cancer segmentation system that can provide results and feedback quickly, accurately and intelligently. It is composed of two parts: Skin Image Analysis Module and Skin Image Segmentation Module. In the skin image analysis module, the system learns segmentation from the training set images, and verifies the correctness of learning from part of the images. In Skin Image Segmentation Module, the system segments all the images in the test set folder. Among several experiments, the system using GPU training and learning with 100 images of ISIC dataset after 10 epochs has the training accuracy of 0.9085, while the validation accuracy is 0.9536. The system allows users to upload their lesion images to a test folder to obtain reliable segmentation results in a timely manner, thereby improving the survival rate of potential patients.

NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training.

Excitatory and Inhibitory Descending Commissural Interneurons Differentially Integrate Supraspinal and Segmental Sensory Signals.

The limited information about how descending inputs from the brain and sensory inputs from the periphery use spinal cord interneurons (INs) is a major barrier to understanding how these inputs may contribute to motor functions under normal and pathologic conditions. Commissural interneurons (CINs) are a heterogeneous population of spinal INs that has been implicated in crossed motor responses and bilateral motor coordination (ability to use the right and left side of the body in a coordinated manner) and, therefore, are likely involved in many types of movement (e.g., dynamic posture stabilization, jumping, kicking, walking). In this study, we incorporate mouse genetics, anatomy, electrophysiology, and single-cell calcium imaging to investigate how a subset of CINs, those with descending axons called dCINs, are recruited by descending reticulospinal and segmental sensory signals independently and in combination. We focus on two groups of dCINs set apart by their principal neurotransmitter (glutamate and GABA) and identified as VGluT2+ dCINs and GAD2+ dCINs. We show that VGluT2+ and GAD2+ dCINs are both extensively recruited by reticulospinal and sensory input alone but that VGluT2+ and GAD2+ dCINs integrate these inputs differently. Critically, we find that when recruitment depends on the combined action of reticulospinal and sensory inputs (subthreshold inputs), VGluT2+ dCINs, but not GAD2+ dCINs, are recruited. This difference in the integrative capacity of VGluT2+ and GAD2+ dCINs represents a circuit mechanism that the reticulospinal and segmental sensory systems may avail themselves of to regulate motor behaviors both normally and after injury.SIGNIFICANCE STATEMENT The way supraspinal and peripheral sensory inputs use spinal cord interneurons is fundamental to defining how motor functions are supported both in health and disease. This study, which focuses on dCINs, a heterogeneous population of spinal interneurons critical for crossed motor responses and bilateral motor coordination, shows that both glutamatergic (excitatory) and GABAergic (inhibitory) dCINs can be recruited by supraspinal (reticulospinal) or peripheral sensory inputs. Additionally, the study demonstrates that in conditions where the recruitment of dCINs depends on the combined action of reticulospinal and sensory inputs, only excitatory dCINs are recruited. The study uncovers a circuit mechanism that the reticulospinal and segmental sensory systems may avail themselves of to regulate motor behaviors both normally and after injury.

Continuous enantiomeric separation using water-oil-water segmented flow system

The multiphase segmented flow, which consisted of the material, extraction, and recovery phases, was applied to the chiral separation. The use of simultaneous extraction and back-extraction with multiple phases aims to prevent the accumulation of target material in the extraction phase by efficiently removing it in the recovery phase, thereby enabling extraction performance beyond the equilibrium. This study demonstrated the chiral separation of an aqueous racemic amino acid derivative (3,5-dinitrobenzoyl-(R,S)-leucine) with a cinchona alkaloid chiral host. A droplet manipulation system comprising PFA manifolds and valves was utilized to alternately form an acidic material phase slug and a basic recovery phase slug in a 1-octanol oil phase containing the chiral host. Two aqueous phases were collected separately using valves controlled by an optical sensor signal. The manipulation system achieved stable operation across various conditions, including flow rate, residence time, and slug lengths. The enantiomeric excess, which represents the purity of the chirality, could be increased to 50 % through 200 s extraction in a one-step operation.

Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images

Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images

Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system

BackgroundThe goal of this study is to develop a fully automated segmentation and morphometric parameter estimation system for assessing abnormal corneal endothelial cells (CECs) from LASER in vivo confocal microscopy (IVCM) images.MethodsFirst, we developed a fully automated deep learning system for assessing abnormal CECs using a previous development set composed of normal images and a newly constructed development set composed of abnormal images. Second, two testing sets, one with 169 normal images and the other with 211 abnormal images, were used to evaluate the clinical validity and effectiveness of the proposed system on LASER IVCM images with different corneal endothelial conditions, particularly on abnormal images. Third, the automatically calculated endothelial cell density (ECD) and the manually calculated ECD were compared using both the previous and proposed systems.ResultsThe automated morphometric parameter estimations of the average number of cells, ECD, coefficient of variation in cell area and percentage of hexagonal cells were 257 cells, 2648 ± 511 cells/mm2, 32.18 ± 6.70% and 56.23 ± 8.69% for the normal CEC testing set and 83 cells, 1450 ± 656 cells/mm2, 34.87 ± 10.53% and 42.55 ± 20.64% for the abnormal CEC testing set. Furthermore, for the abnormal CEC testing set, Pearson’s correlation coefficient between the automatically and manually calculated ECDs was 0.9447; the 95% limits of agreement between the manually and automatically calculated ECDs were between 329.0 and − 579.5 (concordance correlation coefficient = 0.93).ConclusionsThis is the first report to count and analyze the morphology of abnormal CECs in LASER IVCM images using deep learning. Deep learning produces highly objective evaluation indicators for LASER IVCM corneal endothelium images and greatly expands the range of applications for LASER IVCM.

