Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1

Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential Delay-Doppler Maps (PN-D), to distinguish between sea ice and sea water using differential Delay-Doppler Maps (dDDMs). PS-D and PN-D make use of power-summation and pixel-number of dDDMs, respectively, to measure the degree of difference between two DDMs so as to determine the transition state (water-water, water-ice, ice-ice and ice-water) and hence ice and water are detected. Moreover, an adaptive incoherent averaging of DDMs is employed to improve the computational efficiency. A large number of DDMs recorded by UK TechDemoSat-1 (TDS-1) over the Arctic region are used to test the proposed sea ice detection methods. Through evaluating against ground-truth measurements from the Ocean Sea Ice SAF, the proposed PS-D and PN-D methods achieve a probability of detection of 99.72% and 99.69% respectively, while the probability of false detection is 0.28% and 0.31% respectively

A centering correction method for GNSS antenna diversity theory and implementation using a software receiver

GPS is performing well in open sky situation. However, severe attenuation or blockage of signals by high buildings may leads to an insufficient number of received satellites. Antenna diversity scheme is viewed as a method to alleviate signal attenuation and enhance the performance of GNSS positioning in the harsh environments. This paper introduces an antenna diversity system, composed of two spatially separated antennas. If relative geometry of two antennas is known, the carrier phase measurement outputs from these two antennas can be combined with Centering Correction Method (CCM). Even each antenna may not able to acquire more than four satellites this antenna diversity system can still precisely estimate each antenna’s location with centimeter-level accuracy, as long as the sum of the captured satellites by two separate antennas is no less than four

HPV prevalence and genotype distribution in 2,306 patients with cervical squamous cell carcinoma in central and eastern China

BackgroundTo explore the positivity rate and genotype distribution of human papillomavirus (HPV) in cervical squamous cell carcinoma (CSCC) tissues in central and eastern China and to provide theoretical basis for cervical cancer screening and prophylactic HPV vaccine development in China.MethodsDNA was extracted from paraffin-embedded tissues of CSCC samples and exfoliated cervical cells of cervical cancer screening populations. 23 HPV genotypes were detected by combining polymerase chain reaction (PCR) and reverse dot hybridized gene chip detection technology in 2,306 CSCC tissues and 10,245 cervical cancer screening populations. The genotype distribution of HPV infection was analyzed.ResultsThe overall infection rate of HPVs in 2,306 CSCC patients was 92.71%. The frequency of single-type HPV infection and multiple-type HPV infection were 86.48% and 13.51%, respectively. The most common HPV genotypes detected in Chinese CSCC tissues were HPV-16, HPV-18, HPV-31, HPV-33, HPV-45, HPV-52, HPV-58, and HPV-59. The overall positivity rate of these eight high-risk HPV (HR-HPV) genotypes in HPV-positive CSCC was as high as 96.91%. Of which the positivity rate of seven HR-HPV genotypes related to nine-valent HPV vaccines in HPV-positive CSCC was 95.09%. Meanwhile, the overall infection rates of HR-HPV and low-risk HPV (LR-HPV) in female aged 35–64 years who underwent cervical cancer screening were 13.16% and 1.32%, respectively. The high-frequency HR-HPV genotypes in cervical cancer screening women were HPV-52, HPV-58, HPV-16, HPV-53, HPV-68, HPV-39, HPV-51, and HPV-56, with positivity rates of 2.25%, 1.60%, 1.31%, 1.22%, 0.93%, 0.92%, 0.78%, and 0.74%, respectively.ConclusionAmong women screened for cervical cancer in China, detecting the 8 high-frequency HR-HPV genotypes can reduce technical difficulty and reagent costs, while also improving the efficiency and effectiveness of cervical cancer screening. HPV genotyping assists gynecologists in assessing the risk of HR-HPV-positive cervical intraepithelial neoplasia and guiding them in implementing appropriate interventions. Furthermore, HPV genotyping is helpful for doctors to follow up HR-HPV-positive women and to evaluate the protective effect of HPV vaccine

