A priori knowledge-free fast positioning approach for BeiDou receivers

A Global Navigation Satellite System (GNSS) receiver usually needs a sufficient number of full pseudorange measurements to obtain a position solution. However, it is time-consuming to acquire full pseudorange information from only the satellite broadcast signals due to the navigation data features of GNSS. In order to realize fast positioning during a cold or warm start in a GNSS receiver, the existing approaches require an initial estimation of position and time or require a number of computational steps to recover the full pseudorange information from fractional pseudoranges and then compute the position solution. The BeiDou Navigation Satellite System (BDS) has a unique constellation distribution and a fast navigation data rate for geostationary earth orbit (GEO) satellites. Taking advantage of these features, we propose a fast positioning technique for BDS receivers. It simultaneously processes the full and fractional pseudorange measurements from the BDS GEOs and non-GEOs, respectively, which is faster than processing all full measurements. This method resolves the position solution and recovers the full pseudoranges for non-GEOs simultaneously within 1 s theoretically and does not need an estimate of the initial position. Simulation and real data experiments confirm that the proposed technique completes fast positioning without a priori position and time estimation, and the positioning accuracy is identical with the conventional single-point positioning approach using full pseudorange measurements from all available satellites

Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings

Dense prediction tasks are common for 3D point clouds, but the uncertainties inherent in massive points and their embeddings have long been ignored. In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds. Inspired by metric learning, the key idea of CUE is to explore cross-point embeddings upon a conventional 3D dense prediction pipeline. Specifically, CUE involves building a probabilistic embedding model and then enforcing metric alignments of massive points in the embedding space. We also propose CUE+, which enhances CUE by explicitly modeling cross-point dependencies in the covariance matrix. We demonstrate that both CUE and CUE+ are generic and effective for uncertainty estimation in 3D point clouds with two different tasks: (1) in 3D geometric feature learning we for the first time obtain well-calibrated uncertainty, and (2) in semantic segmentation we reduce uncertainty's Expected Calibration Error of the state-of-the-arts by 16.5%. All uncertainties are estimated without compromising predictive performance

STUN: Self-Teaching Uncertainty Estimation for Place Recognition

Place recognition is key to Simultaneous Localization and Mapping (SLAM) and spatial perception. However, a place recognition in the wild often suffers from erroneous predictions due to image variations, e.g., changing viewpoints and street appearance. Integrating uncertainty estimation into the life cycle of place recognition is a promising method to mitigate the impact of variations on place recognition performance. However, existing uncertainty estimation approaches in this vein are either computationally inefficient (e.g., Monte Carlo dropout) or at the cost of dropped accuracy. This paper proposes STUN, a self-teaching framework that learns to simultaneously predict the place and estimate the prediction uncertainty given an input image. To this end, we first train a teacher net using a standard metric learning pipeline to produce embedding priors. Then, supervised by the pretrained teacher net, a student net with an additional variance branch is trained to finetune the embedding priors and estimate the uncertainty sample by sample. During the online inference phase, we only use the student net to generate a place prediction in conjunction with the uncertainty. When compared with place recognition systems that are ignorant to the uncertainty, our framework features the uncertainty estimation for free without sacrificing any prediction accuracy. Our experimental results on the large-scale Pittsburgh30k dataset demonstrate that STUN outperforms the state-of-the-art methods in both recognition accuracy and the quality of uncertainty estimation.Comment: To appear at the 35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2022

Extrauterine adenomyoma of the liver with a focally cellular smooth muscle component occurring in a patient with a history of myomectomy: case report and review of the literature

VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1327125766102291. Since first reported in 1986, 14 cases of extrauterine adenomyoma have been reported in the English literature, most often occurring in the ovaries. In this report, we present the first case of extrauterine adenomyoma involving the liver in a 29-year-old woman who presented with a 2-year history of low back pain with recent worsening and a history of laparoscopic myomectomy 5 years previously. Gross inspection of the specimen revealed a subcapsular mass that had a well-circumscribed margin with the adjacent liver tissue. By histopathologic examination, the multilobular mass was composed of a smooth muscle component and benign endometrioid glands and stroma. The smooth muscle component was focally cellular, and the endometrioid glands had secretory features. Both the smooth muscle component and endometrioid tissue were positive for ER and PR. The smooth muscle component was also positive for desmin and SMA, while the endometrioid stroma was positive for CD10. Other extrauterine lesions composed of a mixture of smooth muscle tissue and heterotopic endometrioid tissue, including endometriosis with a smooth muscle component, leiomyomatosis/leiomyomas associated with endometriosis and uterus-like masses, should be included in differential diagnoses. The patient was free from recurrence 5 months after liver tumor resection

Current situation and research prospect of home monitoring technology for cardiovascular system

Cardiovascular disease has become the leading cause of death in China. Home medical monitoring of cardiovascular circulatory system (cardiovascular system for short) is of great significance for the early detection, diagnosis and treatment of cardiovascular diseases. Physiological parameters of the cardiovascular system mainly include blood pressure, cardiac output, blood glucose content, blood oxygen saturation, ECG, respiration, etc. These physiological parameters need long-term daily monitoring to find the abnormalities and changes of the system. Therefore, home medical monitoring is necessary. This paper reviews the current situation of home monitoring technology for the main parameters of cardiovascular system in recent years, and prospects its future research trend

Impact of Temperament Types and Anger Intensity on Drivers\u27 EEG Power Spectrum and Sample Entropy: An On-road Evaluation Toward Road Rage Warning

"Road rage", also called driving anger, is becoming an increasingly common phenomenon affecting road safety in auto era as most of previous driving anger detection approaches based on physiological indicators are often unreliable due to the less consideration of drivers\u27 individual differences. This study aims to explore the impact of temperament types and anger intensity on drivers\u27 EEG characteristics. Thirty-two drivers with valid license were enrolled to perform on-road experiments on a particularly busy route on which a variety of provoking events like cutting in line of surrounding vehicle, jaywalking, occupying road of non-motor vehicle and traffic congestion frequently happened. Then, muti-factor analysis of variance (ANOVA) and post hoc analysis were utilized to study the impact of temperament types and anger intensity on drivers\u27 power spectrum and sample entropy of θ and β waves extracted from EEG signals. The study results firstly indicated that right frontal region of the brain has close relationship with driving anger. Secondly, there existed significant main effects of temperament types on power spectrum and sample entropy of β wave while significant main effects of anger intensity on power spectrum and sample entropy of θ and β wave were all observed. Thirdly, significant interactions between temperament types and anger intensity for power spectrum and sample entropy of β wave were both noted. Fourthly, with the increase of anger intensity, the power spectrum and sample entropy both decreased sufficiently for θ wave while increased remarkably for β wave. The study results can provide a theoretical support for designing a personalized and hierarchical warning system for road rage