ANOPHONE: An Annotation Tool for Phonemes and L2 Annotation Systems for Czech

Abstract The goal of this text is the presentation of the ANOPHONE annotation system, which allows for the management and annotation of speech data to develop a tool for the automatic transcription of speech of non-native speakers of Czech. This system is currently designed for annotations on the segmental level of recordings of non-native speakers of Czech, with the aim to train automatic speech recognition (ASR) models used in this tool. After an introductory section that discusses the use of technology in pronunciation teaching and mentions some of the e-learning applications for teaching the pronunciation of second languages (L2), we address both general and more specific aspects of speech data annotation to train ASR models and mention attributive and synthetic segmental systems of speech data annotation for Czech as L2. We also briefly introduce the annotation system of non-native speakers of Czech called BV1, which is used for testing the ANOPHONE tool. The main part of this text focuses on presenting the annotation tool itself, while the conclusion describes the experience of testing the speech data annotation tool using BV1 annotation system for Czech as L2.

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.

The Edge Sensor of Segmented Mirror Based on Fringes of Equal Thickness

Co-phase and co-focus detection is one of the key technologies for large-aperture segmented mirror telescopes. In this paper, a new edge sensor based on fringes of equal thickness is developed, which can detect each segment’s relative piston, tilt, and tip errors from the interferograms. Based on the co-focus demand for many ground-based seeing limited segmented mirror telescopes, an edge sensor prototype based on such a principle is built and applied in the indoor segmented mirror experiment system in the lab. According to the co-focus requirement of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, many simulations and experiments are carried out for co-focus error detection of the segmented mirror system. Experiment results show that the co-focus accuracy is better than 0.″02 rms, which can meet the co-focus requirements of most large or extremely large segmented mirror astronomical telescopes.

Morphology of the heart during the period of progressive growth in conditions of prolonged exposure to low temperatures

Annotation. The aim of the research is to study the cardiac morphology during the period of growth under prolonged exposure to low temperatures. The main group consisted of outbred white rats (260 males and 190 females) undergoing active development, aged 4 weeks at the beginning of the experiment, and was kept in a room with an air temperature of +4±1 °C. The control group of animals was kept in a room with an air temperature of +20±10 °C. The experiment lasted for sixteen weeks. Research methods: macromorphometric study of the heart, histological study of paraffin sections, and ultrastructural study. Micromorphometric study was performed on semi-thin sections stained with 0.1% solution of toluidine blue at an ocular magnification of 10 and objective of 90, using a multi-purpose test system of short segments (Weibel grid) and an automatic analyzer of microscopic images “Integral-2MT”. PAS stain by A.L.Shabadash was used to detect glycogen, and the number of glycogen granules per unit area was counted on electronograms at a magnification of 20000 using a 1 cm2 area square. The stereological study of the electronograms was carried out at a magnification of 8000 using a Weibel grid. The statistical processing of the obtained results was carried out using the SPSS statistical program package, STATISTICA v. 10.0. It was found that the adaptation of the myocardium to the cold occurs according to the universal mechanism of adaptation to various extreme conditions associated with a deficit of energy metabolism. Therefore, the effect of cold can be regarded as non-specific. Based on the conducted research, the following stages of the adaptation process are defined: 1) short-term stress reaction (first 5 days); 2) myocardial hypertrophy (from 2nd to 6th weeks); 3) long-term, stable adaptation (more than 6 weeks). Morphological criteria for myocardial adaptation to long-term cold exposure have been identified: 1) physiological hypertrophy of the myocardium, mainly of the right ventricle; 2) the energy reserve of cardiomyocytes in the form of mitochondrial hyperplasia and glycogen accumulation. We plan to study the microcirculatory bed of the myocardium during the adaptation of the body to prolonged action of low temperature in the future.

