A Novel Crosstalk Elimination Method for Sonar Ranging System in Rescue Robot

AbstractUltrasonic crosstalk can cause false distance measurements and reduce the work efficiency of sonar ranging system. To enhance the performance of sonar ranging system in rescue robot, quadrature phase shift keying (QPSK) excitation sequences modulated using chaotic codes are proposed to fire sonar sensors. In order to obtain the best echo correlation characteristics, a genetic algorithm (GA) is used to optimize the initial values of the chaotic codes. Real experiments have been implemented using a sonar ranging system consisting of eight-channel SensComp 600 series electrostatic sensors excited with 2ms QPSK sequences. Experimental results show that the optimized QPSK excitation sequences can make eight channels sonar ranging system work together without crosstalk

Ovarian preservation and prognosis in adnexal torsion surgery — a retrospective analysis

Objectives: This study aims to analyze the conditions of ovarian preservation during adnexal torsion surgery, and safetyof ovarian preservation.Material and methods: A retrospective analysis of 130 patients, who underwent surgery for ovarian benign tumor pedicletorsion in Fujian Provincial Maternal and Child Health Hospital from June 2013 to June 2018, was conducted. This studyanalyses the possible risk factors affecting the operation method using multiple logistic regression and analyses the complicationsand the recovery of ovarian function after the treatment of the ovarian preservation.Results: Among these patients, 58 received ovarian cystectomy, while 72 received ovariectomy. There was no significantdifference in terms of age, preoperative blood, operation time and surgical bleeding volume between the two groups(p > 0.05). However, there was a significant difference in preoperative adnexal blood flow, abdominal pain to the surgicalinterval, and a collection of torsion cycles (p < 0.05). There was an increased risk of ovarian resection in patients whose bloodflow of the annex disappeared, whose time of abdominal pain was long, and whose number of twists were significant. Forthe preservation group, there were no increases in postoperative complications.Conclusions: According to clinical indicators, such as preoperative adnexal blood flow, abdominal pain to the interval ofsurgery and the number of torsion cycles, it was determined whether it was feasible to keep the ovary. Retaining the ovaryis safe, effective and feasible in adnexal torsion

Deep Learning for Person Reidentification Using Support Vector Machines

© 2017 Mengyu Xu et al. Due to the variations of viewpoint, pose, and illumination, a given individual may appear considerably different across different camera views. Tracking individuals across camera networks with no overlapping fields is still a challenging problem. Previous works mainly focus on feature representation and metric learning individually which tend to have a suboptimal solution. To address this issue, in this work, we propose a novel framework to do the feature representation learning and metric learning jointly. Different from previous works, we represent the pairs of pedestrian images as new resized input and use linear Support Vector Machine to replace softmax activation function for similarity learning. Particularly, dropout and data augmentation techniques are also employed in this model to prevent the network from overfitting. Extensive experiments on two publically available datasets VIPeR and CUHK01 demonstrate the effectiveness of our proposed approach

Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays

The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generation methods. However, the direct generation of radiology graphs from chest X-ray (CXR) images has not been attempted. To address this gap, we propose a novel approach called Prior-RadGraphFormer that utilizes a transformer model with prior knowledge in the form of a probabilistic knowledge graph (PKG) to generate radiology graphs directly from CXR images. The PKG models the statistical relationship between radiology entities, including anatomical structures and medical observations. This additional contextual information enhances the accuracy of entity and relation extraction. The generated radiology graphs can be applied to various downstream tasks, such as free-text or structured reports generation and multi-label classification of pathologies. Our approach represents a promising method for generating radiology graphs directly from CXR images, and has significant potential for improving medical image analysis and clinical decision-making.Comment: In GRAIL @ MICCAI 202

Incremental capacity curve health-indicator extraction based on gaussian filter and improved relevance vector machine for lithium–ion battery remaining useful life estimation.

Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve high–precision RUL estimation of lithium–ion batteries, a novel RUL prediction model is proposed by combining the extraction of health indicators based on incremental capacity curve (IC) and the method of improved adaptive relevance vector machine (RVM). First, the IC curve is extracted based on the charging current and voltage data. To attenuate the noise effects on the IC curve, Gaussian filtering is used and the optimal filtering window is determined to remove the noise interference. Based on this, the peak characteristics of the IC curve are analyzed and four groups of health indicators are extracted, and the strong correlation between health indicators and capacity degradation is determined using Pearson correlation analysis. Then, to optimize the traditional fixed kernel parameter RVM model, an RVM regression model whose kernel parameters are optimized by the Bayesian algorithm is established. Finally, four sets of datasets under CS2 battery in the public dataset of the University of Maryland are carried out for experimental validation. The validation results show that the improved RVM model has better short–term prediction performance and long–term prediction stability, the RUL prediction error is less than 20 cycles, and the mean absolute error is less than 0.02. The performance of the improved RVM model is better than that of the traditional RVM model