IAGC: Interactive Attention Graph Convolution Network for Semantic Segmentation of Point Clouds in Building Indoor Environment

Point-based networks have been widely used in the semantic segmentation of point clouds owing to the powerful 3D convolution neural network (CNN) baseline. Most of the current methods resort to intermediate regular representations for reorganizing the structure of point clouds for 3D CNN networks, but they may neglect the inherent contextual information. In our work, we focus on capturing discriminative features with the interactive attention mechanism and propose a novel method consisting of the regional simplified dual attention network and global graph convolution network. Firstly, we cluster homogeneous points into superpoints and construct a superpoint graph to effectively reduce the computation complexity and greatly maintain spatial topological relations among superpoints. Secondly, we integrate cross-position attention and cross-channel attention into a single head attention module and design a novel interactive attention gating (IAG)-based multilayer perceptron (MLP) network (IAG–MLP), which is utilized for the expansion of the receptive field and augmentation of discriminative features in local embeddings. Afterwards, the combination of stacked IAG–MLP blocks and the global graph convolution network, called IAGC, is proposed to learn high-dimensional local features in superpoints and progressively update these local embeddings with the recurrent neural network (RNN) network. Our proposed framework is evaluated on three indoor open benchmarks, and the 6-fold cross-validation results of the S3DIS dataset show that the local IAG–MLP network brings about 1% and 6.1% improvement in overall accuracy (OA) and mean class intersection-over-union (mIoU), respectively, compared with the PointNet local network. Furthermore, our IAGC network outperforms other CNN-based approaches in the ScanNet V2 dataset by at least 7.9% in mIoU. The experimental results indicate that the proposed method can better capture contextual information and achieve competitive overall performance in the semantic segmentation task

Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1

Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential Delay-Doppler Maps (PN-D), to distinguish between sea ice and sea water using differential Delay-Doppler Maps (dDDMs). PS-D and PN-D make use of power-summation and pixel-number of dDDMs, respectively, to measure the degree of difference between two DDMs so as to determine the transition state (water-water, water-ice, ice-ice and ice-water) and hence ice and water are detected. Moreover, an adaptive incoherent averaging of DDMs is employed to improve the computational efficiency. A large number of DDMs recorded by UK TechDemoSat-1 (TDS-1) over the Arctic region are used to test the proposed sea ice detection methods. Through evaluating against ground-truth measurements from the Ocean Sea Ice SAF, the proposed PS-D and PN-D methods achieve a probability of detection of 99.72% and 99.69% respectively, while the probability of false detection is 0.28% and 0.31% respectively

InSAR Displacement with High-Resolution Optical Remote Sensing for the Early Detection and Deformation Analysis of Active Landslides in the Upper Yellow River

Frequent landslides and other geological disasters pose a serious threat to human life and infrastructure in the Upper Yellow River. Detecting active landslides and ascertaining their impact necessitate the determination of deformation characteristics. In this study, we developed an integrated method combining interferometric synthetic aperture radar and high-resolution optical satellite remote sensing to detect active landslides in the Upper Yellow River region from Longyang Gorge to Lijia Gorge. Sentinel-1 satellite data from January 2019 to April 2021 with ascending and descending orbits were adopted to obtain deformation using the STACKING and interferometric point target analysis techniques. A 97.08% overlap rate in the detected results from the two InSAR technologies confirmed the suitability of both approaches. The missing detection rates (6.79% & 8.73%) from single line-of-sight (LOS) InSAR results indicate the necessity of different orbit direction data. Slight deformation rate changes (<4 mm/month) before and after rainy seasons of the Lijia Gorge landslide group indicate that precipitation exerted little impact on slope activity. This study supports the feasibility of integrated methods for the detection and analysis of active landslides in the Upper Yellow River and other regions

A Novel Ultra-Wideband Double Difference Indoor Positioning Method with Additional Baseline Constraint