Fighting against COVID‐19: Innovations and applications

Fighting against <scp>COVID</scp>‐19: Innovations and applications

An Onboard Point Cloud Semantic Segmentation System for Robotic Platforms

Point clouds represent an important way for robots to perceive their environments, and can be acquired by mobile robots with LiDAR sensors or underwater robots with sonar sensors. Hence, real-time semantic segmentation of point clouds with onboard edge devices is essential for robots to apprehend their surroundings. In this paper, we propose an onboard point cloud semantic segmentation system for robotic platforms to overcome the conflict between attaining high accuracy of segmentation results and the limited available computational resources of onboard devices. Our system takes raw a sequence of point clouds as inputs, and outputs semantic segmentation results for each frame as well as a reconstructed semantic map of the environment. At the core of our system is the transformer-based hierarchical feature extraction module and fusion module. The two modules are implemented with sparse tensor technologies to speed up inference. The predictions are accumulated according to Bayes rules to generate a global semantic map. Experimental results on the SemanticKITTI dataset show that our system achieves +2.2% mIoU and 18× speed improvements compared with SOTA methods. Our system is able to process 2.2 M points per second on Jetson AGX Xavier (NVIDIA, Santa Clara, USA), demonstrating its applicability to various robotic platforms.

Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network—based model on sparsely annotated MRI

This study aims to develop a fully automated imaging protocol independent system for pituitary adenoma segmentation from magnetic resonance imaging (MRI) scans that can work without user interaction and evaluate its accuracy and utility for clinical applications. We trained two independent artificial neural networks on MRI scans of 394 patients. The scans were acquired according to various imaging protocols over the course of 11 years on 1.5T and 3T MRI systems. The segmentation model assigned a class label to each input pixel (pituitary adenoma, internal carotid artery, normal pituitary gland, background). The slice segmentation model classified slices as clinically relevant (structures of interest in slice) or irrelevant (anterior or posterior to sella turcica). We used MRI data of another 99 patients to evaluate the performance of the model during training. We validated the model on a prospective cohort of 28 patients, Dice coefficients of 0.910, 0.719, and 0.240 for tumour, internal carotid artery, and normal gland labels, respectively, were achieved. The slice selection model achieved 82.5% accuracy, 88.7% sensitivity, 76.7% specificity, and an AUC of 0.904. A human expert rated 71.4% of the segmentation results as accurate, 21.4% as slightly inaccurate, and 7.1% as coarsely inaccurate. Our model achieved good results comparable with recent works of other authors on the largest dataset to date and generalized well for various imaging protocols. We discussed future clinical applications, and their considerations. Models and frameworks for clinical use have yet to be developed and evaluated.

Temporal clustering of fissural eruption across multiple segments within the Ethiopian Rift

Magmatic continental rifts show evidence that discrete rift segments experience episodic intrusive and eruptive events, more commonly termed rifting episodes. However, whether multiple rifting episodes across adjacent rift segments are clustered in time is not well understood. To address this issue, we conduct new radiocarbon dating that constrains the timing of the most recent rifting episode at the Boset magmatic segment of the northern Ethiopian rift, and combine this with historical dating of similar rifting events in the adjacent magmatic segments. New radiocarbon dates of multiple charcoal samples from the base of the most recent fissural lava at the Boset Volcanic Complex indicate that it likely occurred between 1812 and 1919 CE. These dates are similar to those from historical accounts of fissural eruption from the neighbouring Kone (∼1810 CE), and Fantale (∼1770 to 1808 CE) magmatic segments. We conduct new analysis of major and trace element compositions from these historical fissural lavas, as well as from a fresh-looking lava flow from Beru cone near to Kone volcano. The results of the geochemistry from these flows of all three magmatic segments show compositions that vary in the basalt and trachybasalt fields, with sufficient variation to rule out them having erupted from a single dike intrusion episode. This, combined with the scatter in dates from the radiocarbon analysis and historical accounts, along with the location of each eruption in a discrete and spatially offset magmatic segment, favours an interpretation of each magmatic segment experiencing separate rifting episodes but with these being clustered in time. Mechanisms to explain the clustering of rifting episodes are more speculative but could include stress transfer from dike intrusion and deep crustal hydraulic connection in the plumbing system of multiple segments.

Detection of Secondary Side Position for Segmented Dynamic Wireless Charging Systems Based on Primary Phase Angle Sensing

In dynamic wireless charging systems, the detection of secondary side positions has been attracting much attention in academic research. Due to the strong electromagnetic interference and the presence of foreign objects in the charging area, the use of conventional detection methods such as wireless communication and infrared techniques may be problematic; therefore, as an alternative to solve the above problem, a new detection method based on phase angle sensing is proposed in this paper. Through phase analysis of the primary input impedance and by reference to the relationship between the input port phase angle and the secondary side position, the proposed method is able to sense the secondary side position in real time. In addition, an analysis of the sensitivity of the proposed method to parameter variations is also carried out. In order to verify the effectiveness of the proposed position detection method, a dynamic wireless charging system with four segments is built for experimental verification. The experimental results show that when the phase angle threshold is set at 300°, the secondary side position can be accurately identified, and the proposed method is quite robust within a parameter deviation of up to 4%.