Ultra-wideband (UWB) technology is suitable for indoor positioning owing to its high resolution and penetration. However, the current UWB positioning methods not only fail to fully analyze errors, but do not have the ability to eliminate gross and large random errors. In this article, the errors of UWB indoor positioning are analyzed comprehensively, and the basic function model is given. An indoor positioning method based on a double difference UWB with ranging observations is proposed and realized. In the proposed method, two UWB rover stations and a common base station are introduced, and the known baseline length between two rovers is used as the constraint condition for quality control. The observations and coordinate estimations are constrained by the prior and posteriori, respectively, and the weight of ranging observations with large residuals is reduced. Two groups of static experiments are designed. After adopting the proposed method, the plane error of one rover is 3.4 cm and 2.1 cm, and plane error of another rover is 3.3 cm and 2.0 cm, respectively. The positioning precision is improved by more than 80% compared with the traditional method. In the dynamic experiment, the coordinates of the starting and ending point obtained by the proposed method are basically consistent with the truth value, and the positioning results are close to the reference trajectory. The experimental results show that the proposed method can eliminate systematic and large random errors and improve the positioning precision effectively

Dam deformation analysis based on BPNN merging models

Hydropower has made a significant contribution to the economic development of Vietnam, thus it is important to monitor the safety of hydropower dams for the good of the country and the people. In this paper, dam horizontal displacement is analyzed and then forecasted using three methods: the multi-regression model, the seasonal integrated auto-regressive moving average (SARIMA) model and the back-propagation neural network (BPNN) merging models. The monitoring data of the Hoa Binh Dam in Vietnam, including horizontal displacement, time, reservoir water level, and air temperature, are used for the experiments. The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies. Hence, their short-term forecasts can provide valuable references for the dam safety

An Improved Semisoft Threshold Algorithm and Its Evaluation for Denoising Random Walk in GNSS Time Series

The differences in the satellite orbit and signal quality of global navigation satellite positioning system, resulting in the complexity of random walk noise in GNSS time series, has become a bottleneck problem in applying GNSS technology to the high precision deformation monitoring industry. For the complex characteristics of random walk noise, small magnitude, low frequency and low sensitivity, an improved semisoft threshold algorithm is presented. Then it forms a unified system of semisoft threshold function, so as to improve the adaptability of conventional semisoft threshold for random walk noise. In order to verify and evaluate the effect of improved semisoft threshold algorithm, MATLAB platform is used to generate a linear trend, periodic and random walk noise of the GNSS time series, a total of 1700 epochs. The results show that the improved semisoft threshold method is better than the classical method, and has better performance in the SNR and root mean square error. The evaluation results show that the morphological character has been performanced high consistency between the noise reduced by improved method with random walk noise. Further from the view of quantitative point, the evaluation results of spectral index analysis verify the applicability of the improved method for random walk noise

An Optimization Method of Ambiguity Function Based on Multi-Antenna Constrained and Application in Vehicle Attitude Determination

The global navigation satellite system (GNSS)-based multi-antenna attitude determination method has the advantages of a simple algorithm and no error accumulation with time in long endurance operation. However, it is sometimes difficult to simultaneous obtain the fixed solutions of all antennas in vehicle attitude determination. If float or incorrect fixed solutions are used, precision and reliability of attitude cannot be guaranteed. Given this fact, a baseline-constrained ambiguity function method (BCAFM) based on a self-built four GNSS antennas hardware platform is proposed. The coordinates obtained by BCAFM can replace the unreliable real-time kinematic (RTK) float or incorrect fixed solutions, so as to assist the direct method for attitude determination. In the proposed BCAFM, the baseline constraint is applied to improve search efficiency (searching time), and the ambiguity function value (AFV) formula is optimized to enhance the discrimination of true peak. The correctness of the proposed method is verified by vehicle attitude determination results and baseline length difference. Experimental results demonstrate that the function values of error peaks are reduced, and the only true peak can be identified accurately. The valid epoch proportion increases by 14.95% after true peak coordinates are used to replace the GNSS-RTK float or incorrect fixed solutions. The precision of the three attitude angles is 0.54°, 1.46°, and 1.15°, respectively. Meanwhile, the RMS of baseline length difference is 3.8 mm